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259
Extending the soar cognitive architecture
- In: Proceedings of the First Conference on Artificial General Intelligence
, 2008
"... Abstract. One approach in pursuit of general intelligent agents has been to concentrate on the underlying cognitive architecture, of which Soar is a prime example. In the past, Soar has relied on a minimal number of architectural modules together with purely symbolic representations of knowledge. Th ..."
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Cited by 100 (23 self)
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Abstract. One approach in pursuit of general intelligent agents has been to concentrate on the underlying cognitive architecture, of which Soar is a prime example. In the past, Soar has relied on a minimal number of architectural modules together with purely symbolic representations of knowledge. This paper presents the cognitive architecture approach to general intelligence and the traditional, symbolic Soar architecture. This is followed by major additions to Soar: nonsymbolic representations, new learning mechanisms, and long-term memories.
Toward Virtual Humans
- AI Magazine
, 2006
"... Abstract This paper describes the virtual humans developed as part of the Mission Rehearsal Exercise project, a virtual reality based training system. This project is an ambitious exercise in integration, both in the sense of integrating technology with entertainment industry content, but also in t ..."
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Cited by 81 (18 self)
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Abstract This paper describes the virtual humans developed as part of the Mission Rehearsal Exercise project, a virtual reality based training system. This project is an ambitious exercise in integration, both in the sense of integrating technology with entertainment industry content, but also in that we have joined a number of component technologies that have not been integrated before. This integration has not only raised new research issues, but it has also suggested some new approaches to difficult problems. We describe the key capabilities of the virtual humans, including task representation and reasoning, natural language dialogue, and emotion reasoning, and show how these capabilities are integrated to provide more human-level intelligence than would otherwise be possible.
Evaluating a computational model of emotion
- Autonomous Agents and Multi-Agent Systems
"... Spurred by a range of potential applications, there has been a growing body of research in computational models of human emotion. To advance the development of these models, it is critical that we evaluate them against the phenomena they purport to model. In this paper, we present one method to eval ..."
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Cited by 63 (5 self)
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Spurred by a range of potential applications, there has been a growing body of research in computational models of human emotion. To advance the development of these models, it is critical that we evaluate them against the phenomena they purport to model. In this paper, we present one method to evaluate an emotion model that compares the behavior of the model against human behavior using a standard clinical instrument for assessing human emotion and coping. We use this method to evaluate the EMA model of emotion [1-3]. The evaluation highlights strengths of the approach and identifies where the model needs further development. 1.
Narrative planning: Balancing plot and character
- Journal of Artificial Intelligence Research
"... Narrative, and in particular storytelling, is an important part of the human experience. Consequently, computational systems that can reason about narrative can be more effective communicators, entertainers, educators, and trainers. One of the central challenges in computational narrative reasoning ..."
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Cited by 61 (21 self)
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Narrative, and in particular storytelling, is an important part of the human experience. Consequently, computational systems that can reason about narrative can be more effective communicators, entertainers, educators, and trainers. One of the central challenges in computational narrative reasoning is narrative generation, the automated creation of meaningful event sequences. There are many factors – logical and aesthetic – that contribute to the success of a narrative artifact. Central to this success is its understandability. We argue that the following two attributes of narratives are universal: (a) the logical causal progression of plot, and (b) character believability. Character believability is the perception by the audience that the actions performed by characters do not negatively impact the audience’s suspension of disbelief. Specifically, characters must be perceived by the audience to be intentional agents. In this article, we explore the use of refinement search as a technique for solving the narrative generation problem – to find a sound and believable sequence of character actions that transforms an initial world state into a world state in which goal propositions hold. We describe a novel refinement search planning algorithm – the Intent-based Partial Order Causal Link (IPOCL) planner – that, in addition to creating causally sound plot progression, reasons about character intentionality by identifying possible character goals that explain their actions and creating plan structures that explain why those characters commit to their goals. We present the results of an empirical evaluation that demonstrates that narrative plans generated by the IPOCL algorithm support audience comprehension of character intentions better than plans generated by conventional partial-order planners. 1.
multi-issue, multi-strategy negotiation for multi-modal virtual agents
- in Proc. of IVA
, 2008
"... Abstract. We present a model of negotiation for virtual agents that extends previous work to be more human-like and applicable to a broader range of situations, including more than two negotiators with different goals, and negotiating over multiple options. The agents can dynamically change their ne ..."
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Cited by 54 (24 self)
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Abstract. We present a model of negotiation for virtual agents that extends previous work to be more human-like and applicable to a broader range of situations, including more than two negotiators with different goals, and negotiating over multiple options. The agents can dynamically change their negotiating strategies based on the current values of several parameters and factors that can be updated in the course of the negotiation. We have implemented this model and done preliminary evaluation within a prototype training system and a three-party negotiation with two virtual humans and one human. 1
EMA: A process model of appraisal dynamics
- Cognitive Systems Research 10(1), 70 – 90
, 2009
"... A computational model of emotion must explain both the rapid dynamics of some emotional reactions as well as the slower responses that follow deliberation. This is often addressed by positing multiple levels of appraisal processes such as fast pattern directed vs. slower deliberative appraisals. In ..."
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Cited by 51 (7 self)
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A computational model of emotion must explain both the rapid dynamics of some emotional reactions as well as the slower responses that follow deliberation. This is often addressed by positing multiple levels of appraisal processes such as fast pattern directed vs. slower deliberative appraisals. In our view, this confuses appraisal with inference. Rather, we argue for a single and automatic appraisal process that operates over a person’s interpretation of their relationship to the environment. Dynamics arise from perceptual and inferential processes operating on this interpretation (including deliberative and reactive processes). This article discusses current developments in a computational model of emotion processes and illustrates how a single-level model of appraisal obviates a multi-level approach within the context of modeling a naturalistic emotional situation. 1
U-director: A decision-theoretic narrative planning architecture for storytelling environments
- In Proceedings of the Fifth International Conference on Autonomous Agents and Multi-Agent Systems
, 2006
"... Recent years have seen significant growth in work on interactive storytelling environments. A key challenge posed by these environments is narrative planning, in which a director agent orchestrates all of the events in a storyworld to create an optimal experience for a user, who is herself an active ..."
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Cited by 46 (12 self)
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Recent years have seen significant growth in work on interactive storytelling environments. A key challenge posed by these environments is narrative planning, in which a director agent orchestrates all of the events in a storyworld to create an optimal experience for a user, who is herself an active participant in the unfolding story. To create effective stories, the director agent must cope with the task’s inherent uncertainty, including uncertainty about the user’s intentions and the absence of a complete theory of narrative. Director agents must be efficient so they can operate in real time. In this paper, we present U-DIRECTOR, a decision-theoretic narrative planning architecture that dynamically models narrative objectives (e.g., plot progress, narrative flow), storyworld state (e.g., plot focus), and user state (e.g., goals, beliefs) with a dynamic decision network that continually selects storyworld actions to maximize narrative utility on an ongoing basis. The U-DIRECTOR architecture has been implemented in a narrative planner for Crystal Island, an interactive storyworld in which users play the role of a medical detective solving a science mystery. Preliminary evaluations suggest that the U-DIRECTOR architecture satisfies the real-time constraints of interactive environments and creates engaging narrative experiences. Categories and Subject Descriptors H.5.1 [Multimedia Information Systems]: Artificial, augmented, and virtual realities.
Teaching negotiation skills through practice and reflection with virtual humans. SIMULATION
- SIMULATION
, 2006
"... Keywords: virtual humans, negotiation training, conversation strategies, emotion modeling, intelligent tutoring systems, explanation systems ..."
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Cited by 46 (8 self)
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Keywords: virtual humans, negotiation training, conversation strategies, emotion modeling, intelligent tutoring systems, explanation systems
Early Prediction of Student Frustration
"... Abstract. Affective reasoning has been the subject of increasing attention in recent years. Because negative affective states such as frustration and anxiety can impede progress toward learning goals, intelligent tutoring systems should be able to detect when a student is anxious or frustrated. Bein ..."
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Cited by 34 (9 self)
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Abstract. Affective reasoning has been the subject of increasing attention in recent years. Because negative affective states such as frustration and anxiety can impede progress toward learning goals, intelligent tutoring systems should be able to detect when a student is anxious or frustrated. Being able to detect negative affective states early, i.e., before they lead students to abandon learning tasks, could permit intelligent tutoring systems sufficient time to adequately prepare for, plan, and enact affective tutorial support strategies. A first step toward this objective is to develop predictive models of student frustration. This paper describes an inductive approach to student frustration detection and reports on an experiment whose results suggest that frustration models can make predictions early and accurately. 1
Evaluating the modeling and use of emotion in virtual humans
- in AAMAS 2004
"... Spurred by a range of potential applications, there has been a growing body of research in computational models of human emotion. To advance the development of these models, it is critical that we evaluate them against the phenomena they purport to model. In this paper, we pre-sent one methodology t ..."
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Cited by 28 (7 self)
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Spurred by a range of potential applications, there has been a growing body of research in computational models of human emotion. To advance the development of these models, it is critical that we evaluate them against the phenomena they purport to model. In this paper, we pre-sent one methodology to evaluate an emotion model that compares the behavior of the model against human be-havior using a standard clinical instrument for assessing human emotion and coping. We use this methodology to evaluate the EMA model of emotion [1, 2]. The evaluation highlights strengths of the approach and identifies where the model needs further development. 1.
