The Fourteenth International Conference on Automated Planning and Scheduling (ICAPS-04) was held in Canada in June of 2004. It covered the latest theoretical and empirical advances in planning and scheduling. The conference program consisted of tutorials, workshops, a doctoral consortium, and three days of technical paper presentations in a single plenary track, one day of which was jointly organized with the Ninth International Conference on Principles of Knowledge Representation and Reasoning. ICAPS-04 also hosted the International Planning Competition, including a classical track and a newly formed probabilistic track.
The Association for the Advancement of Artificial Intelligence, in cooperation, with Stanford University's Department of Com-puter Science, presented the 1999 Spring Symposium Series on 22 to 24 March 1999 at Stanford University. The titles of the seven symposia were (1) Agents with Adjustable Autonomy, (2) Artificial Intelligence and Computer Games, (3) Artificial Intelligence in Equipment Maintenance Service and Support, (4) Hybrid Systems and AI: Modeling, Analysis, and Control of Discrete Continuous Systems, (5) Intelligent Agents in Cyberspace, (6) Predictive Toxicology of Chemicals: Experiences and Impact of AI Tools, and (7) Search Techniques for Problem Solving under Uncertainty and Incomplete Information.
The Association for the Advancement of Artificial Intelligence (AAAI) held its 1996 Fall Symposia Series on 9 to 11 November in Cambridge, Massachusetts. This article contains summaries of the seven symposia that were conducted: (1) Configuration; (2) Developing Assistive Technology for People with Disabilities; (3) Embodied Cognition and Action; (4) Flexible Computation: Results, Issues, and Opportunities; (5) Knowledge Representation Systems Based on Natural Language; (6) Learning Complex Behaviors in Adaptive Intelligent Systems; and (7) Plan Execution: Problems and Issues.
Anytime algorithms give intelligent systems the capability to trade deliberation time for quality of results. What characterizes these domains is that it is not feasible (computationally) or desirable (economically) to compute the optimal answer. This article surveys the main control problems that arise when a system is composed of several anytime algorithms. After a brief introduction to anytime computation, I outline a wide range of existing solutions to the metalevel control problem and describe current work that is aimed at increasing the applicability of anytime computation.