Open AccessDecision‐Theoretic Planning
Author(s) -
Blythe Jim
Publication year - 1999
Publication title -
ai magazine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.597
H-Index - 79
eISSN - 2371-9621
pISSN - 0738-4602
DOI - 10.1609/aimag.v20i2.1455
Subject(s) - plan (archaeology) , computer science , inference , work (physics) , probabilistic logic , automated planning and scheduling , range (aeronautics) , management science , operations research , influence diagram , risk analysis (engineering) , data science , artificial intelligence , engineering , decision tree , business , mechanical engineering , archaeology , history , aerospace engineering
The recent advances in computer speed and algorithms for probabilistic inference have led to a resurgence of work on planning under uncertainty. The aim is to design AI planners for environments where there might be incomplete or faulty information, where actions might not always have the same results, and where there might be tradeoffs between the different possible outcomes of a plan. Addressing uncertainty in AI, planning algorithms will greatly increase the range of potential applications, but there is plenty of work to be done before we see practical decision‐theoretic planning systems. This article outlines some of the challenges that need to be overcome and surveys some of the recent work in the area.