LCW-Based Agent Planning for the Semantic Web

Jeff Heflin and Hector Munoz-Avila

The Semantic Web has the potential to allow software agents to intelligently process and integrate the Web’s wealth of information. These agents must plan how to achieve their goals in light of the information available. However, because the Web is so vast and changes so rapidly, the agent cannot make a closed-world assumption. This condition makes it difficult for an agent to know when it has gathered all relevant information or when additional searches may be redundant. We propose to use local closed world (LCW) information to assist these agents. LCW information can be obtained by accessing sources that are described in a Semantic Web language with LCW extensions, or by executing operators that provide exhaustive information. In this paper, we demonstrate how two Semantic Web languages (DAML+OIL and SHOE) can be augmented with the ability to state LCW information. We also show that DAML+OIL can represent many kinds of LCW information even without additional language features. Finally, we describe how ordered task decomposition can be used with LCW information to efficiently plan in distributed information environments.

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