Abstract Actions for Stochastic Planning
Jonathan King Tash
Group in Logic and the Methodology of Science
University of California
Berkeley, California 94720
tash@cs.berkeley.edu
Abstract
This paper presents a method for abstracting action
representation, reducing the computational burden of stochastic planning.
Previous methods of forming abstractions in probabilistic environments have
relied on summarizing clusters of states or actions with worst-case or
average feature values. In contrast to these, the current proposal treats
abstract actions as plans to plan. An underspecified action sequence is
used in abstract plans like the expected consequences of its realization.
This more accurately reflects our natural use of abstract actions, and
improves their utility for planning. An exemplification of this idea is
presented for maze route-finding modeled as a Markov decision process.
Appeared in
1995 AAAI Spring Symposium Technical Report on Extending Theories of
Action: Formal Theory and Practical Applications, pp. 184-187
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