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1994 Spring Symposium Call for Participation
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Title: Decision-Theoretic Planning
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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
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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.
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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