Explaining problem solver answers
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abstract: Knowledge based systems (KBSs) should explain their answers if their usersare expected to understand and thus trust the answers. Problem solvers,KBSs implementing problem solving methods (PSMs), should also explaintheir answers. Few KBS systems, however, are effective at explaining theiranswers either because they cannot systematically generate explanationsor, when they can, their explanation components cannot easily be extendedto new kinds of tasks. In this paper we present an approach enablingproblem solvers to explain their answers in a systematic way. To generateproofs for their answers, the approach relies on the fact that problemsolvers can retrieve and reuse their PSMs. To generate explanationsautomatically the approach relies on the Inference Web infrastructure.The approach is implemented for a deployed problem solver tool usingexplanations to train police teams to perform resource allocation forpublic safety.
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- paper: KSL-05-02.pdf
- slides:
| Abstract | Knowledge based systems (KBSs) should expl … Knowledge based systems (KBSs) should explain their answers if their usersare expected to understand and thus trust the answers. Problem solvers,KBSs implementing problem solving methods (PSMs), should also explaintheir answers. Few KBS systems, however, are effective at explaining theiranswers either because they cannot systematically generate explanationsor, when they can, their explanation components cannot easily be extendedto new kinds of tasks. In this paper we present an approach enablingproblem solvers to explain their answers in a systematic way. To generateproofs for their answers, the approach relies on the fact that problemsolvers can retrieve and reuse their PSMs. To generate explanationsautomatically the approach relies on the Inference Web infrastructure.The approach is implemented for a deployed problem solver tool usingexplanations to train police teams to perform resource allocation forpublic safety. form resource allocation forpublic safety. |
| Address | Stanford, CA, USA + |
| Author | Vladia Pinheiro +, Vasco Furtado +, Paulo Pinheiro da Silva +, and Deborah L. McGuinness + |
| Bibtype | techreport + |
| Institution | Knowledge Systems, AI Laboratory, Stanford University + |
| Key | KSL-05-02 + |
| Number | KSL-05-02 + |
| Paper | KSL-05-02.pdf + |
| Tag | Computer science + |
| Title | Explaining Problem Solver Answers + |
| Tr id | KSL-05-02 + |
| Year | 2005 + |
