In previous work [6, 9, 10], we advanced a new technique for direct visual matching of images for the purposes of face recognition and image retrieval, using a probabilistic measure of similarity based primarily on a Bayesian (MAP) analysis of image differences, leadingto a "dual" basis similar to eigenfaces [13]. The performance advantage of this probabilistic matching technique over standard Euclidean nearest-neighbor eigenface matching was recently demonstrated using results from DARPA's 1996 "FERET" face recognition competition, in which this probabilistic matching algorithm was found to be the top performer. We have further developed a simple method of replacing the costly compution of nonlinear (online) Bayesian similarity measures by the relatively inexpensive computation of linear (offline) subspace projections and simple (online) Euclidean norms, thus resulting in a significant computational speedup for implementation with very large image databases as typically encountered in real-world applications.

Distributed SIPE (DSIPE) is a distributed planning system that provides decision support to human planners in a collaborative planning environment. The key contributions of our research on DSIPE are (1) constraint-based, consistent local views of the global plan that give each planner a view of how other planners' subplans relate to their local planning decisions; (2) methods for automatically identifying and sharing potentially relevant information among distributed planning agents; and (3) techniques for merging subplans that leverage the shared subplan structure to generate a complete, final plan. DSIPE is a fully implemented system and has been demonstrated to end users in the maritime (United States Navy and United States Marine Corps) planning community.

This article reports on the first phase of the continuous planning and execution framework (CPEF), a system that employs sophisticated plan-generation, -execution, -monitoring, and -repair capabilities to solve complex tasks in unpredictable and dynamic environments. CPEF embraces the philosophy that plans are dynamic, open-ended artifacts that must evolve in response to an ever-changing environment. In particular, plans and activities are updated in response to new information and requirements to ensure that they remain viable and relevant. Users are an integral part of the process, providing input that influences plan generation, repair, and overall system control. CPEF has been applied successfully to generate, execute, and repair complex plans for gaining and maintaining air superiority within a simulated operating environment.

All regular members is available by writing to ncai@aaai.

In this article, I describe several challenges facing the integration of two distinct lines of AI research: (1) decision-theoretic planning (DTP) and (2) multiagent systems. Both areas (especially the second) are attracting considerable interest, but work in multiagent systems often assumes either classical planning models or prespecified economic valuations on the part of the agents in question. By integrating models of DTP in multiagent systems research, more sophisticated multiagent planning scenarios can be accommodated, at the same time explaining precisely how agents determine their valuations for different sources or activities. I discuss several research challenges that emerge from this integration, involving the development of coordination protocols, the reasoning about lack of coordination, and the predicting of behavior in markets. I also briefly mention some opportunities afforded planning agents in multiagent settings and how these might be addressed.

Some application domains highlight the importance of distributed continual planning concepts; coordinating teams of unmanned ground vehicles in dynamic environments is an example of such a domain. In this article, I illustrate the ideas in, and promises of, distributed continual planning by showing how acquiring and distributing operator intent among multiple semiautonomous vehicles supports ongoing, cooperative mission elaboration and revision.

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.

For many years, research in AI plan generation was governed by a number of strong, simplifying assumptions: The planning agent is omniscient, its actions are deterministic and instantaneous, its goals are fixed and categorical, and its environment is static. More recently, researchers have developed expanded planning algorithms that are not predicated on such assumptions, but changing the way in which plans are formed is only part of what is required when the classical assumptions are abandoned. The demands of dynamic, uncertain environments mean that in addition to being able to form plans -- even probabilistic, uncertain plans -- agents must be able to effectively manage their plans. In this article, which is based on a talk given at the 1998 AAAI Fall Symposium on Distributed, Continual Planning, we first identify reasoning tasks that are involved in plan management, including commitment management, environment monitoring, alternative assessment, plan elaboration, metalevel control, and coordination with other agents. We next survey approaches we have developed to many of these tasks and discuss a plan-management system we are building to ground our theoretical work, by providing us with a platform for integrating our techniques and exploring their value in a realistic problem. Throughout, our discussion is informal and relies on numerous examples; the reader can consult the various papers cited for technical details.

Complex, real-world domains require rethinking traditional approaches to AI planning. Planning and executing the resulting plans in a dynamic environment implies a continual approach in which planning and execution are interleaved, uncertainty in the current and projected world state is recognized and handled appropriately, and replanning can be performed when the situation changes or planned actions fail. Furthermore, complex planning and execution problems may require multiple computational agents and human planners to collaborate on a solution. In this article, we describe a new paradigm for planning in complex, dynamic environments, which we term distributed, continual planning (DCP). We argue that developing DCP systems will be necessary for planning applications to be successful in these environments. We give a historical overview of research leading to the current state of the art in DCP and describe research in distributed and continual planning.

Distributed SIPE (DSIPE) is a distributed planning system that provides decision support to human planners in a collaborative planning environment. The key contributions of our research on DSIPE are (1) constraint-based, consistent local views of the global plan that give each planner a view of how other planners' subplans relate to their local planning decisions; (2) methods for automatically identifying and sharing potentially relevant information among distributed planning agents; and (3) techniques for merging subplans that leverage the shared subplan structure to generate a complete, final plan. DSIPE is a fully implemented system and has been demonstrated to end users in the maritime (United States Navy and United States Marine Corps) planning community.