Implementing a bayesian scheme for revising belief commitments : Lashon B. Booker, Naveen Hota, and Gavin Hemphill Navy Center for Applied Research in AI, Code 5510, Naval Research Laboratory, Washington, DC 20375
- Book ID
- 103921854
- Publisher
- Elsevier Science
- Year
- 1988
- Tongue
- English
- Weight
- 62 KB
- Volume
- 2
- Category
- Article
- ISSN
- 0888-613X
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✦ Synopsis
layer is based on framelike data structures that capture the uncertainty information used in the inference layer and the uncertainty meta-information used in the control layer. The inference layer provides a selection of five T-norm-based uncertainty calculi with which to perform the intersection, detachment, union, and pooling of information. The control layer uses the meta-information to select the appropriate calculus for each context and to resolve eventual ignorance or conflict in the information. This layer also provides a context mechanism that allows the system to focus on the relevant portion of the knowledge base, and an uncertain-belief revision system that incrementally updates the certainty values of well-formed formulas (wff's) in an acyclic directed deduction graph.
RUM has been tested and validated in a sequence of experiments in both naval and aerial situation assessment (SA), consisting of correlating reports and tracks, locating and classifying platforms, and identifying intents and threats. An example of naval situation assessment is illustrated. The testbed environment for developing these experiments has been provided by LOTTA, a symbolic simulator implemented in Zetalisp Flavors. This simulator maintains time-varying situations in a multi-player antagonistic game where players must make decisions in light of uncertain and incomplete data. RUM has been used to assist one of the LOTTA players to perform the SA task.