This article gives an overview of current research on animated pedagogical agents at the Center for Advanced Research in Technology for Education (CARTE) at the University of Southern California/Information Sciences Institute. Animated pedagogical agents, nicknamed guidebots, interact with learners to help keep learning activities on track. They combine the pedagogical expertise of intelligent tutoring systems with the interpersonal interaction capabilities of embodied conversational characters. They can support the acquisition of team skills as well as skills performed alone by individuals. At CARTE, we have been developing guidebots that help learners acquire a variety of problem-solving skills in virtual worlds, in multimedia environments, and on the web. We are also developing technologies for creating interactive pedagogical dramas populated with guidebots and other autonomous animated characters.

TALplanner is a forward-chaining planner that utilizes domain-dependent knowledge to control search in the state space generated by action invocation. The domain-dependent control knowledge, background knowledge, plans, and goals are all represented using formulas in a temporal logic called tal, which has been developed independently as a formalism for specifying agent narratives and reasoning about them. In the Fifth International Artificial Intelligence Planning and Scheduling Conference planning competition, TALplanner exhibited impressive performance, winning the Outstanding Performance Award in the Domain-Dependent Planning Competition.

TALplanner is a forward-chaining planner that utilizes domain-dependent knowledge to control search in the state space generated by action invocation. The domain-dependent control knowledge, background knowledge, plans, and goals are all represented using formulas in a temporal logic called tal, which has been developed independently as a formalism for specifying agent narratives and reasoning about them. In the Fifth International Artificial Intelligence Planning and Scheduling Conference planning competition, TALplanner exhibited impressive performance, winning the Outstanding Performance Award in the Domain-Dependent Planning Competition. In this article, we provide an overview of TALplanner

The planning competition has become a regular part of the biennial Artificial Intelligence Planning and Scheduling (AIPS) conferences. AIPS'98 featured the very first competition, and for AIPS'00, we built on this foundation to run the second competition. The 2000 competition featured a much larger group of participants and a wide variety of different approaches to planning. Some of these approaches were refinements of known techniques, and others were quite different from anything that had been tried before. Besides the dramatic increase in participation, the 2000 competition demonstrated that planning technology has taken a giant leap forward in performance since 1998. The 2000 competition featured planning systems that were orders of magnitude faster than the planners of just two years prior. This article presents an overview of the competition and reviews the main results.

We present a novel approach to goal recognition based on a two-stage paradigm of graph construction and analysis. First, a graph structure called a Goal Graph is constructed to represent the observed actions, the state of the world, and the achieved goals as well as various connections between these nodes at consecutive time steps. Then, the Goal Graph is analysed at each time step to recognise those partially or fully achieved goals that are consistent with the actions observed so far. The Goal Graph analysis also reveals valid plans for the recognised goals or part of these goals. Our approach to goal recognition does not need a plan library. It does not suffer from the problems in the acquisition and hand-coding of large plan libraries, neither does it have the problems in searching the plan space of exponential size. We describe two algorithms for Goal Graph construction and analysis in this paradigm. These algorithms are both provably sound, polynomial-time, and polynomial-space. The number of goals recognised by our algorithms is usually very small after a sequence of observed actions has been processed. Thus the sequence of observed actions is well explained by the recognised goals with little ambiguity. We have evaluated these algorithms in the UNIX domain, in which excellent performance has been achieved in terms of accuracy, efficiency, and scalability.

In this special issue, we selected six of the papers, including one of the invited talks, and asked the authors to expand their conference presentations to provide more explanatory material. We believe these articles are representative of the current state of the art in innovative applications of AI.

As the title indicates, Multiagent Systems: A Modern Approach to Distributed Artificial Intelligence covers the design and development of multiagent and distributed AI systems. The purpose of this book is to provide a comprehensive overview of the field. It is an excellent collection of closely related papers that provides a wonderful introduction to multiagent systems and distributed AI.

Deployed applications are three-dimensional scenes, speech input Rapid Development of a systems that have been in use for at for information access, multimodal Course-of-Action Critiquer," by Gheorghe least several months by individuals or dialog, machine learning in engineering Tecuci, Mihai Boicu, Mike Bowman, organizations other than their developers, design, ontologies, agent models, and Dorin Marcu, describes a critiquing have measurable benefits, and and case-based reasoning.

First, we introduce the concept of a learning agent shell as a tool to be used directly by a subjectmatter of theories, methods, and tools that expert (SME) to develop an agent. In his invited talk at the 1993 National strategies. In addition, it supported the (MIT), Stanford University, and Conference on Artificial Intelligence, development of methods for rapidly Northwestern University, developed two Edward Feigenbaum compared the technology extracting knowledge from natural language end-to-end integrated systems that were of a knowledge-based computer texts and the World Wide Web evaluated by Information Extraction system with a tiger in a cage. Rarely does and for knowledge acquisition from subject and Transport Inc. (IET), the challenge a technology arise that offers such a matter experts (SMEs). However, emphasis of the HPKB Program was 1999. Both systems demonstrated high this technology is still far from the use of challenge problems, which are performance through knowledge reuse achieving its potential. This tiger is in a complex, innovative military applications and semantic integration and created a cage, and to free it, the AI research community of AI that are intended to focus the significant amount of reusable knowledge.

This paper investigates the problems arising in the construction of a program to play the game of contract bridge. These problems include both the difficulty of solving the game's perfect information variant, and techniques needed to address the fact that bridge is not, in fact, a perfect information game. GIB, the program being described, involves five separate technical advances: partition search, the practical application of Monte Carlo techniques to realistic problems, a focus on achievable sets to solve problems inherent in the Monte Carlo approach, an extension of alpha-beta pruning from total orders to arbitrary distributive lattices, and the use of squeaky wheel optimization to find approximately optimal solutions to cardplay problems. GIB is currently believed to be of approximately expert caliber, and is currently the strongest computer bridge program in the world.