Publications
Search
Liu, Lixing; Ustun, Volkan; Kumar, Rajay
Leveraging Organizational Hierarchy to Simplify Reward Design in Cooperative Multi-agent Reinforcement Learning Journal Article
In: FLAIRS, vol. 37, 2024, ISSN: 2334-0762.
@article{liu_leveraging_2024,
title = {Leveraging Organizational Hierarchy to Simplify Reward Design in Cooperative Multi-agent Reinforcement Learning},
author = {Lixing Liu and Volkan Ustun and Rajay Kumar},
url = {https://journals.flvc.org/FLAIRS/article/view/135588},
doi = {10.32473/flairs.37.1.135588},
issn = {2334-0762},
year = {2024},
date = {2024-05-01},
urldate = {2024-08-13},
journal = {FLAIRS},
volume = {37},
abstract = {The effectiveness of multi-agent reinforcement learning (MARL) hinges largely on the meticulous arrangement of objectives. Yet, conventional MARL methods might not completely harness the inherent structures present in environmental states and agent relationships for goal organization. This study is conducted within the domain of military training simulations, which are typically characterized by complexity, heterogeneity, non-stationary and doctrine-driven environments with a clear organizational hierarchy and a top-down chain of command. This research investigates the approximation and integration of the organizational hierarchy into MARL for cooperative training scenarios, with the goal of streamlining the processes of reward engineering and enhancing team coordination. In the preliminary experiments, we employed two-tiered commander-subordinate feudal hierarchical (CSFH) networks to separate the prioritized team goal and individual goals. The empirical results demonstrate that the proposed framework enhances learning efficiency. It guarantees the learning of a prioritized policy for the commander agent and encourages subordinate agents to explore areas of interest more frequently, guided by appropriate soft constraints imposed by the commander.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Aris, Timothy; Ustun, Volkan; Kumar, Rajay
Training Reinforcement Learning Agents to React to an Ambush for Military Simulations Journal Article
In: FLAIRS, vol. 37, 2024, ISSN: 2334-0762.
@article{aris_training_2024,
title = {Training Reinforcement Learning Agents to React to an Ambush for Military Simulations},
author = {Timothy Aris and Volkan Ustun and Rajay Kumar},
url = {https://journals.flvc.org/FLAIRS/article/view/135578},
doi = {10.32473/flairs.37.1.135578},
issn = {2334-0762},
year = {2024},
date = {2024-05-01},
urldate = {2024-08-13},
journal = {FLAIRS},
volume = {37},
abstract = {There is a need for realistic Opposing Forces (OPFOR)behavior in military training simulations. Current trainingsimulations generally only have simple, non-adaptivebehaviors, requiring human instructors to play the role ofOPFOR in any complicated scenario. This poster addressesthis need by focusing on a specific scenario: trainingreinforcement learning agents to react to an ambush. Itproposes a novel way to check for occlusion algorithmically.It shows vector fields showing the agent’s actions throughthe course of a training run. It shows that a single agentswitching between multiple goals is possible, at least in asimplified environment. Such an approach could reduce theneed to develop different agents for different scenarios.Finally, it shows a competent agent trained on a simplifiedReact to Ambush scenario, demonstrating the plausibility ofa scaled-up version.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Koresh, Caleb; Ustun, Volkan; Kumar, Rajay; Aris, Tim
Improving Reinforcement Learning Experiments in Unity through Waypoint Utilization Journal Article
In: FLAIRS, vol. 37, 2024, ISSN: 2334-0762.
@article{koresh_improving_2024,
title = {Improving Reinforcement Learning Experiments in Unity through Waypoint Utilization},
author = {Caleb Koresh and Volkan Ustun and Rajay Kumar and Tim Aris},
url = {https://journals.flvc.org/FLAIRS/article/view/135571},
doi = {10.32473/flairs.37.1.135571},
issn = {2334-0762},
year = {2024},
date = {2024-05-01},
urldate = {2024-08-13},
journal = {FLAIRS},
volume = {37},
abstract = {Multi-agent Reinforcement Learning (MARL) models teams of agents that learn by dynamically interacting with an environment and each other, presenting opportunities to train adaptive models for team-based scenarios. However, MARL algorithms pose substantial challenges due to their immense computational requirements. This paper introduces an automatically generated waypoint-based movement system to abstract and simplify complex environments in Unity while allowing agents to learn strategic cooperation. To demonstrate the effectiveness of our approach, we utilized a simple scenario with heterogeneous roles in each team. We trained this scenario on variations of realistic terrains and compared learning between fine-grained (almost) continuous and waypoint-based movement systems. Our results indicate efficiency in learning and improved performance with waypoint-based navigation. Furthermore, our results show that waypoint-based movement systems can effectively learn differentiated behavior policies for heterogeneous roles in these experiments. These early exploratory results point out the potential of waypoint-based navigation for reducing the computational costs of developing and training MARL models in complex environments. The complete project with all scenarios and results is available on GitHub: https://github.com/HATS-ICT/ml-agents-dodgeball-env-ICT.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Ustun, Volkan; Jorvekar, Ronit; Gurney, Nikolos; Pynadath, David; Wang, Yunzhe
Assessing Routing Decisions of Search and Rescue Teams in Service of an Artificial Social Intelligence Agent: Proceedings Article
In: Proceedings of the 16th International Conference on Agents and Artificial Intelligence, pp. 313–320, SCITEPRESS - Science and Technology Publications, Rome, Italy, 2024, ISBN: 978-989-758-680-4.
@inproceedings{ustun_assessing_2024,
title = {Assessing Routing Decisions of Search and Rescue Teams in Service of an Artificial Social Intelligence Agent:},
author = {Volkan Ustun and Ronit Jorvekar and Nikolos Gurney and David Pynadath and Yunzhe Wang},
url = {https://www.scitepress.org/DigitalLibrary/Link.aspx?doi=10.5220/0012388100003636},
doi = {10.5220/0012388100003636},
isbn = {978-989-758-680-4},
year = {2024},
date = {2024-02-01},
urldate = {2024-03-19},
booktitle = {Proceedings of the 16th International Conference on Agents and Artificial Intelligence},
pages = {313–320},
publisher = {SCITEPRESS - Science and Technology Publications},
address = {Rome, Italy},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Gurney, Nikolos; Pynadath, David V.; Ustun, Volkan
Spontaneous Theory of Mind for Artificial Intelligence Journal Article
In: 2024, (Publisher: [object Object] Version Number: 1).
@article{gurney_spontaneous_2024,
title = {Spontaneous Theory of Mind for Artificial Intelligence},
author = {Nikolos Gurney and David V. Pynadath and Volkan Ustun},
url = {https://arxiv.org/abs/2402.13272},
doi = {10.48550/ARXIV.2402.13272},
year = {2024},
date = {2024-02-01},
urldate = {2024-03-14},
abstract = {Existing approaches to Theory of Mind (ToM) in Artificial Intelligence (AI) overemphasize prompted, or cue-based, ToM, which may limit our collective ability to develop Artificial Social Intelligence (ASI). Drawing from research in computer science, cognitive science, and related disciplines, we contrast prompted ToM with what we call spontaneous ToM – reasoning about others' mental states that is grounded in unintentional, possibly uncontrollable cognitive functions. We argue for a principled approach to studying and developing AI ToM and suggest that a robust, or general, ASI will respond to prompts textbackslashtextitand spontaneously engage in social reasoning.},
note = {Publisher: [object Object]
Version Number: 1},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Joshi, Himanshu; Ustun, Volkan
Augmenting Cognitive Architectures with Large Language Models Journal Article
In: AAAI-SS, vol. 2, no. 1, pp. 281–285, 2024, ISSN: 2994-4317.
@article{joshi_augmenting_2024,
title = {Augmenting Cognitive Architectures with Large Language Models},
author = {Himanshu Joshi and Volkan Ustun},
url = {https://ojs.aaai.org/index.php/AAAI-SS/article/view/27689},
doi = {10.1609/aaaiss.v2i1.27689},
issn = {2994-4317},
year = {2024},
date = {2024-01-01},
urldate = {2024-04-16},
journal = {AAAI-SS},
volume = {2},
number = {1},
pages = {281–285},
abstract = {A particular fusion of generative models and cognitive architectures is discussed with the help of the Soar and Sigma cognitive architectures. After a brief introduction to cognitive architecture concepts and Large Language Models as exemplar generative AI models, one approach towards their fusion is discussed. This is then analyzed with a summary of potential benefits and extensions needed to existing cognitive architecture that is closest to the proposal.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Aris, Timothy; Ustun, Volkan; Kumar, Rajay
Learning to Take Cover with Navigation-Based Waypoints via Reinforcement Learning Journal Article
In: FLAIRS, vol. 36, 2023, ISSN: 2334-0762.
@article{aris_learning_2023,
title = {Learning to Take Cover with Navigation-Based Waypoints via Reinforcement Learning},
author = {Timothy Aris and Volkan Ustun and Rajay Kumar},
url = {https://journals.flvc.org/FLAIRS/article/view/133348},
doi = {10.32473/flairs.36.133348},
issn = {2334-0762},
year = {2023},
date = {2023-05-01},
urldate = {2023-08-04},
journal = {FLAIRS},
volume = {36},
abstract = {This paper presents a reinforcement learning model designed to learn how to take cover on geo-specific terrains, an essential behavior component for military training simulations. Training of the models is performed on the Rapid Integration and Development Environment (RIDE) leveraging the Unity ML-Agents framework. This work expands on previous work on raycast-based agents by increasing the number of enemies from one to three. We demonstrate an automated way of generating training and testing data within geo-specific terrains. We show that replacing the action space with a more abstracted, navmesh-based waypoint movement system can increase the generality and success rate of the models while providing similar results to our previous paper's results regarding retraining across terrains. We also comprehensively evaluate the differences between these and the previous models. Finally, we show that incorporating pixels into the model's input can increase performance at the cost of longer training times.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Pynadath, David V; Gurney, Nikolos; Kenny, Sarah; Kumar, Rajay; Marsella, Stacy C.; Matuszak, Haley; Mostafa, Hala; Ustun, Volkan; Wu, Peggy; Sequeira, Pedro
Effectiveness of Teamwork-Level Interventions through Decision-Theoretic Reasoning in a Minecraft Search-and-Rescue Task Proceedings Article
In: AAMAS '23: Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems, pp. Pages 2334–2336, 2023.
@inproceedings{pynadath_effectiveness_2023,
title = {Effectiveness of Teamwork-Level Interventions through Decision-Theoretic Reasoning in a Minecraft Search-and-Rescue Task},
author = {David V Pynadath and Nikolos Gurney and Sarah Kenny and Rajay Kumar and Stacy C. Marsella and Haley Matuszak and Hala Mostafa and Volkan Ustun and Peggy Wu and Pedro Sequeira},
url = {https://dl.acm.org/doi/10.5555/3545946.3598925},
year = {2023},
date = {2023-05-01},
booktitle = {AAMAS '23: Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems},
pages = {Pages 2334–2336},
abstract = {Autonomous agents offer the promise of improved human teamwork through automated assessment and assistance during task performance [15, 16, 18]. Studies of human teamwork have identified various processes that underlie joint task performance, while abstracting away the specifics of the task [7, 11, 13, 17].We present here an agent that focuses exclusively on teamwork-level variables in deciding what interventions to use in assisting a human team. Our agent does not directly observe or model the environment or the people in it, but instead relies on input from analytic components (ACs) (developed by other research teams) that process environmental information and output only teamwork-relevant measures. Our agent models these teamwork variables and updates its beliefs over them using a Bayesian Theory of Mind [1], applying Partially Observable Markov Decision Processes (POMDPs) [9] in a recursive manner to assess the state of the team it is currently observing and to choose interventions to best assist them.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Chadalapaka, Viswanath; Ustun, Volkan; Liu, Lixing
Leveraging Graph Networks to Model Environments in Reinforcement Learning Journal Article
In: FLAIRS, vol. 36, 2023, ISSN: 2334-0762.
@article{chadalapaka_leveraging_2023,
title = {Leveraging Graph Networks to Model Environments in Reinforcement Learning},
author = {Viswanath Chadalapaka and Volkan Ustun and Lixing Liu},
url = {https://journals.flvc.org/FLAIRS/article/view/133118},
doi = {10.32473/flairs.36.133118},
issn = {2334-0762},
year = {2023},
date = {2023-05-01},
urldate = {2023-08-04},
journal = {FLAIRS},
volume = {36},
abstract = {This paper proposes leveraging graph neural networks (GNNs) to model an agent’s environment to construct superior policy networks in reinforcement learning (RL). To this end, we explore the effects of different combinations of GNNs and graph network pooling functions on policy performance. We also run experiments at different levels of problem complexity, which affect how easily we expect an agent to learn an optimal policy and therefore show whether or not graph networks are effective at various problem complexity levels. The efficacy of our approach is shown via experimentation in a partially-observable, non-stationary environment that parallels the highly-practical scenario of a military training exercise with human trainees, where the learning goal is to become the best sparring partner possible for human trainees. Our results present that our models can generate better-performing sparring partners by employing GNNs, as demonstrated by these experiments in the proof-of-concept environment. We also explore our model’s applicability in Multi-Agent RL scenarios. Our code is available online at https://github.com/Derposoft/GNNsAsEnvs.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Aris, Timothy; Ustun, Volkan; Kumar, Rajay
Learning to Take Cover on Geo-Specific Terrains via Reinforcement Learning Journal Article
In: FLAIRS, vol. 35, 2022, ISSN: 2334-0762.
@article{aris_learning_2022,
title = {Learning to Take Cover on Geo-Specific Terrains via Reinforcement Learning},
author = {Timothy Aris and Volkan Ustun and Rajay Kumar},
url = {https://journals.flvc.org/FLAIRS/article/view/130871},
doi = {10.32473/flairs.v35i.130871},
issn = {2334-0762},
year = {2022},
date = {2022-05-01},
urldate = {2022-09-15},
journal = {FLAIRS},
volume = {35},
abstract = {This paper presents a reinforcement learning model designed to learn how to take cover on geo-specific terrains, an essential behavior component for military training simulations. Training of the models is performed on the Rapid Integration and Development Environment (RIDE) leveraging the Unity ML-Agents framework. We show that increasing the number of novel situations the agent is exposed to increases the performance on the test set. In addition, the trained models possess some ability to generalize across terrains, and it can also take less time to retrain an agent to a new terrain, if that terrain has a level of complexity less than or equal to the terrain it was previously trained on.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Hartholt, Arno; Fast, Ed; Leeds, Andrew; Kim, Kevin; Gordon, Andrew; McCullough, Kyle; Ustun, Volkan; Mozgai, Sharon
Demonstrating the Rapid Integration & Development Environment (RIDE): Embodied Conversational Agent (ECA) and Multiagent Capabilities Proceedings Article
In: Proceedings of the 21st International Conference on Autonomous Agents and Multiagent Systems, pp. 1902–1904, International Foundation for Autonomous Agents and Multiagent Systems, Richland, SC, 2022, ISBN: 978-1-4503-9213-6.
@inproceedings{hartholt_demonstrating_2022,
title = {Demonstrating the Rapid Integration & Development Environment (RIDE): Embodied Conversational Agent (ECA) and Multiagent Capabilities},
author = {Arno Hartholt and Ed Fast and Andrew Leeds and Kevin Kim and Andrew Gordon and Kyle McCullough and Volkan Ustun and Sharon Mozgai},
isbn = {978-1-4503-9213-6},
year = {2022},
date = {2022-05-01},
urldate = {2022-09-20},
booktitle = {Proceedings of the 21st International Conference on Autonomous Agents and Multiagent Systems},
pages = {1902–1904},
publisher = {International Foundation for Autonomous Agents and Multiagent Systems},
address = {Richland, SC},
series = {AAMAS '22},
abstract = {We demonstrate the Rapid Integration & Development Environment (RIDE), a research and development platform that enables rapid prototyping in support of multiagents and embodied conversational agents. RIDE is based on commodity game engines and includes a flexible architecture, system interoperability, and native support for artificial intelligence and machine learning frameworks.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Zhou, Jincheng; Ustun, Volkan
PySigma: Towards Enhanced Grand Unification for the Sigma Cognitive Architecture Book Section
In: Goertzel, Ben; Iklé, Matthew; Potapov, Alexey (Ed.): Artificial General Intelligence, vol. 13154, pp. 355–366, Springer International Publishing, Cham, 2022, ISBN: 978-3-030-93757-7 978-3-030-93758-4.
@incollection{zhou_pysigma_2022,
title = {PySigma: Towards Enhanced Grand Unification for the Sigma Cognitive Architecture},
author = {Jincheng Zhou and Volkan Ustun},
editor = {Ben Goertzel and Matthew Iklé and Alexey Potapov},
url = {https://link.springer.com/10.1007/978-3-030-93758-4_36},
doi = {10.1007/978-3-030-93758-4_36},
isbn = {978-3-030-93757-7 978-3-030-93758-4},
year = {2022},
date = {2022-01-01},
urldate = {2022-09-21},
booktitle = {Artificial General Intelligence},
volume = {13154},
pages = {355–366},
publisher = {Springer International Publishing},
address = {Cham},
keywords = {},
pubstate = {published},
tppubtype = {incollection}
}
Wang, Yunzhe; Gurney, Nikolos; Zhou, Jincheng; Pynadath, David V.; Ustun, Volkan
Route Optimization in Service of a Search and Rescue Artificial Social Intelligence Agent Book Section
In: Gurney, Nikolos; Sukthankar, Gita (Ed.): Computational Theory of Mind for Human-Machine Teams, vol. 13775, pp. 220–228, Springer Nature Switzerland, Cham, 2022, ISBN: 978-3-031-21670-1 978-3-031-21671-8, (Series Title: Lecture Notes in Computer Science).
@incollection{gurney_route_2022,
title = {Route Optimization in Service of a Search and Rescue Artificial Social Intelligence Agent},
author = {Yunzhe Wang and Nikolos Gurney and Jincheng Zhou and David V. Pynadath and Volkan Ustun},
editor = {Nikolos Gurney and Gita Sukthankar},
url = {https://link.springer.com/10.1007/978-3-031-21671-8_14},
doi = {10.1007/978-3-031-21671-8_14},
isbn = {978-3-031-21670-1 978-3-031-21671-8},
year = {2022},
date = {2022-01-01},
urldate = {2023-02-10},
booktitle = {Computational Theory of Mind for Human-Machine Teams},
volume = {13775},
pages = {220–228},
publisher = {Springer Nature Switzerland},
address = {Cham},
note = {Series Title: Lecture Notes in Computer Science},
keywords = {},
pubstate = {published},
tppubtype = {incollection}
}
Gurney, Nikolos; Marsella, Stacy; Ustun, Volkan; Pynadath, David V.
Operationalizing Theories of Theory of Mind: A Survey Book Section
In: Gurney, Nikolos; Sukthankar, Gita (Ed.): Computational Theory of Mind for Human-Machine Teams, vol. 13775, pp. 3–20, Springer Nature Switzerland, Cham, 2022, ISBN: 978-3-031-21670-1 978-3-031-21671-8, (Series Title: Lecture Notes in Computer Science).
@incollection{gurney_operationalizing_2022,
title = {Operationalizing Theories of Theory of Mind: A Survey},
author = {Nikolos Gurney and Stacy Marsella and Volkan Ustun and David V. Pynadath},
editor = {Nikolos Gurney and Gita Sukthankar},
url = {https://link.springer.com/10.1007/978-3-031-21671-8_1},
doi = {10.1007/978-3-031-21671-8_1},
isbn = {978-3-031-21670-1 978-3-031-21671-8},
year = {2022},
date = {2022-01-01},
urldate = {2023-02-10},
booktitle = {Computational Theory of Mind for Human-Machine Teams},
volume = {13775},
pages = {3–20},
publisher = {Springer Nature Switzerland},
address = {Cham},
note = {Series Title: Lecture Notes in Computer Science},
keywords = {},
pubstate = {published},
tppubtype = {incollection}
}
Liu, Lixing; Gurney, Nikolos; McCullough, Kyle; Ustun, Volkan
Graph Neural Network Based Behavior Prediction to Support Multi-Agent Reinforcement Learning in Military Training Simulations Proceedings Article
In: 2021 Winter Simulation Conference (WSC), pp. 1–12, IEEE, Phoenix, AZ, USA, 2021, ISBN: 978-1-66543-311-2.
@inproceedings{liu_graph_2021,
title = {Graph Neural Network Based Behavior Prediction to Support Multi-Agent Reinforcement Learning in Military Training Simulations},
author = {Lixing Liu and Nikolos Gurney and Kyle McCullough and Volkan Ustun},
url = {https://ieeexplore.ieee.org/document/9715433/},
doi = {10.1109/WSC52266.2021.9715433},
isbn = {978-1-66543-311-2},
year = {2021},
date = {2021-12-01},
urldate = {2022-09-21},
booktitle = {2021 Winter Simulation Conference (WSC)},
pages = {1–12},
publisher = {IEEE},
address = {Phoenix, AZ, USA},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Hartholt, Arno; McCullough, Kyle; Mozgai, Sharon; Ustun, Volkan; Gordon, Andrew S
Introducing RIDE: Lowering the Barrier of Entry to Simulation and Training through the Rapid Integration & Development Environment Journal Article
In: pp. 11, 2021.
@article{hartholt_introducing_2021,
title = {Introducing RIDE: Lowering the Barrier of Entry to Simulation and Training through the Rapid Integration & Development Environment},
author = {Arno Hartholt and Kyle McCullough and Sharon Mozgai and Volkan Ustun and Andrew S Gordon},
url = {https://dl.acm.org/doi/10.1145/3472306.3478363},
doi = {10.1145/3472306.3478363},
year = {2021},
date = {2021-11-01},
urldate = {2023-03-31},
booktitle = {Proceedings of the 21st ACM International Conference on Intelligent Virtual Agents},
pages = {11},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
series = {IVA '21},
abstract = {This paper describes the design, development, and philosophy of the Rapid Integration & Development Environment (RIDE). RIDE is a simulation platform that unites many Department of Defense (DoD) and Army simulation efforts to provide an accelerated development foundation and prototyping sandbox that provides direct benefit to the U.S. Army’s Synthetic Training Environment (STE) as well as the larger DoD and Army simulation communities. RIDE integrates a range of capabilities, including One World Terrain, Non-Player Character AI behaviors, xAPI logging, multiplayer networking, scenario creation, destructibility, machine learning approaches, and multi-platform support. The goal of RIDE is to create a simple, drag-and-drop development environment usable by people across all technical levels. RIDE leverages robust game engine technology while designed to be agnostic to any specific game or simulation engine. It provides decision makers with the tools needed to better define requirements and identify potential solutions in much less time and at much reduced costs. RIDE is available through Government Purpose Rights. We aim for RIDE to lower the barrier of entry to research and development efforts within the simulation community in order to reduce required time and effort for simulation and training prototyping. This paper provides an overview of our objective, overall approach, and next steps, in pursuit of these goals.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Hartholt, Arno; McCullough, Kyle; Fast, Ed; Leeds, Andrew; Mozgai, Sharon; Aris, Tim; Ustun, Volkan; Gordon, Andrew; McGroarty, Christopher
Rapid Prototyping for Simulation and Training with the Rapid Integration & Development Environment (RIDE) Proceedings Article
In: 2021.
@inproceedings{hartholt_rapid_2021,
title = {Rapid Prototyping for Simulation and Training with the Rapid Integration & Development Environment (RIDE)},
author = {Arno Hartholt and Kyle McCullough and Ed Fast and Andrew Leeds and Sharon Mozgai and Tim Aris and Volkan Ustun and Andrew Gordon and Christopher McGroarty},
year = {2021},
date = {2021-11-01},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Rosenbloom, Paul S.; Joshi, Himanshu; Ustun, Volkan
(Sub)Symbolic × (a)symmetric × (non)combinatory: A map of AI approaches spanning symbolic/statistical to neural/ML Proceedings Article
In: Proceedings of the 7th Annual Conference on Advances in Cognitive Systems, pp. 113–131, Cognitive Systems Foundation, Cambridge, MA, 2019.
@inproceedings{rosenbloom_subsymbolic_2019,
title = {(Sub)Symbolic × (a)symmetric × (non)combinatory: A map of AI approaches spanning symbolic/statistical to neural/ML},
author = {Paul S. Rosenbloom and Himanshu Joshi and Volkan Ustun},
url = {https://drive.google.com/file/d/1Ynp75A048Mfuh7e3kf_V7hs5kFD7uHsT/view},
year = {2019},
date = {2019-12-01},
booktitle = {Proceedings of the 7th Annual Conference on Advances in Cognitive Systems},
pages = {113–131},
publisher = {Cognitive Systems Foundation},
address = {Cambridge, MA},
abstract = {The traditional symbolic versus subsymbolic dichotomy can be decomposed into three more basic dichotomies, to yield a 3D (2×2×2) space in which symbolic/statistical and neural/ML approaches to intelligence appear in opposite corners. Filling in all eight resulting cells then yields a map that spans a number of standard AI approaches plus a few that may be less familiar. Based on this map, four hypotheses are articulated, explored, and evaluated concerning its relevance to both a deeper understanding of the field of AI as a whole and the general capabilities required in complete AI/cognitive systems.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Rosenbloom, Paul S; Ustun, Volkan
An Architectural Integration of Temporal Motivation Theory for Decision Making Proceedings Article
In: In Proceedings of the 17thAnnual Meeting of the International Conference on Cognitive Modeling, pp. 6, Montreal, Canada, 2019.
@inproceedings{rosenbloom_architectural_2019,
title = {An Architectural Integration of Temporal Motivation Theory for Decision Making},
author = {Paul S Rosenbloom and Volkan Ustun},
url = {https://iccm-conference.neocities.org/2019/proceedings/papers/ICCM2019_paper_7.pdf},
year = {2019},
date = {2019-07-01},
booktitle = {In Proceedings of the 17thAnnual Meeting of the International Conference on Cognitive Modeling},
pages = {6},
address = {Montreal, Canada},
abstract = {Temporal Motivation Theory (TMT) is incorporated into the Sigma cognitive architecture to explore the ability of this combination to yield human-like decision making. In conjunction with Lazy Reinforcement Learning (LRL), which provides the inputs required for this form of decision making, experiments are run on a simple reinforcement learning task, a preference reversal task, and an uncertain two-choice task.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Joshi, Himanshu; Rosenbloom, Paul S; Ustun, Volkan
Exact, Tractable Inference in the Sigma Cognitive Architecture via Sum-Product Networks Journal Article
In: Advances in Cognitive Systems, pp. 31–47, 2018.
@article{joshi_exact_2018,
title = {Exact, Tractable Inference in the Sigma Cognitive Architecture via Sum-Product Networks},
author = {Himanshu Joshi and Paul S Rosenbloom and Volkan Ustun},
url = {http://www.cogsys.org/papers/ACSvol7/papers/paper-7-4.pdf},
year = {2018},
date = {2018-12-01},
journal = {Advances in Cognitive Systems},
pages = {31–47},
abstract = {Sum-product networks (SPNs) are a new kind of deep architecture that support exact, tractable inference over a large class of problems for which traditional graphical models cannot. The Sigma cognitive architecture is based on graphical models, posing a challenge for it to handle problems within this class, such as parsing with probabilistic grammars, a potentially important aspect of language processing. This work proves that an early unidirectional extension to Sigma’s graphical architecture, originally added in service of rule-like behavior but later also shown to support neural networks, can be leveraged to yield exact, tractable computations across this class of problems, and further demonstrates this tractability experimentally for probabilistic parsing. It thus shows that Sigma is able to specify any valid SPN and, despite its grounding in graphical models, retain the desirable inference properties of SPNs when solving them.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Filter
2024
Liu, Lixing; Ustun, Volkan; Kumar, Rajay
Leveraging Organizational Hierarchy to Simplify Reward Design in Cooperative Multi-agent Reinforcement Learning Journal Article
In: FLAIRS, vol. 37, 2024, ISSN: 2334-0762.
Abstract | Links | BibTeX | Tags: Machine Learning
@article{liu_leveraging_2024,
title = {Leveraging Organizational Hierarchy to Simplify Reward Design in Cooperative Multi-agent Reinforcement Learning},
author = {Lixing Liu and Volkan Ustun and Rajay Kumar},
url = {https://journals.flvc.org/FLAIRS/article/view/135588},
doi = {10.32473/flairs.37.1.135588},
issn = {2334-0762},
year = {2024},
date = {2024-05-01},
urldate = {2024-08-13},
journal = {FLAIRS},
volume = {37},
abstract = {The effectiveness of multi-agent reinforcement learning (MARL) hinges largely on the meticulous arrangement of objectives. Yet, conventional MARL methods might not completely harness the inherent structures present in environmental states and agent relationships for goal organization. This study is conducted within the domain of military training simulations, which are typically characterized by complexity, heterogeneity, non-stationary and doctrine-driven environments with a clear organizational hierarchy and a top-down chain of command. This research investigates the approximation and integration of the organizational hierarchy into MARL for cooperative training scenarios, with the goal of streamlining the processes of reward engineering and enhancing team coordination. In the preliminary experiments, we employed two-tiered commander-subordinate feudal hierarchical (CSFH) networks to separate the prioritized team goal and individual goals. The empirical results demonstrate that the proposed framework enhances learning efficiency. It guarantees the learning of a prioritized policy for the commander agent and encourages subordinate agents to explore areas of interest more frequently, guided by appropriate soft constraints imposed by the commander.},
keywords = {Machine Learning},
pubstate = {published},
tppubtype = {article}
}
Aris, Timothy; Ustun, Volkan; Kumar, Rajay
Training Reinforcement Learning Agents to React to an Ambush for Military Simulations Journal Article
In: FLAIRS, vol. 37, 2024, ISSN: 2334-0762.
Abstract | Links | BibTeX | Tags: Simulation, VR
@article{aris_training_2024,
title = {Training Reinforcement Learning Agents to React to an Ambush for Military Simulations},
author = {Timothy Aris and Volkan Ustun and Rajay Kumar},
url = {https://journals.flvc.org/FLAIRS/article/view/135578},
doi = {10.32473/flairs.37.1.135578},
issn = {2334-0762},
year = {2024},
date = {2024-05-01},
urldate = {2024-08-13},
journal = {FLAIRS},
volume = {37},
abstract = {There is a need for realistic Opposing Forces (OPFOR)behavior in military training simulations. Current trainingsimulations generally only have simple, non-adaptivebehaviors, requiring human instructors to play the role ofOPFOR in any complicated scenario. This poster addressesthis need by focusing on a specific scenario: trainingreinforcement learning agents to react to an ambush. Itproposes a novel way to check for occlusion algorithmically.It shows vector fields showing the agent’s actions throughthe course of a training run. It shows that a single agentswitching between multiple goals is possible, at least in asimplified environment. Such an approach could reduce theneed to develop different agents for different scenarios.Finally, it shows a competent agent trained on a simplifiedReact to Ambush scenario, demonstrating the plausibility ofa scaled-up version.},
keywords = {Simulation, VR},
pubstate = {published},
tppubtype = {article}
}
Koresh, Caleb; Ustun, Volkan; Kumar, Rajay; Aris, Tim
Improving Reinforcement Learning Experiments in Unity through Waypoint Utilization Journal Article
In: FLAIRS, vol. 37, 2024, ISSN: 2334-0762.
Abstract | Links | BibTeX | Tags: Machine Learning
@article{koresh_improving_2024,
title = {Improving Reinforcement Learning Experiments in Unity through Waypoint Utilization},
author = {Caleb Koresh and Volkan Ustun and Rajay Kumar and Tim Aris},
url = {https://journals.flvc.org/FLAIRS/article/view/135571},
doi = {10.32473/flairs.37.1.135571},
issn = {2334-0762},
year = {2024},
date = {2024-05-01},
urldate = {2024-08-13},
journal = {FLAIRS},
volume = {37},
abstract = {Multi-agent Reinforcement Learning (MARL) models teams of agents that learn by dynamically interacting with an environment and each other, presenting opportunities to train adaptive models for team-based scenarios. However, MARL algorithms pose substantial challenges due to their immense computational requirements. This paper introduces an automatically generated waypoint-based movement system to abstract and simplify complex environments in Unity while allowing agents to learn strategic cooperation. To demonstrate the effectiveness of our approach, we utilized a simple scenario with heterogeneous roles in each team. We trained this scenario on variations of realistic terrains and compared learning between fine-grained (almost) continuous and waypoint-based movement systems. Our results indicate efficiency in learning and improved performance with waypoint-based navigation. Furthermore, our results show that waypoint-based movement systems can effectively learn differentiated behavior policies for heterogeneous roles in these experiments. These early exploratory results point out the potential of waypoint-based navigation for reducing the computational costs of developing and training MARL models in complex environments. The complete project with all scenarios and results is available on GitHub: https://github.com/HATS-ICT/ml-agents-dodgeball-env-ICT.},
keywords = {Machine Learning},
pubstate = {published},
tppubtype = {article}
}
Ustun, Volkan; Jorvekar, Ronit; Gurney, Nikolos; Pynadath, David; Wang, Yunzhe
Assessing Routing Decisions of Search and Rescue Teams in Service of an Artificial Social Intelligence Agent: Proceedings Article
In: Proceedings of the 16th International Conference on Agents and Artificial Intelligence, pp. 313–320, SCITEPRESS - Science and Technology Publications, Rome, Italy, 2024, ISBN: 978-989-758-680-4.
Links | BibTeX | Tags: AI, Cognitive Architecture, Social Simulation
@inproceedings{ustun_assessing_2024,
title = {Assessing Routing Decisions of Search and Rescue Teams in Service of an Artificial Social Intelligence Agent:},
author = {Volkan Ustun and Ronit Jorvekar and Nikolos Gurney and David Pynadath and Yunzhe Wang},
url = {https://www.scitepress.org/DigitalLibrary/Link.aspx?doi=10.5220/0012388100003636},
doi = {10.5220/0012388100003636},
isbn = {978-989-758-680-4},
year = {2024},
date = {2024-02-01},
urldate = {2024-03-19},
booktitle = {Proceedings of the 16th International Conference on Agents and Artificial Intelligence},
pages = {313–320},
publisher = {SCITEPRESS - Science and Technology Publications},
address = {Rome, Italy},
keywords = {AI, Cognitive Architecture, Social Simulation},
pubstate = {published},
tppubtype = {inproceedings}
}
Gurney, Nikolos; Pynadath, David V.; Ustun, Volkan
Spontaneous Theory of Mind for Artificial Intelligence Journal Article
In: 2024, (Publisher: [object Object] Version Number: 1).
Abstract | Links | BibTeX | Tags: AI, DTIC, Social Simulation, UARC
@article{gurney_spontaneous_2024,
title = {Spontaneous Theory of Mind for Artificial Intelligence},
author = {Nikolos Gurney and David V. Pynadath and Volkan Ustun},
url = {https://arxiv.org/abs/2402.13272},
doi = {10.48550/ARXIV.2402.13272},
year = {2024},
date = {2024-02-01},
urldate = {2024-03-14},
abstract = {Existing approaches to Theory of Mind (ToM) in Artificial Intelligence (AI) overemphasize prompted, or cue-based, ToM, which may limit our collective ability to develop Artificial Social Intelligence (ASI). Drawing from research in computer science, cognitive science, and related disciplines, we contrast prompted ToM with what we call spontaneous ToM – reasoning about others' mental states that is grounded in unintentional, possibly uncontrollable cognitive functions. We argue for a principled approach to studying and developing AI ToM and suggest that a robust, or general, ASI will respond to prompts textbackslashtextitand spontaneously engage in social reasoning.},
note = {Publisher: [object Object]
Version Number: 1},
keywords = {AI, DTIC, Social Simulation, UARC},
pubstate = {published},
tppubtype = {article}
}
Joshi, Himanshu; Ustun, Volkan
Augmenting Cognitive Architectures with Large Language Models Journal Article
In: AAAI-SS, vol. 2, no. 1, pp. 281–285, 2024, ISSN: 2994-4317.
Abstract | Links | BibTeX | Tags: Cognitive Architecture
@article{joshi_augmenting_2024,
title = {Augmenting Cognitive Architectures with Large Language Models},
author = {Himanshu Joshi and Volkan Ustun},
url = {https://ojs.aaai.org/index.php/AAAI-SS/article/view/27689},
doi = {10.1609/aaaiss.v2i1.27689},
issn = {2994-4317},
year = {2024},
date = {2024-01-01},
urldate = {2024-04-16},
journal = {AAAI-SS},
volume = {2},
number = {1},
pages = {281–285},
abstract = {A particular fusion of generative models and cognitive architectures is discussed with the help of the Soar and Sigma cognitive architectures. After a brief introduction to cognitive architecture concepts and Large Language Models as exemplar generative AI models, one approach towards their fusion is discussed. This is then analyzed with a summary of potential benefits and extensions needed to existing cognitive architecture that is closest to the proposal.},
keywords = {Cognitive Architecture},
pubstate = {published},
tppubtype = {article}
}
2023
Aris, Timothy; Ustun, Volkan; Kumar, Rajay
Learning to Take Cover with Navigation-Based Waypoints via Reinforcement Learning Journal Article
In: FLAIRS, vol. 36, 2023, ISSN: 2334-0762.
Abstract | Links | BibTeX | Tags: CogArch, Cognitive Architecture, DTIC, UARC, Virtual Humans
@article{aris_learning_2023,
title = {Learning to Take Cover with Navigation-Based Waypoints via Reinforcement Learning},
author = {Timothy Aris and Volkan Ustun and Rajay Kumar},
url = {https://journals.flvc.org/FLAIRS/article/view/133348},
doi = {10.32473/flairs.36.133348},
issn = {2334-0762},
year = {2023},
date = {2023-05-01},
urldate = {2023-08-04},
journal = {FLAIRS},
volume = {36},
abstract = {This paper presents a reinforcement learning model designed to learn how to take cover on geo-specific terrains, an essential behavior component for military training simulations. Training of the models is performed on the Rapid Integration and Development Environment (RIDE) leveraging the Unity ML-Agents framework. This work expands on previous work on raycast-based agents by increasing the number of enemies from one to three. We demonstrate an automated way of generating training and testing data within geo-specific terrains. We show that replacing the action space with a more abstracted, navmesh-based waypoint movement system can increase the generality and success rate of the models while providing similar results to our previous paper's results regarding retraining across terrains. We also comprehensively evaluate the differences between these and the previous models. Finally, we show that incorporating pixels into the model's input can increase performance at the cost of longer training times.},
keywords = {CogArch, Cognitive Architecture, DTIC, UARC, Virtual Humans},
pubstate = {published},
tppubtype = {article}
}
Pynadath, David V; Gurney, Nikolos; Kenny, Sarah; Kumar, Rajay; Marsella, Stacy C.; Matuszak, Haley; Mostafa, Hala; Ustun, Volkan; Wu, Peggy; Sequeira, Pedro
Effectiveness of Teamwork-Level Interventions through Decision-Theoretic Reasoning in a Minecraft Search-and-Rescue Task Proceedings Article
In: AAMAS '23: Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems, pp. Pages 2334–2336, 2023.
Abstract | Links | BibTeX | Tags: DTIC, Social Simulation, UARC
@inproceedings{pynadath_effectiveness_2023,
title = {Effectiveness of Teamwork-Level Interventions through Decision-Theoretic Reasoning in a Minecraft Search-and-Rescue Task},
author = {David V Pynadath and Nikolos Gurney and Sarah Kenny and Rajay Kumar and Stacy C. Marsella and Haley Matuszak and Hala Mostafa and Volkan Ustun and Peggy Wu and Pedro Sequeira},
url = {https://dl.acm.org/doi/10.5555/3545946.3598925},
year = {2023},
date = {2023-05-01},
booktitle = {AAMAS '23: Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems},
pages = {Pages 2334–2336},
abstract = {Autonomous agents offer the promise of improved human teamwork through automated assessment and assistance during task performance [15, 16, 18]. Studies of human teamwork have identified various processes that underlie joint task performance, while abstracting away the specifics of the task [7, 11, 13, 17].We present here an agent that focuses exclusively on teamwork-level variables in deciding what interventions to use in assisting a human team. Our agent does not directly observe or model the environment or the people in it, but instead relies on input from analytic components (ACs) (developed by other research teams) that process environmental information and output only teamwork-relevant measures. Our agent models these teamwork variables and updates its beliefs over them using a Bayesian Theory of Mind [1], applying Partially Observable Markov Decision Processes (POMDPs) [9] in a recursive manner to assess the state of the team it is currently observing and to choose interventions to best assist them.},
keywords = {DTIC, Social Simulation, UARC},
pubstate = {published},
tppubtype = {inproceedings}
}
Chadalapaka, Viswanath; Ustun, Volkan; Liu, Lixing
Leveraging Graph Networks to Model Environments in Reinforcement Learning Journal Article
In: FLAIRS, vol. 36, 2023, ISSN: 2334-0762.
Abstract | Links | BibTeX | Tags: CogArch, Cognitive Architecture, DTIC, UARC
@article{chadalapaka_leveraging_2023,
title = {Leveraging Graph Networks to Model Environments in Reinforcement Learning},
author = {Viswanath Chadalapaka and Volkan Ustun and Lixing Liu},
url = {https://journals.flvc.org/FLAIRS/article/view/133118},
doi = {10.32473/flairs.36.133118},
issn = {2334-0762},
year = {2023},
date = {2023-05-01},
urldate = {2023-08-04},
journal = {FLAIRS},
volume = {36},
abstract = {This paper proposes leveraging graph neural networks (GNNs) to model an agent’s environment to construct superior policy networks in reinforcement learning (RL). To this end, we explore the effects of different combinations of GNNs and graph network pooling functions on policy performance. We also run experiments at different levels of problem complexity, which affect how easily we expect an agent to learn an optimal policy and therefore show whether or not graph networks are effective at various problem complexity levels. The efficacy of our approach is shown via experimentation in a partially-observable, non-stationary environment that parallels the highly-practical scenario of a military training exercise with human trainees, where the learning goal is to become the best sparring partner possible for human trainees. Our results present that our models can generate better-performing sparring partners by employing GNNs, as demonstrated by these experiments in the proof-of-concept environment. We also explore our model’s applicability in Multi-Agent RL scenarios. Our code is available online at https://github.com/Derposoft/GNNsAsEnvs.},
keywords = {CogArch, Cognitive Architecture, DTIC, UARC},
pubstate = {published},
tppubtype = {article}
}
2022
Aris, Timothy; Ustun, Volkan; Kumar, Rajay
Learning to Take Cover on Geo-Specific Terrains via Reinforcement Learning Journal Article
In: FLAIRS, vol. 35, 2022, ISSN: 2334-0762.
Abstract | Links | BibTeX | Tags: DTIC, Integration Technology
@article{aris_learning_2022,
title = {Learning to Take Cover on Geo-Specific Terrains via Reinforcement Learning},
author = {Timothy Aris and Volkan Ustun and Rajay Kumar},
url = {https://journals.flvc.org/FLAIRS/article/view/130871},
doi = {10.32473/flairs.v35i.130871},
issn = {2334-0762},
year = {2022},
date = {2022-05-01},
urldate = {2022-09-15},
journal = {FLAIRS},
volume = {35},
abstract = {This paper presents a reinforcement learning model designed to learn how to take cover on geo-specific terrains, an essential behavior component for military training simulations. Training of the models is performed on the Rapid Integration and Development Environment (RIDE) leveraging the Unity ML-Agents framework. We show that increasing the number of novel situations the agent is exposed to increases the performance on the test set. In addition, the trained models possess some ability to generalize across terrains, and it can also take less time to retrain an agent to a new terrain, if that terrain has a level of complexity less than or equal to the terrain it was previously trained on.},
keywords = {DTIC, Integration Technology},
pubstate = {published},
tppubtype = {article}
}
Hartholt, Arno; Fast, Ed; Leeds, Andrew; Kim, Kevin; Gordon, Andrew; McCullough, Kyle; Ustun, Volkan; Mozgai, Sharon
Demonstrating the Rapid Integration & Development Environment (RIDE): Embodied Conversational Agent (ECA) and Multiagent Capabilities Proceedings Article
In: Proceedings of the 21st International Conference on Autonomous Agents and Multiagent Systems, pp. 1902–1904, International Foundation for Autonomous Agents and Multiagent Systems, Richland, SC, 2022, ISBN: 978-1-4503-9213-6.
Abstract | BibTeX | Tags: AI, DTIC, Integration Technology, Machine Learning, UARC, VHTL, Virtual Humans
@inproceedings{hartholt_demonstrating_2022,
title = {Demonstrating the Rapid Integration & Development Environment (RIDE): Embodied Conversational Agent (ECA) and Multiagent Capabilities},
author = {Arno Hartholt and Ed Fast and Andrew Leeds and Kevin Kim and Andrew Gordon and Kyle McCullough and Volkan Ustun and Sharon Mozgai},
isbn = {978-1-4503-9213-6},
year = {2022},
date = {2022-05-01},
urldate = {2022-09-20},
booktitle = {Proceedings of the 21st International Conference on Autonomous Agents and Multiagent Systems},
pages = {1902–1904},
publisher = {International Foundation for Autonomous Agents and Multiagent Systems},
address = {Richland, SC},
series = {AAMAS '22},
abstract = {We demonstrate the Rapid Integration & Development Environment (RIDE), a research and development platform that enables rapid prototyping in support of multiagents and embodied conversational agents. RIDE is based on commodity game engines and includes a flexible architecture, system interoperability, and native support for artificial intelligence and machine learning frameworks.},
keywords = {AI, DTIC, Integration Technology, Machine Learning, UARC, VHTL, Virtual Humans},
pubstate = {published},
tppubtype = {inproceedings}
}
Zhou, Jincheng; Ustun, Volkan
PySigma: Towards Enhanced Grand Unification for the Sigma Cognitive Architecture Book Section
In: Goertzel, Ben; Iklé, Matthew; Potapov, Alexey (Ed.): Artificial General Intelligence, vol. 13154, pp. 355–366, Springer International Publishing, Cham, 2022, ISBN: 978-3-030-93757-7 978-3-030-93758-4.
Links | BibTeX | Tags: CogArch, Cognitive Architecture, DTIC, UARC
@incollection{zhou_pysigma_2022,
title = {PySigma: Towards Enhanced Grand Unification for the Sigma Cognitive Architecture},
author = {Jincheng Zhou and Volkan Ustun},
editor = {Ben Goertzel and Matthew Iklé and Alexey Potapov},
url = {https://link.springer.com/10.1007/978-3-030-93758-4_36},
doi = {10.1007/978-3-030-93758-4_36},
isbn = {978-3-030-93757-7 978-3-030-93758-4},
year = {2022},
date = {2022-01-01},
urldate = {2022-09-21},
booktitle = {Artificial General Intelligence},
volume = {13154},
pages = {355–366},
publisher = {Springer International Publishing},
address = {Cham},
keywords = {CogArch, Cognitive Architecture, DTIC, UARC},
pubstate = {published},
tppubtype = {incollection}
}
Wang, Yunzhe; Gurney, Nikolos; Zhou, Jincheng; Pynadath, David V.; Ustun, Volkan
Route Optimization in Service of a Search and Rescue Artificial Social Intelligence Agent Book Section
In: Gurney, Nikolos; Sukthankar, Gita (Ed.): Computational Theory of Mind for Human-Machine Teams, vol. 13775, pp. 220–228, Springer Nature Switzerland, Cham, 2022, ISBN: 978-3-031-21670-1 978-3-031-21671-8, (Series Title: Lecture Notes in Computer Science).
Links | BibTeX | Tags: Cognitive Architecture, Social Simulation, UARC
@incollection{gurney_route_2022,
title = {Route Optimization in Service of a Search and Rescue Artificial Social Intelligence Agent},
author = {Yunzhe Wang and Nikolos Gurney and Jincheng Zhou and David V. Pynadath and Volkan Ustun},
editor = {Nikolos Gurney and Gita Sukthankar},
url = {https://link.springer.com/10.1007/978-3-031-21671-8_14},
doi = {10.1007/978-3-031-21671-8_14},
isbn = {978-3-031-21670-1 978-3-031-21671-8},
year = {2022},
date = {2022-01-01},
urldate = {2023-02-10},
booktitle = {Computational Theory of Mind for Human-Machine Teams},
volume = {13775},
pages = {220–228},
publisher = {Springer Nature Switzerland},
address = {Cham},
note = {Series Title: Lecture Notes in Computer Science},
keywords = {Cognitive Architecture, Social Simulation, UARC},
pubstate = {published},
tppubtype = {incollection}
}
Gurney, Nikolos; Marsella, Stacy; Ustun, Volkan; Pynadath, David V.
Operationalizing Theories of Theory of Mind: A Survey Book Section
In: Gurney, Nikolos; Sukthankar, Gita (Ed.): Computational Theory of Mind for Human-Machine Teams, vol. 13775, pp. 3–20, Springer Nature Switzerland, Cham, 2022, ISBN: 978-3-031-21670-1 978-3-031-21671-8, (Series Title: Lecture Notes in Computer Science).
Links | BibTeX | Tags: Cognitive Architecture, Social Simulation, UARC
@incollection{gurney_operationalizing_2022,
title = {Operationalizing Theories of Theory of Mind: A Survey},
author = {Nikolos Gurney and Stacy Marsella and Volkan Ustun and David V. Pynadath},
editor = {Nikolos Gurney and Gita Sukthankar},
url = {https://link.springer.com/10.1007/978-3-031-21671-8_1},
doi = {10.1007/978-3-031-21671-8_1},
isbn = {978-3-031-21670-1 978-3-031-21671-8},
year = {2022},
date = {2022-01-01},
urldate = {2023-02-10},
booktitle = {Computational Theory of Mind for Human-Machine Teams},
volume = {13775},
pages = {3–20},
publisher = {Springer Nature Switzerland},
address = {Cham},
note = {Series Title: Lecture Notes in Computer Science},
keywords = {Cognitive Architecture, Social Simulation, UARC},
pubstate = {published},
tppubtype = {incollection}
}
2021
Liu, Lixing; Gurney, Nikolos; McCullough, Kyle; Ustun, Volkan
Graph Neural Network Based Behavior Prediction to Support Multi-Agent Reinforcement Learning in Military Training Simulations Proceedings Article
In: 2021 Winter Simulation Conference (WSC), pp. 1–12, IEEE, Phoenix, AZ, USA, 2021, ISBN: 978-1-66543-311-2.
Links | BibTeX | Tags: DTIC, Learning Sciences, UARC, Virtual Humans
@inproceedings{liu_graph_2021,
title = {Graph Neural Network Based Behavior Prediction to Support Multi-Agent Reinforcement Learning in Military Training Simulations},
author = {Lixing Liu and Nikolos Gurney and Kyle McCullough and Volkan Ustun},
url = {https://ieeexplore.ieee.org/document/9715433/},
doi = {10.1109/WSC52266.2021.9715433},
isbn = {978-1-66543-311-2},
year = {2021},
date = {2021-12-01},
urldate = {2022-09-21},
booktitle = {2021 Winter Simulation Conference (WSC)},
pages = {1–12},
publisher = {IEEE},
address = {Phoenix, AZ, USA},
keywords = {DTIC, Learning Sciences, UARC, Virtual Humans},
pubstate = {published},
tppubtype = {inproceedings}
}
Hartholt, Arno; McCullough, Kyle; Mozgai, Sharon; Ustun, Volkan; Gordon, Andrew S
Introducing RIDE: Lowering the Barrier of Entry to Simulation and Training through the Rapid Integration & Development Environment Journal Article
In: pp. 11, 2021.
Abstract | Links | BibTeX | Tags: VHTL
@article{hartholt_introducing_2021,
title = {Introducing RIDE: Lowering the Barrier of Entry to Simulation and Training through the Rapid Integration & Development Environment},
author = {Arno Hartholt and Kyle McCullough and Sharon Mozgai and Volkan Ustun and Andrew S Gordon},
url = {https://dl.acm.org/doi/10.1145/3472306.3478363},
doi = {10.1145/3472306.3478363},
year = {2021},
date = {2021-11-01},
urldate = {2023-03-31},
booktitle = {Proceedings of the 21st ACM International Conference on Intelligent Virtual Agents},
pages = {11},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
series = {IVA '21},
abstract = {This paper describes the design, development, and philosophy of the Rapid Integration & Development Environment (RIDE). RIDE is a simulation platform that unites many Department of Defense (DoD) and Army simulation efforts to provide an accelerated development foundation and prototyping sandbox that provides direct benefit to the U.S. Army’s Synthetic Training Environment (STE) as well as the larger DoD and Army simulation communities. RIDE integrates a range of capabilities, including One World Terrain, Non-Player Character AI behaviors, xAPI logging, multiplayer networking, scenario creation, destructibility, machine learning approaches, and multi-platform support. The goal of RIDE is to create a simple, drag-and-drop development environment usable by people across all technical levels. RIDE leverages robust game engine technology while designed to be agnostic to any specific game or simulation engine. It provides decision makers with the tools needed to better define requirements and identify potential solutions in much less time and at much reduced costs. RIDE is available through Government Purpose Rights. We aim for RIDE to lower the barrier of entry to research and development efforts within the simulation community in order to reduce required time and effort for simulation and training prototyping. This paper provides an overview of our objective, overall approach, and next steps, in pursuit of these goals.},
keywords = {VHTL},
pubstate = {published},
tppubtype = {article}
}
Hartholt, Arno; McCullough, Kyle; Fast, Ed; Leeds, Andrew; Mozgai, Sharon; Aris, Tim; Ustun, Volkan; Gordon, Andrew; McGroarty, Christopher
Rapid Prototyping for Simulation and Training with the Rapid Integration & Development Environment (RIDE) Proceedings Article
In: 2021.
BibTeX | Tags: AI, DTIC, Integration Technology, Machine Learning, Simulation, UARC, VHTL
@inproceedings{hartholt_rapid_2021,
title = {Rapid Prototyping for Simulation and Training with the Rapid Integration & Development Environment (RIDE)},
author = {Arno Hartholt and Kyle McCullough and Ed Fast and Andrew Leeds and Sharon Mozgai and Tim Aris and Volkan Ustun and Andrew Gordon and Christopher McGroarty},
year = {2021},
date = {2021-11-01},
keywords = {AI, DTIC, Integration Technology, Machine Learning, Simulation, UARC, VHTL},
pubstate = {published},
tppubtype = {inproceedings}
}
2019
Rosenbloom, Paul S.; Joshi, Himanshu; Ustun, Volkan
(Sub)Symbolic × (a)symmetric × (non)combinatory: A map of AI approaches spanning symbolic/statistical to neural/ML Proceedings Article
In: Proceedings of the 7th Annual Conference on Advances in Cognitive Systems, pp. 113–131, Cognitive Systems Foundation, Cambridge, MA, 2019.
Abstract | Links | BibTeX | Tags: UARC, Virtual Humans
@inproceedings{rosenbloom_subsymbolic_2019,
title = {(Sub)Symbolic × (a)symmetric × (non)combinatory: A map of AI approaches spanning symbolic/statistical to neural/ML},
author = {Paul S. Rosenbloom and Himanshu Joshi and Volkan Ustun},
url = {https://drive.google.com/file/d/1Ynp75A048Mfuh7e3kf_V7hs5kFD7uHsT/view},
year = {2019},
date = {2019-12-01},
booktitle = {Proceedings of the 7th Annual Conference on Advances in Cognitive Systems},
pages = {113–131},
publisher = {Cognitive Systems Foundation},
address = {Cambridge, MA},
abstract = {The traditional symbolic versus subsymbolic dichotomy can be decomposed into three more basic dichotomies, to yield a 3D (2×2×2) space in which symbolic/statistical and neural/ML approaches to intelligence appear in opposite corners. Filling in all eight resulting cells then yields a map that spans a number of standard AI approaches plus a few that may be less familiar. Based on this map, four hypotheses are articulated, explored, and evaluated concerning its relevance to both a deeper understanding of the field of AI as a whole and the general capabilities required in complete AI/cognitive systems.},
keywords = {UARC, Virtual Humans},
pubstate = {published},
tppubtype = {inproceedings}
}
Rosenbloom, Paul S; Ustun, Volkan
An Architectural Integration of Temporal Motivation Theory for Decision Making Proceedings Article
In: In Proceedings of the 17thAnnual Meeting of the International Conference on Cognitive Modeling, pp. 6, Montreal, Canada, 2019.
Abstract | Links | BibTeX | Tags: UARC, Virtual Humans
@inproceedings{rosenbloom_architectural_2019,
title = {An Architectural Integration of Temporal Motivation Theory for Decision Making},
author = {Paul S Rosenbloom and Volkan Ustun},
url = {https://iccm-conference.neocities.org/2019/proceedings/papers/ICCM2019_paper_7.pdf},
year = {2019},
date = {2019-07-01},
booktitle = {In Proceedings of the 17thAnnual Meeting of the International Conference on Cognitive Modeling},
pages = {6},
address = {Montreal, Canada},
abstract = {Temporal Motivation Theory (TMT) is incorporated into the Sigma cognitive architecture to explore the ability of this combination to yield human-like decision making. In conjunction with Lazy Reinforcement Learning (LRL), which provides the inputs required for this form of decision making, experiments are run on a simple reinforcement learning task, a preference reversal task, and an uncertain two-choice task.},
keywords = {UARC, Virtual Humans},
pubstate = {published},
tppubtype = {inproceedings}
}
2018
Joshi, Himanshu; Rosenbloom, Paul S; Ustun, Volkan
Exact, Tractable Inference in the Sigma Cognitive Architecture via Sum-Product Networks Journal Article
In: Advances in Cognitive Systems, pp. 31–47, 2018.
Abstract | Links | BibTeX | Tags: UARC, Virtual Humans
@article{joshi_exact_2018,
title = {Exact, Tractable Inference in the Sigma Cognitive Architecture via Sum-Product Networks},
author = {Himanshu Joshi and Paul S Rosenbloom and Volkan Ustun},
url = {http://www.cogsys.org/papers/ACSvol7/papers/paper-7-4.pdf},
year = {2018},
date = {2018-12-01},
journal = {Advances in Cognitive Systems},
pages = {31–47},
abstract = {Sum-product networks (SPNs) are a new kind of deep architecture that support exact, tractable inference over a large class of problems for which traditional graphical models cannot. The Sigma cognitive architecture is based on graphical models, posing a challenge for it to handle problems within this class, such as parsing with probabilistic grammars, a potentially important aspect of language processing. This work proves that an early unidirectional extension to Sigma’s graphical architecture, originally added in service of rule-like behavior but later also shown to support neural networks, can be leveraged to yield exact, tractable computations across this class of problems, and further demonstrates this tractability experimentally for probabilistic parsing. It thus shows that Sigma is able to specify any valid SPN and, despite its grounding in graphical models, retain the desirable inference properties of SPNs when solving them.},
keywords = {UARC, Virtual Humans},
pubstate = {published},
tppubtype = {article}
}
Ustun, Volkan; Rosenbloom, Paul S; Sajjadi, Seyed; Nuttall, Jeremy
Controlling Synthetic Characters in Simulations: A Case for Cognitive Architectures and Sigma Proceedings Article
In: Proceedings of I/ITSEC 2018, National Training and Simulation Association, Orlando, FL, 2018.
Abstract | Links | BibTeX | Tags: UARC, Virtual Humans
@inproceedings{ustun_controlling_2018,
title = {Controlling Synthetic Characters in Simulations: A Case for Cognitive Architectures and Sigma},
author = {Volkan Ustun and Paul S Rosenbloom and Seyed Sajjadi and Jeremy Nuttall},
url = {http://bcf.usc.edu/ rosenblo/Pubs/Ustun_IITSEC2018_D.pdf},
year = {2018},
date = {2018-11-01},
booktitle = {Proceedings of I/ITSEC 2018},
publisher = {National Training and Simulation Association},
address = {Orlando, FL},
abstract = {Simulations, along with other similar applications like virtual worlds and video games, require computational models of intelligence that generate realistic and credible behavior for the participating synthetic characters. Cognitive architectures, which are models of the fixed structure underlying intelligent behavior in both natural and artificial systems, provide a conceptually valid common basis, as evidenced by the current efforts towards a standard model of the mind, to generate human-like intelligent behavior for these synthetic characters. Developments in the field of artificial intelligence, mainly in probabilistic graphical models and neural networks, open up new opportunities for cognitive architectures to make the synthetic characters more autonomous and to enrich their behavior. Sigma (Σ) is a cognitive architecture and system that strives to combine what has been learned from four decades of independent work on symbolic cognitive architectures, probabilistic graphical models, and more recently neural models, under its graphical architecture hypothesis. Sigma leverages an extended form of factor graphs towards a uniform grand unification of not only traditional cognitive capabilities but also key non-cognitive aspects, creating unique opportunities for the construction of new kinds of cognitive models that possess a Theory-of-Mind and that are perceptual, autonomous, interactive, affective, and adaptive. In this paper, we will introduce Sigma along with its diverse capabilities and then use three distinct proof-of-concept Sigma models to highlight combinations of these capabilities: (1) Distributional reinforcement learning models in a simple OpenAI Gym problem; (2) A pair of adaptive and interactive agent models that demonstrate rule-based, probabilistic, and social reasoning in a physical security scenario instantiated within the SmartBody character animation platform; and (3) A knowledge-free exploration model in which an agent leverages only architectural appraisal variables, namely attention and curiosity, to locate an item while building up a map in a Unity environment.},
keywords = {UARC, Virtual Humans},
pubstate = {published},
tppubtype = {inproceedings}
}
2017
Rosenbloom, Paul S.; Demski, Abram; Ustun, Volkan
Toward a Neural-Symbolic Sigma: Introducing Neural Network Learning Proceedings Article
In: Proceedings of the 15th Annual Meeting of the International Conference on Cognitive Modelling, 2002–2017 EasyChair, Coventry, United Kingdom, 2017.
Abstract | Links | BibTeX | Tags: UARC, Virtual Humans
@inproceedings{rosenbloom_toward_2017,
title = {Toward a Neural-Symbolic Sigma: Introducing Neural Network Learning},
author = {Paul S. Rosenbloom and Abram Demski and Volkan Ustun},
url = {http://cs.usc.edu/ rosenblo/Pubs/ESNNL%20D.pdf},
year = {2017},
date = {2017-07-01},
booktitle = {Proceedings of the 15th Annual Meeting of the International Conference on Cognitive Modelling},
publisher = {2002–2017 EasyChair},
address = {Coventry, United Kingdom},
abstract = {Building on earlier work extending Sigma’s mixed (symbols + probabilities) graphical band to inference in feedforward neural networks, two forms of neural network learning – target propagation and backpropagation – are introduced, bringing Sigma closer to a full neural-symbolic architecture. Adapting Sigma’s reinforcement learning (RL) capability to use neural networks in policy learning then yields a hybrid form of neural RL with probabilistic action modeling.},
keywords = {UARC, Virtual Humans},
pubstate = {published},
tppubtype = {inproceedings}
}
2016
Joshi, Himanshu; Rosenbloom, Paul S.; Ustun, Volkan
Continuous phone recognition in the Sigma cognitive architecture Journal Article
In: Biologically Inspired Cognitive Architectures, vol. 18, pp. 23–32, 2016, ISSN: 2212683X.
Abstract | Links | BibTeX | Tags: CogArch, UARC, Virtual Humans
@article{joshi_continuous_2016,
title = {Continuous phone recognition in the Sigma cognitive architecture},
author = {Himanshu Joshi and Paul S. Rosenbloom and Volkan Ustun},
url = {http://linkinghub.elsevier.com/retrieve/pii/S2212683X16300652},
doi = {10.1016/j.bica.2016.09.001},
issn = {2212683X},
year = {2016},
date = {2016-10-01},
journal = {Biologically Inspired Cognitive Architectures},
volume = {18},
pages = {23–32},
abstract = {Spoken language processing is an important capability of human intelligence that has hitherto been unexplored by cognitive architectures. This reflects on both the symbolic and sub-symbolic nature of the speech problem, and the capabilities provided by cognitive architectures to model the latter and its rich interplay with the former. Sigma has been designed to leverage the state-of-the-art hybrid (discrete + continuous) mixed (symbolic + probabilistic) capability of graphical models to provide in a uniform non-modular fashion effective forms of, and integration across, both cognitive and sub-cognitive behavior. In this article, previous work on speaker dependent isolated word recognition has been extended to demonstrate Sigma’s feasibility to process a stream of fluent audio and recognize phones, in an online and incremental manner with speaker independence. Phone recognition is an important step in integrating spoken language processing into Sigma. This work also extends the acoustic front-end used in the previous work in service of speaker independence. All of the knowledge used in phone recognition was added supraarchitecturally – i.e. on top of the architecture – without requiring the addition of new mechanisms to the architecture.},
keywords = {CogArch, UARC, Virtual Humans},
pubstate = {published},
tppubtype = {article}
}
Rosenbloom, Paul S.; Demski, Abram; Ustun, Volkan
The Sigma Cognitive Architecture and System: Towards Functionally Elegant Grand Unification Journal Article
In: Journal of Artificial General Intelligence, 2016, ISSN: 1946-0163.
Abstract | Links | BibTeX | Tags: CogArch, UARC, Virtual Humans
@article{rosenbloom_sigma_2016,
title = {The Sigma Cognitive Architecture and System: Towards Functionally Elegant Grand Unification},
author = {Paul S. Rosenbloom and Abram Demski and Volkan Ustun},
url = {http://www.degruyter.com/view/j/jagi.ahead-of-print/jagi-2016-0001/jagi-2016-0001.xml},
doi = {10.1515/jagi-2016-0001},
issn = {1946-0163},
year = {2016},
date = {2016-07-01},
journal = {Journal of Artificial General Intelligence},
abstract = {Sigma (Σ) is a cognitive architecture and system whose development is driven by a combination of four desiderata: grand unification, generic cognition, functional elegance, and sufficient efficiency. Work towards these desiderata is guided by the graphical architecture hypothesis, that key to progress on them is combining what has been learned from over three decades’ worth of separate work on cognitive architectures and graphical models. In this article, these four desiderata are motivated and explained, and then combined with the graphical architecture hypothesis to yield a rationale for the development of Sigma. The current state of the cognitive architecture is then introduced in detail, along with the graphical architecture that sits below it and implements it. Progress in extending Sigma beyond these architectures and towards a full cognitive system is then detailed in terms of both a systematic set of higher level cognitive idioms that have been developed and several virtual humans that are built from combinations of these idioms. Sigma as a whole is then analyzed in terms of how well the progress to date satisfies the desiderata. This article thus provides the first full motivation, presentation and analysis of Sigma, along with a diversity of more specific results that have been generated during its development.},
keywords = {CogArch, UARC, Virtual Humans},
pubstate = {published},
tppubtype = {article}
}
Rosenbloom, Paul S.; Demski, Abram; Ustun, Volkan
Rethinking Sigma’s Graphical Architecture: An Extension to Neural Networks Proceedings Article
In: International Conference on Artificial General Intelligence, pp. 84–94, Springer, New York, NY, 2016, ISBN: 978-3-319-41649-6.
Abstract | Links | BibTeX | Tags: CogArch, UARC, Virtual Humans
@inproceedings{rosenbloom_rethinking_2016,
title = {Rethinking Sigma’s Graphical Architecture: An Extension to Neural Networks},
author = {Paul S. Rosenbloom and Abram Demski and Volkan Ustun},
url = {http://link.springer.com/chapter/10.1007/978-3-319-41649-6_9},
doi = {10.1007/978-3-319-41649-6_9},
isbn = {978-3-319-41649-6},
year = {2016},
date = {2016-07-01},
booktitle = {International Conference on Artificial General Intelligence},
volume = {9782},
pages = {84–94},
publisher = {Springer},
address = {New York, NY},
abstract = {The status of Sigma’s grounding in graphical models is challenged by the ways in which their semantics has been violated while incorporating rule-based reasoning into them. This has led to a rethinking of what goes on in its graphical architecture, with results that include a straightforward extension to feedforward neural networks (although not yet with learning).},
keywords = {CogArch, UARC, Virtual Humans},
pubstate = {published},
tppubtype = {inproceedings}
}
Ustun, Volkan; Rosenbloom, Paul
Towards Truly Autonomous Synthetic Characters with the Sigma Cognitive Architecture Book Section
In: Integrating Cognitive Architectures into Virtual Character Design, pp. 213 – 237, IGI Global, Hershey, PA, 2016, ISBN: 978-1-5225-0454-2.
Abstract | Links | BibTeX | Tags: CogArch, UARC, Virtual Humans
@incollection{ustun_towards_2016,
title = {Towards Truly Autonomous Synthetic Characters with the Sigma Cognitive Architecture},
author = {Volkan Ustun and Paul Rosenbloom},
url = {http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/978-1-5225-0454-2},
isbn = {978-1-5225-0454-2},
year = {2016},
date = {2016-06-01},
booktitle = {Integrating Cognitive Architectures into Virtual Character Design},
pages = {213 – 237},
publisher = {IGI Global},
address = {Hershey, PA},
abstract = {Realism is required not only for how synthetic characters look but also for how they behave. Many applications, such as simulations, virtual worlds, and video games, require computational models of intelligence that generate realistic and credible behavior for the participating synthetic characters. Sigma (Σ) is being built as a computational model of general intelligence with a long-term goal of understanding and replicating the architecture of the mind; i.e., the fixed structure underlying intelligent behavior. Sigma leverages probabilistic graphical models towards a uniform grand unification of not only traditional cognitive capabilities but also key non-cognitive aspects, creating unique opportunities for the construction of new kinds of non-modular behavioral models. These ambitions strive for the complete control of synthetic characters that behave as humanly as possible. In this paper, Sigma is introduced along with two disparate proof-of-concept virtual humans – one conversational and the other a pair of ambulatory agents – that demonstrate its diverse capabilities.},
keywords = {CogArch, UARC, Virtual Humans},
pubstate = {published},
tppubtype = {incollection}
}
2015
Ustun, Volkan; Rosenbloom, Paul S.; Kim, Julia; Li, Lingshan
BUILDING HIGH FIDELITY HUMAN BEHAVIOR MODELS IN THE SIGMA COGNITIVE ARCHITECTURE Proceedings Article
In: Proceedings of the 2015 Winter Simulation Conference, pp. 3124–3125, IEEE, Huntington Beach, CA, 2015, ISBN: 978-1-4673-9741-4.
Abstract | Links | BibTeX | Tags: CogArch, Virtual Humans
@inproceedings{ustun_building_2015,
title = {BUILDING HIGH FIDELITY HUMAN BEHAVIOR MODELS IN THE SIGMA COGNITIVE ARCHITECTURE},
author = {Volkan Ustun and Paul S. Rosenbloom and Julia Kim and Lingshan Li},
url = {http://dl.acm.org/citation.cfm?id=2888619.2888999},
isbn = {978-1-4673-9741-4},
year = {2015},
date = {2015-12-01},
booktitle = {Proceedings of the 2015 Winter Simulation Conference},
pages = {3124–3125},
publisher = {IEEE},
address = {Huntington Beach, CA},
abstract = {Many agent simulations involve computational models of intelligent human behavior. In a variety of cases, these behavior models should be high-fidelity to provide the required realism and credibility. Cognitive architectures may assist the generation of such high-fidelity models as they specify the fixed structure underlying an intelligent cognitive system that does not change over time and across domains. Existing symbolic architectures, such as Soar and ACT-R, have been used in this way, but here the focus is on a new architecture, Sigma (!), that leverages probabilistic graphical models towards a uniform grand unification of not only the traditional cognitive capabilities but also key non-cognitive aspects, and which thus yields unique opportunities for construction of new kinds of non-modular high-fidelity behavior models. Here, we briefly introduce Sigma along with two disparate proof-of-concept virtual humans – one conversational and the other a pair of ambulatory agents – that demonstrate its diverse capabilities.},
keywords = {CogArch, Virtual Humans},
pubstate = {published},
tppubtype = {inproceedings}
}
Ustun, Volkan; Rosenbloom, Paul S.
Towards Adaptive, Interactive Virtual Humans in Sigma Proceedings Article
In: Intelligent Virtual Agents, pp. 98 –108, Springer, Delft, Netherlands, 2015, ISBN: 978-3-319-21995-0.
Abstract | Links | BibTeX | Tags: CogArch, UARC, Virtual Humans
@inproceedings{ustun_towards_2015,
title = {Towards Adaptive, Interactive Virtual Humans in Sigma},
author = {Volkan Ustun and Paul S. Rosenbloom},
url = {http://ict.usc.edu/pubs/Towards%20Adaptive,%20Interactive%20Virtual%20Humans%20in%20Sigma.pdf},
doi = {10.1007/978-3-319-21996-7_10},
isbn = {978-3-319-21995-0},
year = {2015},
date = {2015-08-01},
booktitle = {Intelligent Virtual Agents},
volume = {9238},
pages = {98 –108},
publisher = {Springer},
address = {Delft, Netherlands},
abstract = {Sigma is a nascent cognitive architecture/system that combines concepts from graphical models with traditional symbolic architectures. Here an initial Sigma-based virtual human (VH) is introduced that combines probabilistic reasoning, rule-based decision-making, Theory of Mind, Simultaneous Localization and Mapping and reinforcement learning in a unified manner. This non-modular unification of diverse cognitive, robotic and VH capabilities provides an important first step towards fully adaptive and interactive VHs in Sigma.},
keywords = {CogArch, UARC, Virtual Humans},
pubstate = {published},
tppubtype = {inproceedings}
}
Rosenbloom, Paul S.; Gratch, Jonathan; Ustun, Volkan
Towards Emotion in Sigma: From Appraisal to Attention Proceedings Article
In: Proceedings of AGI 2015, pp. 142 – 151, Springer International Publishing, Berlin, Germany, 2015.
Abstract | Links | BibTeX | Tags: CogArch, UARC, Virtual Humans
@inproceedings{rosenbloom_towards_2015,
title = {Towards Emotion in Sigma: From Appraisal to Attention},
author = {Paul S. Rosenbloom and Jonathan Gratch and Volkan Ustun},
url = {http://ict.usc.edu/pubs/Towards%20Emotion%20in%20Sigma%20-%20From%20Appraisal%20to%20Attention.pdf},
year = {2015},
date = {2015-07-01},
booktitle = {Proceedings of AGI 2015},
volume = {9205},
pages = {142 – 151},
publisher = {Springer International Publishing},
address = {Berlin, Germany},
abstract = {A first step is taken towards incorporating emotional processing into Sigma, a cognitive architecture that is grounded in graphical models, with the addition of appraisal variables for expectedness and desirability plus their initial implications for attention at two levels of the control hierarchy. The results leverage many of Sigma's existing capabilities but with a few key additions.},
keywords = {CogArch, UARC, Virtual Humans},
pubstate = {published},
tppubtype = {inproceedings}
}
Kommers, Cody; Ustun, Volkan; Demski, Abram; Rosenbloom, Paul
Hierarchical Reasoning with Distributed Vector Representations Proceedings Article
In: Proceedings of 37th Annual Conference of the Cognitive Science Society, Cognitive Science Society, Pasadena, CA, 2015.
Abstract | Links | BibTeX | Tags: CogArch, UARC, Virtual Humans
@inproceedings{kommers_hierarchical_2015,
title = {Hierarchical Reasoning with Distributed Vector Representations},
author = {Cody Kommers and Volkan Ustun and Abram Demski and Paul Rosenbloom},
url = {http://ict.usc.edu/pubs/Hierarchical%20Reasoning%20with%20Distributed%20Vector%20Representations.pdf},
year = {2015},
date = {2015-07-01},
booktitle = {Proceedings of 37th Annual Conference of the Cognitive Science Society},
publisher = {Cognitive Science Society},
address = {Pasadena, CA},
abstract = {We demonstrate that distributed vector representations are capable of hierarchical reasoning by summing sets of vectors representing hyponyms (subordinate concepts) to yield a vector that resembles the associated hypernym (superordinate concept). These distributed vector representations constitute a potentially neurally plausible model while demonstrating a high level of performance in many different cognitive tasks. Experiments were run using DVRS, a word embedding system designed for the Sigma cognitive architecture, and Word2Vec, a state-of-the-art word embedding system. These results contribute to a growing body of work demonstrating the various tasks on which distributed vector representations perform competently.},
keywords = {CogArch, UARC, Virtual Humans},
pubstate = {published},
tppubtype = {inproceedings}
}
Garten, Justin; Sagae, Kenji; Ustun, Volkan; Dehghani, Morteza
Combining Distributed Vector Representations for Words Proceedings Article
In: Proceedings of NAACL-HLT 2015, pp. 95–101, Association for Computational Linguistics, Denver, Colorado, 2015.
Abstract | Links | BibTeX | Tags: The Narrative Group, UARC
@inproceedings{garten_combining_2015,
title = {Combining Distributed Vector Representations for Words},
author = {Justin Garten and Kenji Sagae and Volkan Ustun and Morteza Dehghani},
url = {http://ict.usc.edu/pubs/Combining%20Distributed%20Vector%20Representations%20for%20Words.pdf},
year = {2015},
date = {2015-06-01},
booktitle = {Proceedings of NAACL-HLT 2015},
pages = {95–101},
publisher = {Association for Computational Linguistics},
address = {Denver, Colorado},
abstract = {Recent interest in distributed vector representations for words has resulted in an increased diversity of approaches, each with strengths and weaknesses. We demonstrate how diverse vector representations may be inexpensively composed into hybrid representations, effectively leveraging strengths of individual components, as evidenced by substantial improvements on a standard word analogy task. We further compare these results over different sizes of training sets and find these advantages are more pronounced when training data is limited. Finally, we explore the relative impacts of the differences in the learning methods themselves and the size of the contexts they access.},
keywords = {The Narrative Group, UARC},
pubstate = {published},
tppubtype = {inproceedings}
}
Rosenbloom, Paul S.; Demski, Abram; Ustun, Volkan
Efficient message computation in Sigma’s graphical architecture Journal Article
In: Biologically Inspired Cognitive Architectures, vol. 11, pp. 1–9, 2015, ISSN: 2212683X.
Abstract | Links | BibTeX | Tags: CogArch, Cognitive Architecture, UARC
@article{rosenbloom_efficient_2015,
title = {Efficient message computation in Sigma’s graphical architecture},
author = {Paul S. Rosenbloom and Abram Demski and Volkan Ustun},
url = {http://linkinghub.elsevier.com/retrieve/pii/S2212683X14000723},
doi = {10.1016/j.bica.2014.11.009},
issn = {2212683X},
year = {2015},
date = {2015-01-01},
journal = {Biologically Inspired Cognitive Architectures},
volume = {11},
pages = {1–9},
abstract = {Human cognition runs at ∼50 ms per cognitive cycle, implying that any biologically inspired cognitive architecture that strives for real-time performance needs to be able to run at this speed. Sigma is a cognitive architecture built upon graphical models – a broadly applicable state-of-the-art formalism for implementing cognitive capabilities – that are solved via message passing (with complex messages based on n-dimensional piecewise-linear functions). Earlier work explored optimizations to Sigma that reduced by an order of magnitude the number of messages sent per cycle. Here, optimizations are introduced that reduce by an order of magnitude the average time required per message sent.},
keywords = {CogArch, Cognitive Architecture, UARC},
pubstate = {published},
tppubtype = {article}
}
2014
Joshi, Himanshu; Rosenbloom, Paul S.; Ustun, Volkan
Isolated word recognition in the Sigma cognitive architecture Journal Article
In: Biologically Inspired Cognitive Architectures, vol. 10, pp. 1–9, 2014, ISSN: 2212683X.
Abstract | Links | BibTeX | Tags: CogArch, Cognitive Architecture, UARC
@article{joshi_isolated_2014,
title = {Isolated word recognition in the Sigma cognitive architecture},
author = {Himanshu Joshi and Paul S. Rosenbloom and Volkan Ustun},
url = {http://linkinghub.elsevier.com/retrieve/pii/S2212683X14000644},
doi = {10.1016/j.bica.2014.11.001},
issn = {2212683X},
year = {2014},
date = {2014-10-01},
journal = {Biologically Inspired Cognitive Architectures},
volume = {10},
pages = {1–9},
abstract = {Symbolic architectures are effective at complex cognitive reasoning, but typically are incapable of important forms of sub-cognitive processing – such as perception – without distinct modules connected to them via low-bandwidth interfaces. Neural architectures, in contrast, may be quite effective at the latter, but typically struggle with the former. Sigma has been designed to leverage the state-of-the-art hybrid (discrete + continuous) mixed (symbolic + probabilistic) capability of graphical models to provide in a uniform non-modular fashion effective forms of, and integration across, both cognitive and sub-cognitive behavior. Here it is shown that Sigma is not only capable of performing a simple variant of speech recognition via the same knowledge structures and reasoning algorithm used for cognitive processing, but also of leveraging its existing knowledge templates and learning algorithm to acquire automatically most of the structures and parameters needed for this recognition activity.},
keywords = {CogArch, Cognitive Architecture, UARC},
pubstate = {published},
tppubtype = {article}
}
Ustun, Volkan; Rosenbloom, Paul S.; Sagae, Kenji; Demski, Abram
Distributed Vector Representations of Words in the Sigma Cognitive Architecture Proceedings Article
In: Proceedings of the 7th Conference on Artificial General Intelligence 2014, Québec City, Canada, 2014.
Abstract | Links | BibTeX | Tags: CogArch, Cognitive Architecture, UARC, Virtual Humans
@inproceedings{ustun_distributed_2014,
title = {Distributed Vector Representations of Words in the Sigma Cognitive Architecture},
author = {Volkan Ustun and Paul S. Rosenbloom and Kenji Sagae and Abram Demski},
url = {http://ict.usc.edu/pubs/Distributed%20Vector%20Representations%20of%20Words%20in%20the%20Sigma%20Cognitive%20Architecture.pdf},
year = {2014},
date = {2014-08-01},
booktitle = {Proceedings of the 7th Conference on Artificial General Intelligence 2014},
address = {Québec City, Canada},
abstract = {Recently reported results with distributed-vector word representations in natural language processing make them appealing for incorporation into a general cognitive architecture like Sigma. This paper describes a new algorithm for learning such word representations from large, shallow information resources, and how this algorithm can be implemented via small modifications to Sigma. The effectiveness and speed of the algorithm are evaluated via a comparison of an external simulation of it with state-of-the-art algorithms. The results from more limited experiments with Sigma are also promising, but more work is required for it to reach the effectiveness and speed of the simulation.},
keywords = {CogArch, Cognitive Architecture, UARC, Virtual Humans},
pubstate = {published},
tppubtype = {inproceedings}
}
2013
Rosenbloom, Paul S.; Demski, Abram; Han, Teawon; Ustun, Volkan
Learning via Gradient Descent in Sigma Proceedings Article
In: International Conference on Cognitive Modeling, Ottawa, Canada, 2013.
Abstract | Links | BibTeX | Tags: CogArch, Cognitive Architecture, UARC, Virtual Humans
@inproceedings{rosenbloom_learning_2013,
title = {Learning via Gradient Descent in Sigma},
author = {Paul S. Rosenbloom and Abram Demski and Teawon Han and Volkan Ustun},
url = {http://ict.usc.edu/pubs/Learning%20via%20Gradient%20Descent%20in%20Sigma.pdf},
year = {2013},
date = {2013-07-01},
booktitle = {International Conference on Cognitive Modeling},
address = {Ottawa, Canada},
abstract = {Integrating a gradient-descent learning mechanism at the core of the graphical models upon which the Sigma cognitive architecture/system is built yields learning behaviors that span important forms of both procedural learning (e.g., action and reinforcement learning) and declarative learning (e.g., supervised and unsupervised concept formation), plus several additional forms of learning (e.g., distribution tracking and map learning) relevant to cognitive systems/modeling. The core result presented here is this breadth of cognitive learning behaviors that is producible in this uniform manner.},
keywords = {CogArch, Cognitive Architecture, UARC, Virtual Humans},
pubstate = {published},
tppubtype = {inproceedings}
}
0000
Hartholt, Arno; McCullough, Kyle; Mozgai, Sharon; Ustun, Volkan; Gordon, Andrew S
Introducing RIDE: Lowering the Barrier of Entry to Simulation and Training through the Rapid Integration & Development Environment Journal Article
In: pp. 11, 0000.
@article{hartholt_introducing_nodate-1,
title = {Introducing RIDE: Lowering the Barrier of Entry to Simulation and Training through the Rapid Integration & Development Environment},
author = {Arno Hartholt and Kyle McCullough and Sharon Mozgai and Volkan Ustun and Andrew S Gordon},
pages = {11},
abstract = {This paper describes the design, development, and philosophy of the Rapid Integration & Development Environment (RIDE). RIDE is a simulation platform that unites many Department of Defense (DoD) and Army simulation efforts to provide an accelerated development foundation and prototyping sandbox that provides direct benefit to the U.S. Army’s Synthetic Training Environment (STE) as well as the larger DoD and Army simulation communities. RIDE integrates a range of capabilities, including One World Terrain, Non-Player Character AI behaviors, xAPI logging, multiplayer networking, scenario creation, destructibility, machine learning approaches, and multi-platform support. The goal of RIDE is to create a simple, drag-and-drop development environment usable by people across all technical levels. RIDE leverages robust game engine technology while designed to be agnostic to any specific game or simulation engine. It provides decision makers with the tools needed to better define requirements and identify potential solutions in much less time and at much reduced costs. RIDE is available through Government Purpose Rights. We aim for RIDE to lower the barrier of entry to research and development efforts within the simulation community in order to reduce required time and effort for simulation and training prototyping. This paper provides an overview of our objective, overall approach, and next steps, in pursuit of these goals.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Hartholt, Arno; McCullough, Kyle; Mozgai, Sharon; Ustun, Volkan; Gordon, Andrew S
Introducing RIDE: Lowering the Barrier of Entry to Simulation and Training through the Rapid Integration & Development Environment Journal Article
In: pp. 11, 0000.
@article{hartholt_introducing_nodate,
title = {Introducing RIDE: Lowering the Barrier of Entry to Simulation and Training through the Rapid Integration & Development Environment},
author = {Arno Hartholt and Kyle McCullough and Sharon Mozgai and Volkan Ustun and Andrew S Gordon},
pages = {11},
abstract = {This paper describes the design, development, and philosophy of the Rapid Integration & Development Environment (RIDE). RIDE is a simulation platform that unites many Department of Defense (DoD) and Army simulation efforts to provide an accelerated development foundation and prototyping sandbox that provides direct benefit to the U.S. Army’s Synthetic Training Environment (STE) as well as the larger DoD and Army simulation communities. RIDE integrates a range of capabilities, including One World Terrain, Non-Player Character AI behaviors, xAPI logging, multiplayer networking, scenario creation, destructibility, machine learning approaches, and multi-platform support. The goal of RIDE is to create a simple, drag-and-drop development environment usable by people across all technical levels. RIDE leverages robust game engine technology while designed to be agnostic to any specific game or simulation engine. It provides decision makers with the tools needed to better define requirements and identify potential solutions in much less time and at much reduced costs. RIDE is available through Government Purpose Rights. We aim for RIDE to lower the barrier of entry to research and development efforts within the simulation community in order to reduce required time and effort for simulation and training prototyping. This paper provides an overview of our objective, overall approach, and next steps, in pursuit of these goals.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}