Planning under risk and knightian uncertainty

Felipe W. Trevizan, Leliane N. De Barros, Fábio G. Cozman

Research output: Contribution to conferenceConference Paper

30 Scopus citations


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.
Original languageAmerican English
Number of pages6
StatePublished - 1 Dec 2007
Externally publishedYes
EventIJCAI International Joint Conference on Artificial Intelligence -
Duration: 1 Dec 2007 → …


ConferenceIJCAI International Joint Conference on Artificial Intelligence
Period1/12/07 → …


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