Folsom-Kovarik, J. T. (Soar Technology, Inc.) | Schatz, Sae (MESH Solutions, LLC, a DSCI Company) | Jones, Randolph M. (Soar Technology, Inc.) | Bartlett, Kathleen (MESH Solutions, LLC, a DSCI Company) | Wray, Robert E. (Soar Technology, Inc.)
Jones, Kathleen Bartlett, Robert E. Wray Abstract This article focuses on the problem of patterns of life (POL), which emerge from human social systems. It describes conflicting requirements for this problem and potential AI solutions. AI Challenge Problem: Scalable Models for Patterns of Life Abstract This article focuses on the problem of patterns of life (POL), which emerge from human social systems. It describes conflicting requirements for this problem and potential AI solutions.
Jones, Randolph M., Wray, Robert E.
This article discusses representations and processes for agents and behavior models that integrate large, diverse knowledge stores, are long-lived, and exhibit high degrees of competence and flexibility while interacting with complex environments. There are many different approaches to building such agents, and understanding the important commonalities and differences between approaches is often difficult. We review four agent frameworks, concentrating on the major representations and processes each directly supports. By organizing the approaches according to a common nomenclature, the analysis highlights points of similarity and difference and suggests directions for integrating and unifying disparate approaches and for incorporating research results from one framework into alternatives.
This article describes requirements for synthetic adversaries for urban combat training and a prototype application, MOUTBots. MOUTBots use a commercial computer game to define, implement, and test basic behavior representation requirements and the Soar architecture as the engine for knowledge representation and execution. The article describes how these components aided the development of the prototype and presents an initial evaluation against competence, taskability, fidelity, variability, transparency, and efficiency requirements.
Muslea, Ion, Dignum, Virginia, Corkill, Daniel, Jonker, Catholijn, Dignum, Frank, Coradeschi, Silvia, Saffiotti, Alessandro, Fu, Dan, Orkin, Jeff, Cheetham, William E., Goebel, Kai, Bonissone, Piero, Soh, Leen-Kiat, Jones, Randolph M., Wray, Robert E., Scheutz, Matthias, Farias, Daniela Pucci de, Mannor, Shie, Theocharou, Georgios, Precup, Doina, Mobasher, Bamshad, Anand, Sarabjot Singh, Berendt, Bettina, Hotho, Andreas, Guesgen, Hans, Rosenstein, Michael T., Ghavamzadeh, Mohammad
AAAI presented the AAAI-04 workshop program on July 25-26, 2004 in San Jose, California. This program included twelve workshops covering a wide range of topics in artificial intelligence. The titles of the workshops were as follows: (1) Adaptive Text Extraction and Mining; (2) Agent Organizations: Theory and Practice; (3) Anchoring Symbols to Sensor Data; (4) Challenges in Game AI; (5) Fielding Applications of Artificial Intelligence; (6) Forming and Maintaining Coalitions in Adaptive Multiagent Systems; (7) Intelligent Agent Architectures: Combining the Strengths of Software Engineering and Cognitive Systems; (8) Learning and Planning in Markov Processes -- Advances and Challenges; (9) Semantic Web Personalization; (10) Sensor Networks; (11) Spatial and Temporal Reasoning; and (12) Supervisory Control of Learning and Adaptive Systems.