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.

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.

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.

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.

The Council encouraged Science and Engineering Fair, to be sometimes after an appropriate the Conference Committee to gather held May 8-10 in San Jose. Carol asked waiting period agreeable to our copublisher, extensive feedback after the 2002 conference for a volunteer to replace Mel Montemerlo The MIT Press. The Council voted to gauge how well this new as the coordinator of the judging in favor of reaffirming this policy format was received.

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.

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.

The Thirteenth Annual International Conference of the Florida Artificial Intelligence Research Society was held in Orlando, Florida, on 22 to 24 May. The conference included sessions on 11 topics. The session on validation, verification, and system certification was the most extensive. The conference also included panel discussions and invited talks by Subrata Dasgupta, Jim Hendler, and Janet Kolodner.

In this report, I present a summary of the activities that took place during the Fourth International Conference on Autonomous Agents, which took place in Barcelona Spain from 3 to 7 June 2000.

Disaster rescue is one of the most serious social issues that involves very large numbers of heterogeneous agents in the hostile environment. The intention of the RoboCup Rescue project is to promote research and development in this socially significant domain at various levels, involving multiagent teamwork coordination, physical agents for search and rescue, information infrastructures, personal digital assistants, a standard simulator and decision-support systems, evaluation benchmarks for rescue strategies, and robotic systems that are all integrated into a comprehensive system in the future. For this effort, which was built on the success of the RoboCup Soccer project, we will provide forums of technical discussions and competitive evaluations for researchers and practitioners. Although the rescue domain is intuitively appealing as a large-scale multiagent and intelligent system domain, analysis has not yet revealed its domain characteristics. The first research evaluation meeting will be held at RoboCup-2001, in conjunction with the Seventeenth International Joint Conference on Artificial Intelligence (IJCAI-2001), as part of the RoboCup Rescue Simulation League and RoboCup/AAAI Rescue Robot Competition. In this article, we present a detailed analysis of the task domain and elucidate characteristics necessary for multiagent and intelligent systems for this domain. Then, we present an overview of the RoboCup Rescue project.