Value-Driven Information Gathering

Joshua Grass and Shlomo Zilberstein

We describe a decision-theoretic approach to information gathering from a distributed network of information sources. Our approach uses an explicit representation of the user’s decision model in order to plan and execute information gathering actions. The information gathering planner issues requests based on the value of information taking into account the computational resources and monetary costs of information gathering. At any given time, the system assesses the marginal value of dispatching new queries and selects the one with maximal value. When no further improvement of the comprehensive utility function is possible, the system stops gathering information and reports the results. We show that this approach has significant advantages including high performance, interruptibility, and adaptability to dynamic changes in the environment.

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