Planning under risk and Knightian uncertainty

  • Authors:
  • Felipe W. Trevizan;Fábio G. Cozman;Leliane N. De Barros

  • Affiliations:
  • Instituto de Matemática e Estatística, Universidade de São Paulo, São Paulo, SP, Brazil;Escola Politécnica, Universidade de São Paulo, São Paulo, SP, Brazil;Instituto de Matemática e Estatística, Universidade de São Paulo, São Paulo, SP, Brazil

  • Venue:
  • IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
  • Year:
  • 2007

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Abstract

Two noteworthy models of planning in AI are probabilistic planning (based on MDPs and its generalizations) and nondeterministic planning (mainly based on model checking). In this paper we: (1) show that probabilistic and nondeterministic planning are extremes of a rich continuum of problems that deal simultaneously with risk and (Knightian) uncertainty; (2) obtain a unifying model for these problems using imprecise MDPs; (3) derive a simplified Bellman's principle of optimality for our model; and (4) show how to adapt and analyze state-of-art algorithms such as (L)RTDP and LDFS in this unifying setup. We discuss examples and connections to various proposals for planning under (general) uncertainty.