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 language||American English|
|Number of pages||6|
|State||Published - 1 Dec 2007|
|Event||IJCAI International Joint Conference on Artificial Intelligence - |
Duration: 1 Dec 2007 → …
|Conference||IJCAI International Joint Conference on Artificial Intelligence|
|Period||1/12/07 → …|