Goto

Collaborating Authors

AAAI 2002 Workshops

AI Magazine

The Association for the Advancement of Artificial Intelligence (AAAI) presented the AAAI-02 Workshop Program on Sunday and Monday, 28-29 July 2002 at the Shaw Convention Center in Edmonton, Alberta, Canada. The AAAI-02 workshop program included 18 workshops covering a wide range of topics in AI. The workshops were Agent-Based Technologies for B2B Electronic-Commerce; Automation as a Caregiver: The Role of Intelligent Technology in Elder Care; Autonomy, Delegation, and Control: From Interagent to Groups; Coalition Formation in Dynamic Multiagent Environments; Cognitive Robotics; Game-Theoretic and Decision-Theoretic Agents; Intelligent Service Integration; Intelligent Situation-Aware Media and Presentations; Meaning Negotiation; Multiagent Modeling and Simulation of Economic Systems; Ontologies and the Semantic Web; Planning with and for Multiagent Systems; Preferences in AI and CP: Symbolic Approaches; Probabilistic Approaches in Search; Real-Time Decision Support and Diagnosis Systems; Semantic Web Meets Language Resources; and Spatial and Temporal Reasoning.


1576

AI Magazine

The AAAI-02 workshop program included 18 workshops covering a wide range of topics in AI. The American Association for Artificial Intelligence (AAAI) presented the AAAI-02 Workshop Program on Sunday and Monday, 28-29 July 2002 at the Shaw Convention Center in Edmonton, Alberta, Canada. The AAAI-02 workshop program included 18 workshops covering a wide range of topics in AI. The workshops were Agent-Based Technologies for B2B Electronic-Commerce; Automation as a Caregiver: The Role of Intelligent Technology in Elder Care; Autonomy, Delegation, and Control: From Interagent to Groups; Coalition Formation in Dynamic Multiagent Environments; Cognitive Robotics; Game-Theoretic and Decision-Theoretic Agents; Intelligent Service Integration; Intelligent Situation-Aware Media and Presentations; Meaning Negotiation; Multiagent Modeling and Simulation of Economic Systems; Ontologies and the Semantic Web; Planning with and for Multiagent Systems; Preferences in AI and CP: Symbolic Approaches; Probabilistic Approaches in Search; Real-Time Decision Support and Diagnosis Systems; Semantic Web Meets Language Resources; and Spatial and Temporal Reasoning. Spatial and Temporal Reasoning Technical reports containing the papers of these workshops are available from AAAI Press.


Integrating Reinforcement Learning into a Programming Language

AAAI Conferences

Creating artificial intelligent agents that are high-fidelity simulations of natural agents will require the engagement of behavioral scientists. However, agent programming systems that are accessible to behavioral scientists are too limited to create rich agents, and systems for creating rich agents are accessible mainly to computer scientists, not behavioral scientists. We are solving this problem by engaging behavioral scientists in the design of a programming language, and integrating reinforcement learning into the programming language. This strategy will help our language achieve adaptivity, modularity, and, most importantly, accessibility to behavioral scientists. In addition to allowing behavioral scientist to write rich agent programs, our language — AFABL (A Friendly Behavior Language) — will enable a true discipline of modular agent software engineering with broad implications for games, interactive storytelling, and social simulations.


Real-Time Knowledge-Based Systems

AI Magazine

Real-time domains present a new and challenging environment for the application of knowledge-based problem-solving techniques. However, a substantial amount of research is still needed to solve many difficult problems before realtime expert systems can enhance current monitoring and control systems. In this article, we examine how the real-time problem domain is significantly different from those domains which have traditionally been solved by expert systems. We conduct a survey on the current state of the art in applying knowledge-based systems to real-time problems and describe the key issues that are pertinent in a real-time domain. The survey is divided into three areas: applications, tools, and theoretic issues.


Real-Time Knowledge-Based Systems

AI Magazine

Real-time domains present a new and challenging environment for the application of knowledge-based problem-solving techniques. However, a substantial amount of research is still needed to solve many difficult problems before real-time expert systems can enhance current monitoring and control systems. In this article, we examine how the real-time problem domain is significantly different from those domains which have traditionally been solved by expert systems. We conduct a survey on the current state of the art in applying knowledge-based systems to real-time problems and describe the key issues that are pertinent in a real-time domain. The survey is divided into three areas: applications, tools, and theoretic issues. From the results of the survey, we identify a set of real-time research issues that have yet to be solved and point out limitations of current tools for real-time problems. Finally, we propose a set of requirements that a real-time knowledge-based system must satisfy.