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61.
Graesser, Arthur C; Hu, Xiangen; Nye, Benjamin D.; Sottilare, Robert A.
Intelligent Tutoring Systems, Serious Games, and the Generalized Intelligent Framework for Tutoring (GIFT) Book Section
In: Using Games and Simulations for Teaching and Assessment, pp. 58–79, Routledge, New York, NY, 2016, ISBN: 978-0-415-73787-6.
@incollection{graesser_intelligent_2016,
title = {Intelligent Tutoring Systems, Serious Games, and the Generalized Intelligent Framework for Tutoring (GIFT)},
author = {Arthur C Graesser and Xiangen Hu and Benjamin D. Nye and Robert A. Sottilare},
url = {https://www.researchgate.net/publication/304013322_Intelligent_Tutoring_Systems_Serious_Games_and_the_Generalized_Intelligent_Framework_for_Tutoring_GIFT},
isbn = {978-0-415-73787-6},
year = {2016},
date = {2016-01-01},
booktitle = {Using Games and Simulations for Teaching and Assessment},
pages = {58–79},
publisher = {Routledge},
address = {New York, NY},
abstract = {This chapter explores the prospects of integrating games with intelligent tutoring systems (ITSs). The hope is that there can be learning environments that optimize both motivation through games and deep learning through ITS technologies. Deep learning refers to the acquisition of knowledge, skills, strategies, and reasoning processes at the higher levels of Bloom’s (1956) taxonomy or the Knowledge-Learning-Instruction (KLI) framework (Koedinger, Corbett, & Perfetti, 2012), such as the application of knowledge to new cases, knowledge analysis and synthesis, problem solving, critical thinking, and other difficult cognitive processes. In contrast, shallow learning involves perceptual learning, memorization of explicit material, and mastery of simple rigid procedures. Shallow knowledge may be adequate for near transfer tests of knowledge/skills but not far transfer tests to new situations that have some modicum of complexity.},
keywords = {},
pubstate = {published},
tppubtype = {incollection}
}
This chapter explores the prospects of integrating games with intelligent tutoring systems (ITSs). The hope is that there can be learning environments that optimize both motivation through games and deep learning through ITS technologies. Deep learning refers to the acquisition of knowledge, skills, strategies, and reasoning processes at the higher levels of Bloom’s (1956) taxonomy or the Knowledge-Learning-Instruction (KLI) framework (Koedinger, Corbett, & Perfetti, 2012), such as the application of knowledge to new cases, knowledge analysis and synthesis, problem solving, critical thinking, and other difficult cognitive processes. In contrast, shallow learning involves perceptual learning, memorization of explicit material, and mastery of simple rigid procedures. Shallow knowledge may be adequate for near transfer tests of knowledge/skills but not far transfer tests to new situations that have some modicum of complexity.
62.
Rizzo, Albert; Talbot, Thomas
Virtual Reality Standardized Patients for Clinical Training Book Section
In: The Digital Patient, pp. 255–272, John Wiley & Sons, Inc, Hoboken, NJ, 2016, ISBN: 978-1-118-95278-8 978-1-118-95275-7.
@incollection{rizzo_virtual_2016,
title = {Virtual Reality Standardized Patients for Clinical Training},
author = {Albert Rizzo and Thomas Talbot},
url = {http://doi.wiley.com/10.1002/9781118952788.ch18},
isbn = {978-1-118-95278-8 978-1-118-95275-7},
year = {2016},
date = {2016-01-01},
booktitle = {The Digital Patient},
pages = {255–272},
publisher = {John Wiley & Sons, Inc},
address = {Hoboken, NJ},
abstract = {There are several quite distinct educational approaches that are all called a virtual patient. It includes case presentations, interactive patient scenarios, virtual patient games, human standardized patients (HSPs), high-fidelity software simulations, high-fidelity manikins, and virtual human (VH) conversational agents. VH conversations are possible that include an avatar that responds to pre-selected choices; such an interview is called a structured encounter. Most VSPs attempted to date have been on traditional computers. With the increased prevalence of mobile devices, it is logical to consider the migration of VSP technology to phones and tablets. Future distant recognition (DSR) systems will require a high level of individual speaker discrimination and will likely adopt microphone array-based acoustic beam forming technology. Future success may no longer be rate-limited by the pace of technology, but by the creativity and innovation of educators who will create compelling VSP experiences and curricula.},
keywords = {},
pubstate = {published},
tppubtype = {incollection}
}
There are several quite distinct educational approaches that are all called a virtual patient. It includes case presentations, interactive patient scenarios, virtual patient games, human standardized patients (HSPs), high-fidelity software simulations, high-fidelity manikins, and virtual human (VH) conversational agents. VH conversations are possible that include an avatar that responds to pre-selected choices; such an interview is called a structured encounter. Most VSPs attempted to date have been on traditional computers. With the increased prevalence of mobile devices, it is logical to consider the migration of VSP technology to phones and tablets. Future distant recognition (DSR) systems will require a high level of individual speaker discrimination and will likely adopt microphone array-based acoustic beam forming technology. Future success may no longer be rate-limited by the pace of technology, but by the creativity and innovation of educators who will create compelling VSP experiences and curricula.
63.
Cukor, Judith; Gerardi, Maryrose; Alley, Stephanie; Reist, Christopher; Roy, Michael; Rothbaum, Barbara O.; Difede, JoAnn; Rizzo, Albert
Virtual Reality Exposure Therapy for Combat-Related PTSD Book Section
In: Posttraumatic Stress Disorder and Related Diseases in Combat Veterans, pp. 69–83, Springer International Publishing, Cham, Switzerland, 2016, ISBN: 978-3-319-22984-3 978-3-319-22985-0.
@incollection{cukor_virtual_2016,
title = {Virtual Reality Exposure Therapy for Combat-Related PTSD},
author = {Judith Cukor and Maryrose Gerardi and Stephanie Alley and Christopher Reist and Michael Roy and Barbara O. Rothbaum and JoAnn Difede and Albert Rizzo},
url = {http://link.springer.com/10.1007/978-3-319-22985-0_7},
isbn = {978-3-319-22984-3 978-3-319-22985-0},
year = {2016},
date = {2016-01-01},
booktitle = {Posttraumatic Stress Disorder and Related Diseases in Combat Veterans},
pages = {69–83},
publisher = {Springer International Publishing},
address = {Cham, Switzerland},
abstract = {War is perhaps one of the most challenging situations that a human being can experience. The physical, emotional, cognitive, and psychological demands of a combat environment place enormous stress on even the best-prepared military personnel. Numerous reports indicate that the incidence of posttraumatic stress disorder (PTSD) in returning Operation Enduring Freedom/Operation Iraqi Freedom (OEF/OIF) military personnel is significant. This has served to motivate research on how to better develop and disseminate evidence-based treatments for PTSD that leverage the unique features available with virtual reality (VR) technology. VR-delivered exposure therapy for PTSD is currently being used to treat combatand terrorist attack-related PTSD with initial reports of positive outcomes. This chapter presents a brief overview and rationale for the use of VR exposure for combat-related PTSD and describes the Virtual Iraq/Afghanistan exposure therapy system. This includes a short review of the previous literature, a description of the system components and the treatment protocol, and a case presentation. VR offers an alternative format for delivering exposure-based therapies for PTSD that may appeal to certain service members and veterans who grew up “digital” and who might be inclined to seek treatment in this fashion.},
keywords = {},
pubstate = {published},
tppubtype = {incollection}
}
War is perhaps one of the most challenging situations that a human being can experience. The physical, emotional, cognitive, and psychological demands of a combat environment place enormous stress on even the best-prepared military personnel. Numerous reports indicate that the incidence of posttraumatic stress disorder (PTSD) in returning Operation Enduring Freedom/Operation Iraqi Freedom (OEF/OIF) military personnel is significant. This has served to motivate research on how to better develop and disseminate evidence-based treatments for PTSD that leverage the unique features available with virtual reality (VR) technology. VR-delivered exposure therapy for PTSD is currently being used to treat combatand terrorist attack-related PTSD with initial reports of positive outcomes. This chapter presents a brief overview and rationale for the use of VR exposure for combat-related PTSD and describes the Virtual Iraq/Afghanistan exposure therapy system. This includes a short review of the previous literature, a description of the system components and the treatment protocol, and a case presentation. VR offers an alternative format for delivering exposure-based therapies for PTSD that may appeal to certain service members and veterans who grew up “digital” and who might be inclined to seek treatment in this fashion.
64.
Traum, David; Jones, Andrew; Hays, Kia; Maio, Heather; Alexander, Oleg; Artstein, Ron; Debevec, Paul; Gainer, Alesia; Georgila, Kallirroi; Haase, Kathleen; Jungblut, Karen; Leuski, Anton; Smith, Stephen; Swartout, William
New Dimensions in Testimony: Digitally Preserving a Holocaust Survivor’s Interactive Storytelling Book Section
In: Interactive Storytelling, vol. 9445, pp. 269–281, Springer International Publishing, Copenhagen, Denmark, 2015, ISBN: 978-3-319-27035-7 978-3-319-27036-4.
@incollection{traum_new_2015,
title = {New Dimensions in Testimony: Digitally Preserving a Holocaust Survivor’s Interactive Storytelling},
author = {David Traum and Andrew Jones and Kia Hays and Heather Maio and Oleg Alexander and Ron Artstein and Paul Debevec and Alesia Gainer and Kallirroi Georgila and Kathleen Haase and Karen Jungblut and Anton Leuski and Stephen Smith and William Swartout},
url = {http://link.springer.com/10.1007/978-3-319-27036-4_26},
isbn = {978-3-319-27035-7 978-3-319-27036-4},
year = {2015},
date = {2015-12-01},
booktitle = {Interactive Storytelling},
volume = {9445},
pages = {269–281},
publisher = {Springer International Publishing},
address = {Copenhagen, Denmark},
abstract = {We describe a digital system that allows people to have an interactive conversation with a human storyteller (a Holocaust survivor) who has recorded a number of dialogue contributions, including many compelling narratives of his experiences and thoughts. The goal is to preserve as much as possible of the experience of face-to-face interaction. The survivor's stories, answers to common questions, and testimony are recorded in high ⬚delity, and then delivered interactively to an audience as responses to spoken questions. People can ask questions and receive answers on a broad range of topics including the survivor's experiences before, after and during the war, his attitudes and philosophy. Evaluation results show that most user questions can be addressed by the system, and that audiences are highly engaged with the resulting interaction.},
keywords = {},
pubstate = {published},
tppubtype = {incollection}
}
We describe a digital system that allows people to have an interactive conversation with a human storyteller (a Holocaust survivor) who has recorded a number of dialogue contributions, including many compelling narratives of his experiences and thoughts. The goal is to preserve as much as possible of the experience of face-to-face interaction. The survivor's stories, answers to common questions, and testimony are recorded in high ⬚delity, and then delivered interactively to an audience as responses to spoken questions. People can ask questions and receive answers on a broad range of topics including the survivor's experiences before, after and during the war, his attitudes and philosophy. Evaluation results show that most user questions can be addressed by the system, and that audiences are highly engaged with the resulting interaction.
65.
Roemmele, Melissa; Gordon, Andrew S.
Creative Help: A Story Writing Assistant Book Section
In: Interactive Storytelling, vol. 9445, pp. 81–92, Springer International Publishing, Copenhagen, Denmark, 2015, ISBN: 978-3-319-27036-4.
@incollection{roemmele_creative_2015,
title = {Creative Help: A Story Writing Assistant},
author = {Melissa Roemmele and Andrew S. Gordon},
url = {http://link.springer.com/10.1007/978-3-319-27036-4_8},
isbn = {978-3-319-27036-4},
year = {2015},
date = {2015-12-01},
booktitle = {Interactive Storytelling},
volume = {9445},
pages = {81–92},
publisher = {Springer International Publishing},
address = {Copenhagen, Denmark},
abstract = {We present Creative Help, an application that helps writers by generating suggestions for the next sentence in a story as it being written. Users can modify or delete suggestions according to their own vision of the unfolding narrative. The application tracks users' changes to suggestions in order to measure their perceived helpfulness to the story, with fewer edits indicating more helpful suggestions. We demonstrate how the edit distance between a suggestion and its resulting modi⬚cation can be used to comparatively evaluate di⬚erent models for generating suggestions. We describe a generation model that uses case-based reasoning to find relevant suggestions from a large corpus of stories. The application shows that this model generates suggestions that are more helpful than randomly selected suggestions at a level of marginal statistical signifcance. By giving users control over the generated content, Creative Help provides a new opportunity in open-domain interactive storytelling.},
keywords = {},
pubstate = {published},
tppubtype = {incollection}
}
We present Creative Help, an application that helps writers by generating suggestions for the next sentence in a story as it being written. Users can modify or delete suggestions according to their own vision of the unfolding narrative. The application tracks users' changes to suggestions in order to measure their perceived helpfulness to the story, with fewer edits indicating more helpful suggestions. We demonstrate how the edit distance between a suggestion and its resulting modi⬚cation can be used to comparatively evaluate di⬚erent models for generating suggestions. We describe a generation model that uses case-based reasoning to find relevant suggestions from a large corpus of stories. The application shows that this model generates suggestions that are more helpful than randomly selected suggestions at a level of marginal statistical signifcance. By giving users control over the generated content, Creative Help provides a new opportunity in open-domain interactive storytelling.
66.
Chatterjee, Moitreya; Leuski, Anton
A Novel Statistical Approach for Image and Video Retrieval and Its Adaption for Active Learning Book Section
In: A Novel Statistical Approach for Image and Video Retrieval and Its Adaption for Active Learning, pp. 935–938, ACM, Brisbane, Australia, 2015, ISBN: 978-1-4503-3459-4.
@incollection{chatterjee_novel_2015,
title = {A Novel Statistical Approach for Image and Video Retrieval and Its Adaption for Active Learning},
author = {Moitreya Chatterjee and Anton Leuski},
url = {http://dl.acm.org/citation.cfm?id=2806368},
isbn = {978-1-4503-3459-4},
year = {2015},
date = {2015-10-01},
booktitle = {A Novel Statistical Approach for Image and Video Retrieval and Its Adaption for Active Learning},
pages = {935–938},
publisher = {ACM},
address = {Brisbane, Australia},
abstract = {The ever expanding multimedia content (such as images and videos), especially on the web, necessitates e⬚ective text query-based search (or retrieval) systems. Popular approaches for addressing this issue, use the query-likelihood model which fails to capture the user's information needs. In this work therefore, we explore a new ranking approach in the context of image and video retrieval from text queries. Our approach assumes two separate underlying distributions for query and the document respectively. We then, determine the extent of similarity between these two statistical distributions for the task of ranking. Furthermore we extend our approach, using Active Learning techniques, to address the question of obtaining a good performance without requiring a fully labeled training dataset. This is done by taking Sample Uncertainty, Density and Diversity into account. Our experiments on the popular TRECVID corpus and the open, relatively small-sized USC SmartBody corpus show that we are almost at-par or sometimes better than multiple state-of-the-art baselines.},
keywords = {},
pubstate = {published},
tppubtype = {incollection}
}
The ever expanding multimedia content (such as images and videos), especially on the web, necessitates e⬚ective text query-based search (or retrieval) systems. Popular approaches for addressing this issue, use the query-likelihood model which fails to capture the user's information needs. In this work therefore, we explore a new ranking approach in the context of image and video retrieval from text queries. Our approach assumes two separate underlying distributions for query and the document respectively. We then, determine the extent of similarity between these two statistical distributions for the task of ranking. Furthermore we extend our approach, using Active Learning techniques, to address the question of obtaining a good performance without requiring a fully labeled training dataset. This is done by taking Sample Uncertainty, Density and Diversity into account. Our experiments on the popular TRECVID corpus and the open, relatively small-sized USC SmartBody corpus show that we are almost at-par or sometimes better than multiple state-of-the-art baselines.
67.
Kang, Sin-Hwa; Feng, Andrew W.; Leuski, Anton; Casas, Dan; Shapiro, Ari
The Effect of An Animated Virtual Character on Mobile Chat Interactions Book Section
In: Proceedings of the 3rd International Conference on Human-Agent Interaction, pp. 105–112, ACM, Daegu, Korea, 2015, ISBN: 978-1-4503-3527-0.
@incollection{kang_effect_2015,
title = {The Effect of An Animated Virtual Character on Mobile Chat Interactions},
author = {Sin-Hwa Kang and Andrew W. Feng and Anton Leuski and Dan Casas and Ari Shapiro},
url = {http://dl.acm.org/citation.cfm?id=2814957},
isbn = {978-1-4503-3527-0},
year = {2015},
date = {2015-10-01},
booktitle = {Proceedings of the 3rd International Conference on Human-Agent Interaction},
pages = {105–112},
publisher = {ACM},
address = {Daegu, Korea},
abstract = {This study explores presentation techniques for a 3D animated chat-based virtual human that communicates engagingly with users. Interactions with the virtual human occur via a smartphone outside of the lab in natural settings. Our work compares the responses of users who interact with no image or a static image of a virtual character as opposed to the animated visage of a virtual human capable of displaying appropriate nonverbal behavior. We further investigate users’ responses to the animated character’s gaze aversion which displayed the character’s act of looking away from users and was presented as a listening behavior. The findings of our study demonstrate that people tend to engage in conversation more by talking for a longer amount of time when they interact with a 3D animated virtual human that averts its gaze, compared to an animated virtual human that does not avert its gaze, a static image of a virtual character, or an audio-only interface.},
keywords = {},
pubstate = {published},
tppubtype = {incollection}
}
This study explores presentation techniques for a 3D animated chat-based virtual human that communicates engagingly with users. Interactions with the virtual human occur via a smartphone outside of the lab in natural settings. Our work compares the responses of users who interact with no image or a static image of a virtual character as opposed to the animated visage of a virtual human capable of displaying appropriate nonverbal behavior. We further investigate users’ responses to the animated character’s gaze aversion which displayed the character’s act of looking away from users and was presented as a listening behavior. The findings of our study demonstrate that people tend to engage in conversation more by talking for a longer amount of time when they interact with a 3D animated virtual human that averts its gaze, compared to an animated virtual human that does not avert its gaze, a static image of a virtual character, or an audio-only interface.
68.
Rizzo, Albert "Skip"; Shilling, Russell; Forbell, Eric; Scherer, Stefan; Gratch, Jonathan; Morency, Louis-Philippe
Autonomous Virtual Human Agents for Healthcare Information Support and Clinical Interviewing Book Section
In: pp. 53–79, Elsevier, Inc., Philadelphia, PA, 2015, ISBN: 978-0-12-420248-1.
@incollection{rizzo_autonomous_2015,
title = {Autonomous Virtual Human Agents for Healthcare Information Support and Clinical Interviewing},
author = {Albert "Skip" Rizzo and Russell Shilling and Eric Forbell and Stefan Scherer and Jonathan Gratch and Louis-Philippe Morency},
url = {http://www.sciencedirect.com/science/article/pii/B9780124202481000039},
isbn = {978-0-12-420248-1},
year = {2015},
date = {2015-10-01},
pages = {53–79},
publisher = {Elsevier, Inc.},
address = {Philadelphia, PA},
abstract = {Over the last 20 years, a virtual revolution has taken place in the use of Virtual Reality simulation technology for clinical purposes. Recent shifts in the social and scientific landscape have now set the stage for the next major movement in Clinical Virtual Reality with the “birth” of intelligent virtual human (VH) agents. Seminal research and development has appeared in the creation of highly interactive, artificially intelligent and natural language capable VHs that can engage real human users in a credible fashion. VHs can now be designed to perceive and act in a virtual world, engage in face-to-face spoken dialogues, and in some cases they are capable of exhibiting human-like emotional reactions. This chapter will detail our applications in this area where a virtual human can provide private online healthcare information and support (i.e., SimCoach) and where a VH can serve the role as a clinical interviewer (i.e., SimSensei).},
keywords = {},
pubstate = {published},
tppubtype = {incollection}
}
Over the last 20 years, a virtual revolution has taken place in the use of Virtual Reality simulation technology for clinical purposes. Recent shifts in the social and scientific landscape have now set the stage for the next major movement in Clinical Virtual Reality with the “birth” of intelligent virtual human (VH) agents. Seminal research and development has appeared in the creation of highly interactive, artificially intelligent and natural language capable VHs that can engage real human users in a credible fashion. VHs can now be designed to perceive and act in a virtual world, engage in face-to-face spoken dialogues, and in some cases they are capable of exhibiting human-like emotional reactions. This chapter will detail our applications in this area where a virtual human can provide private online healthcare information and support (i.e., SimCoach) and where a VH can serve the role as a clinical interviewer (i.e., SimSensei).
69.
Lane, H. Chad; Core, Mark G.; Goldberg, Benjamin S.
Lowering the Technical Skill Requirements for Building Intelligent Tutors: A Review of Authoring Tools Book Section
In: Design Recommendations for Intelligent Tutoring Systems, vol. 3, pp. 303 – 318, U.S. Army Research Laboratory, 2015.
@incollection{lane_lowering_2015,
title = {Lowering the Technical Skill Requirements for Building Intelligent Tutors: A Review of Authoring Tools},
author = {H. Chad Lane and Mark G. Core and Benjamin S. Goldberg},
url = {http://ict.usc.edu/pubs/Lowering%20the%20Technical%20Skill%20Requirements%20for%20Building%20Intelligent%20Tutors-A%20Review%20of%20Authoring%20Tools.pdf},
year = {2015},
date = {2015-06-01},
booktitle = {Design Recommendations for Intelligent Tutoring Systems},
volume = {3},
pages = {303 – 318},
publisher = {U.S. Army Research Laboratory},
abstract = {In this chapter, we focus on intelligent tutoring systems (ITSs), an instance of educational technology that is often criticized for not reaching its full potential (Nye, 2013). Researchers have debated why, given such strong empirical evidence in their favor (Anderson, Corbett, Koedinger & Pelletier, 1995; D’Mello & Graesser, 2012; VanLehn et al., 2005; Woolf, 2009), intelligent tutors are not in every classroom, on every device, providing educators with fine-grained assessment information about their students. Although many factors contribute to a lack of adoption (Nye, 2014), one widely agreed upon reason behind slow adoption and poor scalability of ITSs is that the engineering demands are simply too great. This is no surprise given that the effectiveness of ITSs is often attributable to the use of rich knowledge representations and cognitively plausible models of domain knowledge (Mark & Greer, 1995; Valerie J. Shute & Psotka, 1996; VanLehn, 2006; Woolf, 2009), which are inherently burdensome to build. To put it another way: the features that tend to make ITSs effective are also the hardest to build. The heavy reliance on cognitive scientists and artificial intelligence (AI) software engineers seems to be a bottleneck.},
keywords = {},
pubstate = {published},
tppubtype = {incollection}
}
In this chapter, we focus on intelligent tutoring systems (ITSs), an instance of educational technology that is often criticized for not reaching its full potential (Nye, 2013). Researchers have debated why, given such strong empirical evidence in their favor (Anderson, Corbett, Koedinger & Pelletier, 1995; D’Mello & Graesser, 2012; VanLehn et al., 2005; Woolf, 2009), intelligent tutors are not in every classroom, on every device, providing educators with fine-grained assessment information about their students. Although many factors contribute to a lack of adoption (Nye, 2014), one widely agreed upon reason behind slow adoption and poor scalability of ITSs is that the engineering demands are simply too great. This is no surprise given that the effectiveness of ITSs is often attributable to the use of rich knowledge representations and cognitively plausible models of domain knowledge (Mark & Greer, 1995; Valerie J. Shute & Psotka, 1996; VanLehn, 2006; Woolf, 2009), which are inherently burdensome to build. To put it another way: the features that tend to make ITSs effective are also the hardest to build. The heavy reliance on cognitive scientists and artificial intelligence (AI) software engineers seems to be a bottleneck.
70.
Nye, Benjamin D.; Goldberg, Ben; Hu, Xiangen
Generalizing the Genres for ITS: Authoring Considerations for Representative Learning Tasks Book Section
In: Sottilare, Robert A.; Graesser, Arthur C.; Hu, Xiangen; Brawner, Keith (Ed.): Design Recommendations for Intelligent Tutoring Systems: Volume 2: Authoring Tools and Expert Modeling Techniques, vol. 3, pp. 47–63, U.S. Army Research Laboratory, 2015, ISBN: 978-0-9893923-7-2.
@incollection{nye_generalizing_2015,
title = {Generalizing the Genres for ITS: Authoring Considerations for Representative Learning Tasks},
author = {Benjamin D. Nye and Ben Goldberg and Xiangen Hu},
editor = {Robert A. Sottilare and Arthur C. Graesser and Xiangen Hu and Keith Brawner},
url = {http://ict.usc.edu/pubs/Generalizing%20the%20Genres%20for%20ITS%20-%20Authoring%20Considerations%20for%20Representative%20Learning%20Tasks.pdf},
isbn = {978-0-9893923-7-2},
year = {2015},
date = {2015-06-01},
booktitle = {Design Recommendations for Intelligent Tutoring Systems: Volume 2: Authoring Tools and Expert Modeling Techniques},
volume = {3},
pages = {47–63},
publisher = {U.S. Army Research Laboratory},
abstract = {Compared to many other learning technologies, intelligent tutoring systems (ITSs) have a distinct challenge: authoring an adaptive inner loop that provides pedagogical support on one or more learning tasks. This coupling of tutoring behavior to student interaction with a learning task means that authoring tools need to reflect both the learning task and the ITS pedagogy. To explore this issue, common learning activities in intelligent tutoring need to be categorized and analyzed for the information that is required to tutor each task. The types of learning activities considered cover a large range: step-by-step problem solving, bug repair, building generative functions (e.g., computer code), structured argumentation, self-reflection, short question answering, essay writing, classification, semantic matching, representation mapping (e.g., graph to equation), concept map revision, choice scenarios, simulated process scenarios, motor skills practice, collaborative discussion, collaborative design, and team coordination tasks. These different tasks imply a need for different authoring tools and processes used to create tutoring systems for each task. In this chapter, we consider three facets of authoring: 1) the minimum information required to create the task, 2) the minimum information needed to implement common pedagogical strategies, 3) the expertise required for each type of information. The goal of this analysis is to present a roadmap of effective practices in authoring tool interfaces for each tutoring task considered. A long-term vision for ITSs is to have generalizable authoring tools, which could be used to rapidly create content for a variety of ITSs. However, it is as-yet unclear if this goal is even attainable. Authoring tools have a number of serious challenges, from the standpoint of generalizability. These challenges include the domain, the data format, and the author. First, different ITS domains require different sets of authoring tools, because they have different learning tasks. Tools that are convenient for embedding tutoring in a 3D virtual world are completely different than ones that make it convenient to add tutoring to a system for practicing essay-writing, for example. Second, the data produced by an authoring tool needs to be consumed by an ITS that will make pedagogical decisions. As such, at least some of the data is specific to the pedagogy of the ITS, rather than directly reflecting domain content. As a simple example, if an ITS uses text hints, those hints need to be authored, but some systems may just highlight errors rather than providing text hints. As such, the first system actually needs more content authored and represented as data. With that said, typical ITSs use a relatively small and uniform set of authored content to interact with learners, such as correctness feedback, corrections, and hints (VanLehn, 2006). Third, different authors may need different tools (Nye, Rahman, Yang, Hays, Cai, Graesser, & Hu, 2014). This means that even the same content may need distinct authoring tools that match the expertise of different authors. In this chapter, we are focusing primarily on the first challenge: differences in domains. In particular, our stance is that the “content domain” is too coarse-grained to allow much reuse between authoring tools. This is because, to a significant extent, content domains are simply names for related content. However, the skills and pedagogy for the same domain can vary drastically across different topics and expertise levels. For example, Algebra and Geometry are both high-school level math domains. However, in geometry, graphical depictions (e.g., shapes, angles) are a central aspect of the pedagogy, while Algebra tends to use graphics very differently (e.g., coordinate plots). As such, some learning tasks tend to be shared between those subdomains (e.g., equation-solving) and other tasks are not (e.g., classifying shapes). This raises the central point of our paper: the learning tasks for a domain define how we author content for that domain. For example, while Algebra does not involve recognizing many shapes, understanding the elements of architecture involves recognizing a variety of basic and advanced shapes and forms. In total, this means that no single whole-cloth authoring tool will work well for any pair of Algebra, Geometry, and Architectural Forms. However, it also implies that a reasonable number of task-specific tools for each learning task might allow authoring for all three domains. To do this, we need to understand the common learning tasks for domains taught using ITS, and why those tasks are applied to those domains. In the following sections, we identify and categorize common learning tasks for different ITS domains. Then, we extract common principles for those learning tasks. Finally, we suggest a set of general learning activities that might be used to tutor a large number of domains.},
keywords = {},
pubstate = {published},
tppubtype = {incollection}
}
Compared to many other learning technologies, intelligent tutoring systems (ITSs) have a distinct challenge: authoring an adaptive inner loop that provides pedagogical support on one or more learning tasks. This coupling of tutoring behavior to student interaction with a learning task means that authoring tools need to reflect both the learning task and the ITS pedagogy. To explore this issue, common learning activities in intelligent tutoring need to be categorized and analyzed for the information that is required to tutor each task. The types of learning activities considered cover a large range: step-by-step problem solving, bug repair, building generative functions (e.g., computer code), structured argumentation, self-reflection, short question answering, essay writing, classification, semantic matching, representation mapping (e.g., graph to equation), concept map revision, choice scenarios, simulated process scenarios, motor skills practice, collaborative discussion, collaborative design, and team coordination tasks. These different tasks imply a need for different authoring tools and processes used to create tutoring systems for each task. In this chapter, we consider three facets of authoring: 1) the minimum information required to create the task, 2) the minimum information needed to implement common pedagogical strategies, 3) the expertise required for each type of information. The goal of this analysis is to present a roadmap of effective practices in authoring tool interfaces for each tutoring task considered. A long-term vision for ITSs is to have generalizable authoring tools, which could be used to rapidly create content for a variety of ITSs. However, it is as-yet unclear if this goal is even attainable. Authoring tools have a number of serious challenges, from the standpoint of generalizability. These challenges include the domain, the data format, and the author. First, different ITS domains require different sets of authoring tools, because they have different learning tasks. Tools that are convenient for embedding tutoring in a 3D virtual world are completely different than ones that make it convenient to add tutoring to a system for practicing essay-writing, for example. Second, the data produced by an authoring tool needs to be consumed by an ITS that will make pedagogical decisions. As such, at least some of the data is specific to the pedagogy of the ITS, rather than directly reflecting domain content. As a simple example, if an ITS uses text hints, those hints need to be authored, but some systems may just highlight errors rather than providing text hints. As such, the first system actually needs more content authored and represented as data. With that said, typical ITSs use a relatively small and uniform set of authored content to interact with learners, such as correctness feedback, corrections, and hints (VanLehn, 2006). Third, different authors may need different tools (Nye, Rahman, Yang, Hays, Cai, Graesser, & Hu, 2014). This means that even the same content may need distinct authoring tools that match the expertise of different authors. In this chapter, we are focusing primarily on the first challenge: differences in domains. In particular, our stance is that the “content domain” is too coarse-grained to allow much reuse between authoring tools. This is because, to a significant extent, content domains are simply names for related content. However, the skills and pedagogy for the same domain can vary drastically across different topics and expertise levels. For example, Algebra and Geometry are both high-school level math domains. However, in geometry, graphical depictions (e.g., shapes, angles) are a central aspect of the pedagogy, while Algebra tends to use graphics very differently (e.g., coordinate plots). As such, some learning tasks tend to be shared between those subdomains (e.g., equation-solving) and other tasks are not (e.g., classifying shapes). This raises the central point of our paper: the learning tasks for a domain define how we author content for that domain. For example, while Algebra does not involve recognizing many shapes, understanding the elements of architecture involves recognizing a variety of basic and advanced shapes and forms. In total, this means that no single whole-cloth authoring tool will work well for any pair of Algebra, Geometry, and Architectural Forms. However, it also implies that a reasonable number of task-specific tools for each learning task might allow authoring for all three domains. To do this, we need to understand the common learning tasks for domains taught using ITS, and why those tasks are applied to those domains. In the following sections, we identify and categorize common learning tasks for different ITS domains. Then, we extract common principles for those learning tasks. Finally, we suggest a set of general learning activities that might be used to tutor a large number of domains.
71.
Nye, Benjamin D.; Hu, Xiangen
A Historical Perspective on Authoring and ITS: Reviewing Some Lessons Learned Book Section
In: Sottilare, Robert A.; Graesser, Arthur C.; Hu, Xiangen; Brawner, Keith (Ed.): Design Recommendations for Intelligent Tutoring Systems: Volume 2: Authoring Tools and Expert Modeling Techniques, pp. 67–70, U.S. Army Research Laboratory, 2015, ISBN: 978-0-9893923-7-2.
@incollection{nye_historical_2015,
title = {A Historical Perspective on Authoring and ITS: Reviewing Some Lessons Learned},
author = {Benjamin D. Nye and Xiangen Hu},
editor = {Robert A. Sottilare and Arthur C. Graesser and Xiangen Hu and Keith Brawner},
url = {http://ict.usc.edu/pubs/A%20Historical%20Perspective%20on%20Authoring%20and%20ITS%20-%20Reviewing%20Some%20Lessons%20Learned.pdf},
isbn = {978-0-9893923-7-2},
year = {2015},
date = {2015-06-01},
booktitle = {Design Recommendations for Intelligent Tutoring Systems: Volume 2: Authoring Tools and Expert Modeling Techniques},
pages = {67–70},
publisher = {U.S. Army Research Laboratory},
abstract = {This section discusses the practices and lessons learned from authoring tools that have been applied and revised through repeated use by researchers, content authors, and/or instructors. All of the tools noted in this section represent relatively mature applications that can be used to build and configure educationally-effective content. Each tool has been tailored to address both the tutoring content and the expected authors who will be using the tool. As such, even tools which support similar tutoring strategies may use very different interfaces to represent equivalent domain knowledge. In some cases, authoring tools even represent offshoots where different authoring goals led to divergent evolution of both the authoring tools and the intelligent tutoring systems (ITSs) from a common lineage. Understanding how these systems adapted their tools to their particular authoring challenges gives concrete examples of the tradeoffs involved for different types of authoring. By reviewing the successes and challenges of the past, these chapters provide lessons learned for the development of future systems.},
keywords = {},
pubstate = {published},
tppubtype = {incollection}
}
This section discusses the practices and lessons learned from authoring tools that have been applied and revised through repeated use by researchers, content authors, and/or instructors. All of the tools noted in this section represent relatively mature applications that can be used to build and configure educationally-effective content. Each tool has been tailored to address both the tutoring content and the expected authors who will be using the tool. As such, even tools which support similar tutoring strategies may use very different interfaces to represent equivalent domain knowledge. In some cases, authoring tools even represent offshoots where different authoring goals led to divergent evolution of both the authoring tools and the intelligent tutoring systems (ITSs) from a common lineage. Understanding how these systems adapted their tools to their particular authoring challenges gives concrete examples of the tradeoffs involved for different types of authoring. By reviewing the successes and challenges of the past, these chapters provide lessons learned for the development of future systems.
72.
Lucas, Gale M.; McCubbins, Mathew D.; Turner, Mark
Against Game Theory Book Section
In: Emerging Trends in the Social and Behavioral Sciences: An Interdisciplinary, Searchable, and Linkable Resource, pp. 1–16, John Wiley & Sons, Inc., Hoboken, NJ, 2015, ISBN: 978-1-118-90077-2.
@incollection{lucas_against_2015,
title = {Against Game Theory},
author = {Gale M. Lucas and Mathew D. McCubbins and Mark Turner},
url = {http://ict.usc.edu/pubs/Against%20GameTheory.pdf},
isbn = {978-1-118-90077-2},
year = {2015},
date = {2015-05-01},
booktitle = {Emerging Trends in the Social and Behavioral Sciences: An Interdisciplinary, Searchable, and Linkable Resource},
pages = {1–16},
publisher = {John Wiley & Sons, Inc.},
address = {Hoboken, NJ},
abstract = {People make choices. Often, the outcome depends on choices other people make. What mental steps do people go through when making such choices? Game theory, the most influential model of choice in economics and the social sciences, offers an answer, one based on games of strategy like chess and checkers: the chooser considers the choices that others will make and makes a choice that will lead to a better outcome for the chooser, given all those choices by other people. It is universally established in the social sciences that classical game theory (even when heavily modified) is bad at predicting behavior. But instead of abandoning classical game theory, those in the social sciences have mounted a rescue operation under the name of “behavioral game theory.” Its main tool is to propose systematic deviations from the predictions of game theory, deviations that arise from character type, for example. Other deviations purportedly come from cognitive overload or limitations. The fundamental idea of behavioral game theory is that, if we know the deviations, then we can correct our predictions accordingly, and so get it right. There are two problems with this rescue operation, each of them fatal. (1) For a chooser, contemplating the range of possible deviations, as there are many dozens, actually makes it exponentially harder to figure out a path to an outcome. This makes the theoretical models useless for modeling human thought or human behavior in general. (2) Modeling deviations is helpful only if the deviations are consistent, so that scientists (and indeed decision-makers) can make 2 predictions about future choices on the basis of past choices. But the deviations are not consistent. In general, deviations from classical models are not consistent for any individual from one task to the next or between individuals for the same task. In addition, people’s beliefs are in general not consistent with their choices. Accordingly, all hope is hollow that we can construct a general behavioral game theory. What can replace it? We survey some of the emerging candidates.},
keywords = {},
pubstate = {published},
tppubtype = {incollection}
}
People make choices. Often, the outcome depends on choices other people make. What mental steps do people go through when making such choices? Game theory, the most influential model of choice in economics and the social sciences, offers an answer, one based on games of strategy like chess and checkers: the chooser considers the choices that others will make and makes a choice that will lead to a better outcome for the chooser, given all those choices by other people. It is universally established in the social sciences that classical game theory (even when heavily modified) is bad at predicting behavior. But instead of abandoning classical game theory, those in the social sciences have mounted a rescue operation under the name of “behavioral game theory.” Its main tool is to propose systematic deviations from the predictions of game theory, deviations that arise from character type, for example. Other deviations purportedly come from cognitive overload or limitations. The fundamental idea of behavioral game theory is that, if we know the deviations, then we can correct our predictions accordingly, and so get it right. There are two problems with this rescue operation, each of them fatal. (1) For a chooser, contemplating the range of possible deviations, as there are many dozens, actually makes it exponentially harder to figure out a path to an outcome. This makes the theoretical models useless for modeling human thought or human behavior in general. (2) Modeling deviations is helpful only if the deviations are consistent, so that scientists (and indeed decision-makers) can make 2 predictions about future choices on the basis of past choices. But the deviations are not consistent. In general, deviations from classical models are not consistent for any individual from one task to the next or between individuals for the same task. In addition, people’s beliefs are in general not consistent with their choices. Accordingly, all hope is hollow that we can construct a general behavioral game theory. What can replace it? We survey some of the emerging candidates.
73.
Reger, Greg M.; Rizzo, Albert A.; Gahm, Gregory A.
Initial Development and Dissemination of Virtual Reality Exposure Therapy for Combat-Related PTSD Book Section
In: Safir, Marilyn P.; Wallach, Helene S.; Rizzo, Albert "Skip" (Ed.): Future Directions in Post-Traumatic Stress Disorder, pp. 289–302, Springer US, Boston, MA, 2014, ISBN: 978-1-4899-7521-8 978-1-4899-7522-5.
@incollection{reger_initial_2014,
title = {Initial Development and Dissemination of Virtual Reality Exposure Therapy for Combat-Related PTSD},
author = {Greg M. Reger and Albert A. Rizzo and Gregory A. Gahm},
editor = {Marilyn P. Safir and Helene S. Wallach and Albert "Skip" Rizzo},
url = {http://link.springer.com/10.1007/978-1-4899-7522-5_15},
isbn = {978-1-4899-7521-8 978-1-4899-7522-5},
year = {2014},
date = {2014-11-01},
booktitle = {Future Directions in Post-Traumatic Stress Disorder},
pages = {289–302},
publisher = {Springer US},
address = {Boston, MA},
abstract = {Military personnel are at risk for the development of posttraumatic stress disorder. Although effective treatments are available, the need for improved treatment efficacy and less stigmatizing approaches to treatment have resulted in the evolution of virtual reality exposure therapy. This chapter reviews the development and dissemination efforts of a virtual reality system supporting exposure therapy for deployment-related posttraumatic stress disorder. Specifically, the chapter will review the work done to incorporate the feedback of military personnel into the early development of a Virtual Iraq/Afghanistan system and also reviews efforts to disseminate this treatment to military and Veteran behavioral health researchers and providers.},
keywords = {},
pubstate = {published},
tppubtype = {incollection}
}
Military personnel are at risk for the development of posttraumatic stress disorder. Although effective treatments are available, the need for improved treatment efficacy and less stigmatizing approaches to treatment have resulted in the evolution of virtual reality exposure therapy. This chapter reviews the development and dissemination efforts of a virtual reality system supporting exposure therapy for deployment-related posttraumatic stress disorder. Specifically, the chapter will review the work done to incorporate the feedback of military personnel into the early development of a Virtual Iraq/Afghanistan system and also reviews efforts to disseminate this treatment to military and Veteran behavioral health researchers and providers.
74.
Baltrušaitis, Tadas; Robinson, Peter; Morency, Louis-Philippe
Continuous Conditional Neural Fields for Structured Regression Book Section
In: Computer Vision–ECCV 2014, pp. 593–608, Springer, 2014.
@incollection{baltrusaitis_continuous_2014,
title = {Continuous Conditional Neural Fields for Structured Regression},
author = {Tadas Baltrušaitis and Peter Robinson and Louis-Philippe Morency},
url = {http://ict.usc.edu/pubs/Continuous%20Conditional%20Neural%20Fields%20for%20Structured%20Regression.pdf},
year = {2014},
date = {2014-09-01},
booktitle = {Computer Vision–ECCV 2014},
pages = {593–608},
publisher = {Springer},
abstract = {An increasing number of computer vision and pattern recognition problems require structured regression techniques. Problems like human pose estimation, unsegmented action recognition, emotion prediction and facial landmark detection have temporal or spatial output dependencies that regular regression techniques do not capture. In this paper we present continuous conditional neural fields (CCNF) textbackslashtextbackslashvphantom a novel structured regression model that can learn non-linear input-output dependencies, and model temporal and spatial output relationships of vary- ing length sequences. We propose two instances of our CCNF framework: Chain-CCNF for time series modelling, and Grid-CCNF for spatial relationship modelling. We evaluate our model on five public datasets spanning three different regression problems: facial landmark detection in the wild, emotion prediction in music and facial action unit recognition. Our CCNF model demonstrates state-of-the-art performance on all of the datasets used.},
keywords = {},
pubstate = {published},
tppubtype = {incollection}
}
An increasing number of computer vision and pattern recognition problems require structured regression techniques. Problems like human pose estimation, unsegmented action recognition, emotion prediction and facial landmark detection have temporal or spatial output dependencies that regular regression techniques do not capture. In this paper we present continuous conditional neural fields (CCNF) textbackslashtextbackslashvphantom a novel structured regression model that can learn non-linear input-output dependencies, and model temporal and spatial output relationships of vary- ing length sequences. We propose two instances of our CCNF framework: Chain-CCNF for time series modelling, and Grid-CCNF for spatial relationship modelling. We evaluate our model on five public datasets spanning three different regression problems: facial landmark detection in the wild, emotion prediction in music and facial action unit recognition. Our CCNF model demonstrates state-of-the-art performance on all of the datasets used.
75.
Feng, Andrew; Shapiro, Ari; Lhommet, Margaux; Marsella, Stacy
Embodied Autonomous Agents Book Section
In: Handbook of Virtual Environments: Design, Implementation, and Applications, pp. 335–352, 2014.
@incollection{feng_embodied_2014,
title = {Embodied Autonomous Agents},
author = {Andrew Feng and Ari Shapiro and Margaux Lhommet and Stacy Marsella},
url = {http://books.google.com/books?hl=en&lr=&id=7zzSBQAAQBAJ&oi=fnd&pg=PP1&dq=+Handbook+of+Virtual+Environments&ots=Vx3ia0S2Uu&sig=LaVbSdoG3FahlbVYbuCxLmKgFIA#v=onepage&q=Handbook%20of%20Virtual%20Environments&f=false},
year = {2014},
date = {2014-09-01},
booktitle = {Handbook of Virtual Environments: Design, Implementation, and Applications},
pages = {335–352},
abstract = {Since the last decade, virtual environments have been extensively used for a wide range of application, from training systems to video games. Virtual humans are animated characters that are designed to populate these environments and to interact with the objects of the world as well as with the user. A virtual agent must perceive the world in which it exists, reason about those perceptions, and decide on how to act on them in pursuit of its own agenda.},
keywords = {},
pubstate = {published},
tppubtype = {incollection}
}
Since the last decade, virtual environments have been extensively used for a wide range of application, from training systems to video games. Virtual humans are animated characters that are designed to populate these environments and to interact with the objects of the world as well as with the user. A virtual agent must perceive the world in which it exists, reason about those perceptions, and decide on how to act on them in pursuit of its own agenda.
76.
DeVault, David; Stone, Matthew
Pursuing and Demonstrating Understanding in Dialogue Book Section
In: Natural Language Generation in Interactive Systems, pp. 34–62, Cambridge University Press, 2014.
@incollection{devault_pursuing_2014,
title = {Pursuing and Demonstrating Understanding in Dialogue},
author = {David DeVault and Matthew Stone},
url = {http://www.cs.rutgers.edu/ mdstone/pubs/dialogue11.pdf},
year = {2014},
date = {2014-07-01},
booktitle = {Natural Language Generation in Interactive Systems},
pages = {34–62},
publisher = {Cambridge University Press},
abstract = {The appeal of dialogue as an interface modality is its ability to support open-nded mixed-initiative interaction. Many systems o⬚er rich and extensive capabilities, but must support infrequent and untrained users. In such cases, it's unreasonable to expect users to know the actions they need in advance, or to be able to specify them using a regimented scheme of commands or menu options. Dialogue o⬚ers the potential for the user to talk through their needs with the system and arrive collaboratively at a feasible solution.},
keywords = {},
pubstate = {published},
tppubtype = {incollection}
}
The appeal of dialogue as an interface modality is its ability to support open-nded mixed-initiative interaction. Many systems o⬚er rich and extensive capabilities, but must support infrequent and untrained users. In such cases, it's unreasonable to expect users to know the actions they need in advance, or to be able to specify them using a regimented scheme of commands or menu options. Dialogue o⬚ers the potential for the user to talk through their needs with the system and arrive collaboratively at a feasible solution.
77.
Core, Mark; Lane, H. Chad; Traum, David
Intelligent Tutoring Support for Learners Interacting with Virtual Humans Book Section
In: Design Recommendations for Intelligent Tutoring Systems, vol. 2, pp. 249 – 257, 2014, ISBN: 978-0-9893923-2-7.
@incollection{core_intelligent_2014,
title = {Intelligent Tutoring Support for Learners Interacting with Virtual Humans},
author = {Mark Core and H. Chad Lane and David Traum},
url = {http://books.google.com/books?hl=en&lr=&id=BNWEBAAAQBAJ&oi=fnd&pg=PR2&dq=+Design+Recommendations+for+Intelligent+Tutoring+Systems,+volume+2&ots=jIk3zyGi4M&sig=qb_hc4KKE3-rMh2mrs8WkxBicG4#v=onepage&q&f=false},
isbn = {978-0-9893923-2-7},
year = {2014},
date = {2014-06-01},
booktitle = {Design Recommendations for Intelligent Tutoring Systems},
volume = {2},
pages = {249 – 257},
keywords = {},
pubstate = {published},
tppubtype = {incollection}
}
78.
McAlinden, Ryan; Pynadath, David V.; Hill, Randall W.
UrbanSim: Using Social Simulation to Train for Stability Operations Book Section
In: Understanding Megacities with the Reconnaissance, Surveillance, and Intelligence Paradigm, 2014.
@incollection{mcalinden_urbansim_2014,
title = {UrbanSim: Using Social Simulation to Train for Stability Operations},
author = {Ryan McAlinden and David V. Pynadath and Randall W. Hill},
url = {http://ict.usc.edu/pubs/UrbanSim%20-%20Using%20Social%20Simulation%20to%20Train%20for%20Stability%20Operations.pdf},
year = {2014},
date = {2014-04-01},
booktitle = {Understanding Megacities with the Reconnaissance, Surveillance, and Intelligence Paradigm},
abstract = {As the United States reorients itself towards to a period of reduced military capacity and away from large‐footprint military engagements, there is an imperative to keep commanders and decision‐makers mentally sharp and prepared for the next ‘hot spot.’ One potential hot spot, megacities, presents a unique set of challenges due to their expansive, often interwoven ethnographic landscapes, and their overall lack of understanding by many western experts. Social simulation using agent‐based models is one approach for furthering our understanding of distant societies and their security implications, and for preparing leaders to engage these populations if and when the need arises. Over the past ten years, the field of social simulation has become decidedly cross‐discipline, including academics and practitioners from the fields of sociology, anthropology, psychology, artificial intelligence and engineering. This has led to an unparalleled advancement in social simulation theory and practice, and as new threats evolve to operate within dense but expansive urban environments, social simulation has a unique opportunity to shape our perspectives and develop knowledge that may otherwise be difficult to obtain. This article presents a social simulation‐based training application (UrbanSim) developed by the University of Southern California’s Institute for Creative Technologies (USC‐ICT) in partnership with the US Army’s School for Command Preparation (SCP). UrbanSim has been in‐use since 2009 to help Army commanders understand and train for missions in complex, uncertain environments. The discussion describes how the social simulation‐based training application was designed to develop and hone commanders' skills for conducting missions in environs with multifaceted social, ethnic and political fabrics. We present a few considerations when attempting to recreate dense, rapidly growing population centers, and how the integration of real‐world data into social simulation frameworks can add a level of realism and understanding not possible even a few years ago.},
keywords = {},
pubstate = {published},
tppubtype = {incollection}
}
As the United States reorients itself towards to a period of reduced military capacity and away from large‐footprint military engagements, there is an imperative to keep commanders and decision‐makers mentally sharp and prepared for the next ‘hot spot.’ One potential hot spot, megacities, presents a unique set of challenges due to their expansive, often interwoven ethnographic landscapes, and their overall lack of understanding by many western experts. Social simulation using agent‐based models is one approach for furthering our understanding of distant societies and their security implications, and for preparing leaders to engage these populations if and when the need arises. Over the past ten years, the field of social simulation has become decidedly cross‐discipline, including academics and practitioners from the fields of sociology, anthropology, psychology, artificial intelligence and engineering. This has led to an unparalleled advancement in social simulation theory and practice, and as new threats evolve to operate within dense but expansive urban environments, social simulation has a unique opportunity to shape our perspectives and develop knowledge that may otherwise be difficult to obtain. This article presents a social simulation‐based training application (UrbanSim) developed by the University of Southern California’s Institute for Creative Technologies (USC‐ICT) in partnership with the US Army’s School for Command Preparation (SCP). UrbanSim has been in‐use since 2009 to help Army commanders understand and train for missions in complex, uncertain environments. The discussion describes how the social simulation‐based training application was designed to develop and hone commanders' skills for conducting missions in environs with multifaceted social, ethnic and political fabrics. We present a few considerations when attempting to recreate dense, rapidly growing population centers, and how the integration of real‐world data into social simulation frameworks can add a level of realism and understanding not possible even a few years ago.
79.
Hobbs, Jerry R.; Gordon, Andrew
Axiomatizing Complex Concepts from Fundamentals Book Section
In: Hutchison, David; Kanade, Takeo; Kittler, Josef; Kleinberg, Jon M.; Mattern, Friedemann; Mitchell, John C.; Naor, Moni; Nierstrasz, Oscar; Rangan, C. Pandu; Steffen, Bernhard; Sudan, Madhu; Terzopoulos, Demetri; Tygar, Doug; Vardi, Moshe Y.; Weikum, Gerhard; Gelbukh, Alexander (Ed.): Computational Linguistics and Intelligent Text Processing, pp. 351–365, Springer Berlin Heidelberg, Berlin, Heidelberg, 2014, ISBN: 978-3-642-54905-2 978-3-642-54906-9.
@incollection{hobbs_axiomatizing_2014,
title = {Axiomatizing Complex Concepts from Fundamentals},
author = {Jerry R. Hobbs and Andrew Gordon},
editor = {David Hutchison and Takeo Kanade and Josef Kittler and Jon M. Kleinberg and Friedemann Mattern and John C. Mitchell and Moni Naor and Oscar Nierstrasz and C. Pandu Rangan and Bernhard Steffen and Madhu Sudan and Demetri Terzopoulos and Doug Tygar and Moshe Y. Vardi and Gerhard Weikum and Alexander Gelbukh},
url = {http://link.springer.com/10.1007/978-3-642-54906-9_29},
isbn = {978-3-642-54905-2 978-3-642-54906-9},
year = {2014},
date = {2014-04-01},
booktitle = {Computational Linguistics and Intelligent Text Processing},
pages = {351–365},
publisher = {Springer Berlin Heidelberg},
address = {Berlin, Heidelberg},
abstract = {We have been engaged in the project of encoding commonsense theories of cognition, or how we think we think, in a logical representation. In this paper we use the concept of a “serious threat” as our prime example, and examine the infrastructure required for capturing the meaning of this complex concept. It is one of many examples we could have used, but it is particularly interesting because building up to this concept from fundamentals, such as causality and scalar notions, highlights a number of representational issues that have to be faced along the way, where the complexity of the target concepts strongly influences how we resolve those issues. We first describe our approach to definition, defeasibility, and reification, where hard decisions have to bemade to get the enterprise off the ground.We then sketch our approach to causality, scalar notions, goals, and importance. Finally we use all this to characterize what it is to be a serious threat. All of this is necessarily sketchy, but the key ideas essential to the target concept should be clear.},
keywords = {},
pubstate = {published},
tppubtype = {incollection}
}
We have been engaged in the project of encoding commonsense theories of cognition, or how we think we think, in a logical representation. In this paper we use the concept of a “serious threat” as our prime example, and examine the infrastructure required for capturing the meaning of this complex concept. It is one of many examples we could have used, but it is particularly interesting because building up to this concept from fundamentals, such as causality and scalar notions, highlights a number of representational issues that have to be faced along the way, where the complexity of the target concepts strongly influences how we resolve those issues. We first describe our approach to definition, defeasibility, and reification, where hard decisions have to bemade to get the enterprise off the ground.We then sketch our approach to causality, scalar notions, goals, and importance. Finally we use all this to characterize what it is to be a serious threat. All of this is necessarily sketchy, but the key ideas essential to the target concept should be clear.
80.
Hill, Randall W.
Virtual Reality and Leadership Development Book Section
In: Using Experience to Develop Leadership Talent: How Organizations Leverage On-The-Job Development, pp. 286–312, John Wiley & Sons, Inc., 2014, ISBN: 978-1-118-76783-2.
@incollection{hill_virtual_2014,
title = {Virtual Reality and Leadership Development},
author = {Randall W. Hill},
url = {http://www.amazon.com/dp/1118767837/ref=cm_sw_su_dp},
isbn = {978-1-118-76783-2},
year = {2014},
date = {2014-03-01},
booktitle = {Using Experience to Develop Leadership Talent: How Organizations Leverage On-The-Job Development},
pages = {286–312},
publisher = {John Wiley & Sons, Inc.},
series = {J-B SIOP Professional Practice Series (Book 1)},
keywords = {},
pubstate = {published},
tppubtype = {incollection}
}
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