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.
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.
TACAIR-SOAR is an intelligent, rule-based system that generates believable humanlike behavior for large-scale, distributed military simulations. The system is capable of executing most of the airborne missions that the U.S. military flies in fixed-wing aircraft. It accomplishes its missions by integrating a wide variety of intelligent capabilities, including real-time hierarchical execution of complex goals and plans, communication and coordination with humans and simulated entities, maintenance of situational awareness, and the ability to accept and respond to new orders while in flight. The system is currentl y deployed at the Oceana Naval Air Station WISSARD (what-if simulation system for advanced research and development) Lab and the Air Force Research Laboratory in Mesa, Arizona.
Interactive simulation environments constitute one of today's promising emerging technologies, with applications in areas such as education, manufacturing, entertainment, and training. These environments are also rich domains for building and investigating intelligent automated agents, with requirements for the integration of a variety of agent capabilities but without the costs and demands of low-level perceptual processing or robotic control. Our current target is intelligent automated pilots for battlefield-simulation environments. This article provides an overview of this domain and project by analyzing the challenges that automated pilots face in battlefield simulations, describing how TacAir-Soar is successfully able to address many of them -- TacAir-Soar pilots have already successfully participated in constrained air-combat simulations against expert human pilots -- and discussing the issues involved in resolving the remaining research challenges.