A hybrid approach to reasoning under uncertainty

摘要: Abstract A complete approach to reasoning under uncertainty requires support for both identification of the appropriate hypothesis space and ranking hypotheses based on available evidence. We present a hybrid scheme that combines symbolic numerical methods management provide efficient effective these tasks. The is techniques adapted from assumption-based truth maintenance systems (ATMS), combined with Dempster/Shafer theory evidence, as extended in Baldwin's Support Logic Programming system. hybridization achieved by viewing an ATMS algebra system calculations. This technique has several major advantages over conventional performing inference certainty estimates addition ability dynamically determine spaces, including improved dependent partially independent faster run-time evaluation propositional certainties, query value proposition multiple perspectives.