===================== 1994 Spring Symposium Call for Participation ================= Title: Decision-Theoretic Planning ================= Organizing Committee: Steve Hanks, University of Washington (chair) hanks@cs.washington.edu Stuart Russell, University of California, Berkeley russell@cs.berkeley.edu Michael P. Wellman, University of Michigan wellman@engin.umich.edu ===================== Both AI planning and decision theory are devoted to the problem of how an agent chooses a good course of action, based on information about the world and about the agent's capabilities and preferences. The AI community has concentrated on the task of synthesis: given a description of a situation, schematic descriptions of actions with methods for composing them, and some objectives, generate a course of action that furthers those objectives. Classical planning algorithms have for the most part assumed that the agent has perfect information about and control over the world, and that the objectives are described by a symbolic goal state that either is or is not satisfied. These simplifying assumptions conspire to make it difficult to reason about tradeoffs in the planning process, in particular tradeoffs involving the relative likelihood and desirability of possible plan outcomes. Under the classical assumptions, both plan success and plan quality are all-or-nothing propositions. Decision theory provides a language for expressing richer notions of success and quality, along with a normative criterion for making tradeoffs among plans achieving the objectives with varying degrees and likelihoods. Decision theory lacks a computational model of how to generate those plans in the first place, however, and thus does not address the synthesis task. This symposium aims to unify current lines of research in the AI planning community with research in related disciplines---decision analysis, economics, and control theory---by exploring how the richer constructs offered by the decision-theoretic methodology for describing preference and uncertainty can be applied to the problem of plan synthesis. Although AI and decision theory have sometimes been viewed as competing approaches, a growing number of researchers have begun to appreciate their complementary character, and are starting to address the challenge of integrating these ideas. We invite contributions pertaining to all aspects of decision-theoretic planning, particularly those making substantive connections between the two fields. Topics will include, but are not limited to: -- Representing and reasoning about preferences How can AI planning's notion of goals be extended to richer utility models? How do we represent concepts such as partial goal satisfaction, the cost of consuming resources, multiple objectives, and so on? -- Uncertain effects of actions How can causal or action models be extended to take into account uncertainty about the state of the world, the effects of actions, and exogenous events? How do we represent information-gathering actions (e.g. active sensing), and cope with the complexity of conditional planning? -- Model construction How can representations and techniques from the decision sciences and other disciplines---e.g., graphical decision models such as influence diagrams and solution techniques such as policy iteration---be applied to the problem of plan generation? In particular, how can we exploit these technologies without unduly restricting the expressiveness of our representation for actions and their effects? -- Specialized problems and representations For example, what is the relationship between decision-theoretic techniques for path- and motion-planning problems that generally describe their state spaces and operators numerically and the problems involving symbolic state spaces and operators more typically addressed by AI planning research? -- Decision-theoretic meta-level reasoning How can decision-theoretic criteria be applied to the problem of controlling the reasoning of an agent so that it behaves rationally without (necessarily) using decision-theoretic calculations to make its decisions? Possible techniques include the use of decision theory to control the plan-generation process itself, and using the methodology to pre-compile rational behaviors. Various groups in AI, in the decision sciences, in control theory, and in economics have been pursuing research efforts using the concepts and techniques underlying decision-theoretic planning. The efforts have varied in the issues they consider important and in the simplifying assumptions they make. This symposium will provide a forum for exploring these differences, with the aim of distinguishing the fundamental barriers from those merely cultural or terminological. By cross-fertilizing the ideas from several decision disciplines (of which AI planning is one), we will ultimately arrive at a better understanding of the common enterprise. ===================== Submission information We invite extended abstracts (maximum five pages) of two sorts: -- Technical contributions describing progress or addressing issues in decision-theoretic planning. We welcome abstracts reporting on work in progress. -- Position papers describing viewpoints on the enterprise of decision-theoretic planning, or advocating particular approaches. Those wishing to attend but not applying to participate in the symposium's technical or panel presentations should submit a 1-2 page statement of interest indicating the submittor's particular interest in the symposium's topic, specifically addressing how his or her research or experience attacks the problem of reconciling the decision-theoretic and AI planning approaches to problem solving (or would benefit from such a reconciliation). We strongly encourage electronic submissions. Send submissions and questions to dtp-symposium@cs.washington.edu Send paper submissions to Steve Hanks Department of Computer Science & Engineering Room 114 Sieg Hall University of Washington, FR-35 Seattle, WA 98195 206/543-4784