Publications

@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 = {2024-01-01},

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}

}

@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 = {2023-01-01},

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 = {Social Simulation, UARC},

pubstate = {published},

tppubtype = {inproceedings}

}

@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}

}

@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}

}

@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}

}

@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}

}

@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}

}

@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}

}

@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}

}

@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}

}

@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}

}

@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}

}

@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}

}

@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}

}

@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}

}

@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}

}

@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}

}