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AAAI

2000

2000

Rational decision making requires full knowledge of the utility function of the person affected by the decisions. However, in many cases, the task of acquiring such knowledge is not feasible due to the size of the outcome space and the complexity of the utility elicitation process. Given that the amount of utility information we can acquire is limited, we need to make decisions with partial utility information and should carefully select which utility elicitation questions we ask. In this paper, we propose a new approach for this problem that utilizes a prior probability distribution over the person's utility function, perhaps learned from a population of similar people. The relevance of a utility elicitation question for the current decision problem can then be measured using its value of information. We propose an algorithm that interleaves the analysis of the decision problem and utility elicitation to allow these two tasks to inform each other. At every step, it asks the util...

Related Content

Added |
01 Nov 2010 |

Updated |
01 Nov 2010 |

Type |
Conference |

Year |
2000 |

Where |
AAAI |

Authors |
Urszula Chajewska, Daphne Koller, Ronald Parr |

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