The recent approaches of extending the GRAPHPLAN algorithm to handle more expressive planning formalisms raise the question of what the formal meaning of ``expressive power'' is. We formalize the intuition that expressive power is a measure of how concisely planning domains and plans can be expressed in a particular formalism by introducing the notion of ``compilation schemes'' between planning formalisms. Using this notion, we analyze the expressiveness of a large family of propositional planning formalisms, ranging from basic STRIPS to a formalism with conditional effects, partial state specifications, and propositional formulae in the preconditions. One of the results is that conditional effects cannot be compiled away if plan size should grow only linearly but can be compiled away if we allow for polynomial growth of the resulting plans. This result confirms that the recently proposed extensions to the GRAPHPLAN algorithm concerning conditional effects are optimal with respect to the ``compilability'' framework. Another result is that general propositional formulae cannot be compiled into conditional effects if the plan size should be preserved linearly. This implies that allowing general propositional formulae in preconditions and effect conditions adds another level of difficulty in generating a plan.

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.

We introduce a temporal model for reasoning on disjunctive metric constraints on intervals and time points in temporal contexts. This temporal model is composed of a labeled temporal algebra and its reasoning algorithms. The labeled temporal algebra defines labeled disjunctive metric point-based constraints, where each disjunct in each input disjunctive constraint is univocally associated to a label. Reasoning algorithms manage labeled constraints, associated label lists, and sets of mutually inconsistent disjuncts. These algorithms guarantee consistency and obtain a minimal network. Additionally, constraints can be organized in a hierarchy of alternative temporal contexts. Therefore, we can reason on context-dependent disjunctive metric constraints on intervals and points. Moreover, the model is able to represent non-binary constraints, such that logical dependencies on disjuncts in constraints can be handled. The computational cost of reasoning algorithms is exponential in accordance with the underlying problem complexity, although some improvements are proposed.

This paper reviews the connections between Graphplan's planning-graph and the dynamic constraint satisfaction problem and motivates the need for adapting CSP search techniques to the Graphplan algorithm. It then describes how explanation based learning, dependency directed backtracking, dynamic variable ordering, forward checking, sticky values and random-restart search strategies can be adapted to Graphplan. Empirical results are provided to demonstrate that these augmentations improve Graphplan's performance significantly (up to 1000x speedups) on several benchmark problems. Special attention is paid to the explanation-based learning and dependency directed backtracking techniques as they are empirically found to be most useful in improving the performance of Graphplan.

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.

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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.