Project "Probabilistic Planning with Realistic Preference Models"
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Probabilistic search methods typically either maximize the probability or minimize the expected cost of achieving a given goal. However, human decision makers often have more complex preference models, and planning systems need to adopt these objective functions to be helpful for their users. For example, human decision makers are often risk-averse in high-stake one-shot decision situations, such as planning for crisis situations (such as oil spills) or space applications. While artificial intelligence researchers have concentrated on how to search efficiently for more realistic (and thus larger) world models, we have concentrated on how to exploit the structure of probabilistic planning problems to search efficiently for preference models that allow one to model the preferences of human decision makers better than the currently used ones. For example, we have developed fast dynamic programming methods that maximize the expected utility for Markov decision problems with non-linear utility functions, such as an exact backward induction method for one-switch utility functions and several versions of approximate functional value iteration for arbitrary utility functions. These dynamic programming methods manipulate concisely represented functions from wealth levels to values. Overall, we have developed probabilistic search methods for sensor planning, route planning in the presence of traps (such as steep slopes for outdoor robots), planning with risk-attitudes for high-stake one-shot decision situations, planning for deadlines and planning for other real-valued scarce resources, such as energy.

Representative Publications on High-Stake Planning

Representative Publications on Multi-Attribute Planning

Representative Publications on Hard or Soft Deadlines

Dissertations

Some of this material is based upon work supported by the National Science Foundation under Grant No. 9984827 and an IBM Faculty Fellowship. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation or the other sponsoring organizations.

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This page was automatically created by a bibliography maintenance system that was developed as part of an undergraduate research project, advised by Sven Koenig.


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