The AAAI-2000 Workshop Program was held Sunday and Monday, 3031 July 2000 at the Hyatt Regency Austin and the Austin Convention Center in Austin, Texas. The 15 workshops held were (1) Agent-Oriented Information Systems, (2) Artificial Intelligence and Music, (3) Artificial Intelligence and Web Search, (4) Constraints and AI Planning, (5) Integration of AI and OR: Techniques for Combinatorial Optimization, (6) Intelligent Lessons Learned Systems, (7) Knowledge-Based Electronic Markets, (8) Learning from Imbalanced Data Sets, (9) Learning Statistical Models from Rela-tional Data, (10) Leveraging Probability and Uncertainty in Computation, (11) Mobile Robotic Competition and Exhibition, (12) New Research Problems for Machine Learning, (13) Parallel and Distributed Search for Reasoning, (14) Representational Issues for Real-World Planning Systems, and (15) Spatial and Temporal Granularity.

The researcher will be numerous international societies and IJCAI-01/IJCAI-03 Update. Planning required to sign a statement of restricted conferences. Barbara Grosz agreed to for IJCAI-01 is well under way.

The Workshop on Intelligent Information Integration (III), organized in conjunction with the Sixteenth International Joint Conference on Artificial Intelligence, was held on 31 July 1999 in Stockholm, Sweden. Approximately 40 people participated, and nearly 20 papers were presented. This packed workshop schedule resulted from a large number of submissions that made it difficult to reserve discussion time without rejecting an unproportionately large number of papers. Participants included scientists and practitioners from industry and academia. Topics included query planning, applications of III, mediator architectures, and the use of ontologies for III.

The AAAI-99 Workshop Program (a part of the sixteenth national conference on artificial intelligence) was held in Orlando, Florida. The program included 16 workshops covering a wide range of topics in AI. Each workshop was limited to approximately 25 to 50 participants. Participation was by invitation from the workshop organizers. The workshops were Agent-Based Systems in the Business Context, Agents' Conflicts, Artificial Intelligence for Distributed Information Networking, Artificial Intelligence for Electronic Commerce, Computation with Neural Systems Workshop, Configuration, Data Mining with Evolutionary Algorithms: Research Directions (Jointly sponsored by GECCO-99), Environmental Decision Support Systems and Artificial Intelligence, Exploring Synergies of Knowledge Management and Case-Based Reasoning, Intelligent Information Systems, Intelligent Software Engineering, Machine Learning for Information Extraction, Mixed-Initiative Intelligence, Negotiation: Settling Conflicts and Identifying Opportunities, Ontology Management, and Reasoning in Context for AI Applications.

This article discusses five years of experience using three international mobile robot competitions as the foundation for educational projects in undergraduate and graduate computer science courses. The three competitions -- (1) AAAI Mobile Robot, (2) AUVS Unmanned Ground Robotics, and (3) IJCAI RoboCup -- were used in different years for an introductory undergraduate robotics course, an advanced graduate robotics course, and an undergraduate practicum course. Based on these experiences, a strategy is presented for incorporating competitions into courses in such a way as to foster intellectual maturation as well as learn lessons in organizing courses and fielding teams. The article also provides a classification of the major robot competitions and discusses the relative merits of each for educational projects, including the expected course level of computer science students, equipment needed, and costs.

Calculation of Q(t) and R(t) using (4, 5, 7, 9) to execute the path average and the average over sets is relatively straightforward, albeit tedious. We find that -"Yt(l -"Yt)

Calculation of Q(t) and R(t) using (4, 5, 7, 9) to execute the path average and the average over sets is relatively straightforward, albeit tedious. We find that -"Yt(l -"Yt)

People often act together with a shared purpose; they collaborate. Collaboration enables them to work more efficiently and to complete activities they could not accomplish individually. An increasing number of computer applications also require collaboration among various systems and people. Thus, a major challenge for AI researchers is to determine how to construct computer systems that are able to act effectively as partners in collaborative activity. Collaborative activity entails participants forming commitments to achieve the goals of the group activity and requires group decision making and group planning procedures. In addition, agents must be committed to supporting the activities of their fellow participants in support of the group activity. Furthermore, when conflicts arise (for example, from resource bounds), participants must weigh their commitments to various group activities against those for individual activities. This article briefly reviews the major features of one model of collaborative planning called SHARED-PLANS (Grosz and Kraus 1999, 1996). It describes several current efforts to develop collaborative planning agents and systems for human-computer communication based on this model. Finally, it discusses empirical research aimed at determining effective commitment strategies in the SHAREDPLANS context.

The Workshop on Logic-Based Artificial Intelligence (LBAI) was held in Washington, D.C., on 13 to 15 June 1999. The workshop was organized by Jack Minker and John McCarthy. Its purpose was to bring together researchers who use logic as a fundamental tool in AI to permit them to review accomplishments, assess future directions, and share their research in LBAI.

On 30 October 1998, Mihaela Sabin and I ran the Constraint Problem Reformulation Workshop in conjunction with the Fourth International Conference on the Principles and Practices of Constraint Programming held in Pisa, Italy. The goals of the workshop were to discuss the nature of constraint problem reformulation and the benefits and difficulties in reformulating constraint problems and to summarize and understand the recent work in this area.