Planning under risk and knightian uncertainty

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

Resultado de la investigación: Contribución a una conferenciaArtículo de conferencia

30 Citas (Scopus)


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.
Idioma originalInglés estadounidense
Número de páginas6
EstadoPublicada - 1 dic. 2007
Publicado de forma externa
EventoIJCAI International Joint Conference on Artificial Intelligence -
Duración: 1 dic. 2007 → …


ConferenciaIJCAI International Joint Conference on Artificial Intelligence
Período1/12/07 → …


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