Knowledge-based decision model construction for hierarchical diagnosis and repair Public Deposited

http://ir.library.oregonstate.edu/concern/graduate_thesis_or_dissertations/mk61rk919

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  • Knowledge-Based Model Construction (KBMC) has generated a lot of attention due to its importance as a technique for generating probabilistic or decision-theoretic models whose range of applicability in AI has been vastly increased. However, no one has tried to analyze the essential issues in KBMC, to determine if there exists a general efficient KBMC method for any problem domain, or to y identify the fruitful future research on KBMC. This research presents a unified framework for comparative analysis of KBMC systems identifying the essential issues in KBMC, showing that there is no such general efficient KBMC method, and listing the fruitful future research on KBMC. This thesis then presents a new KBMC mechanism for hierarchical diagnosis and repair. Diagnosis is formulated as a stochastic process and modeled using influence diagrams. In the best case using an abstraction hierarchy in problem-solving can yield an exponential speedup in search efficiency. However, this speedup assumes backtracking never occurs across abstraction levels. When this assumption fails, search may have to consider different abstract solutions before finding one that can be refined to a base solution, and, therefore, search efficiency is not necessarily improved. In this thesis, we present a decision model construction method for hierarchical diagnosis and repair. We show analytically and experimentally that our method always yields a significant speedup in search efficiency, and that hierarchies with smaller branching factors yield more significant efficiency gains. This thesis employs two causal pathways (functional and bridge fault) of domain knowledge in device trouble shooting, preventing either whole class of faults we will never be able to diagnose. Each causal pathway models the knowledge of adjacency and behavior within the corresponding interaction layer. Careful search of causal pathways allows us to restrict the search space of fault hypotheses at each time. We model this search among causal pathways decision-theoretically. Decision-theoretic control usually results in significant improvements over unaided human expert judgments. Furthermore, these improvements in performance are robust to substantial errors in the assessed costs and probabilities.
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  • description.provenance : Made available in DSpace on 2012-11-27T19:58:11Z (GMT). No. of bitstreams: 1 YuanSoe-Tsyr1995.pdf: 5310892 bytes, checksum: cbf704d036dca4ed9f53d4b2aca97316 (MD5) Previous issue date: 1994-06-06
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  • description.provenance : Approved for entry into archive by Patricia Black(patricia.black@oregonstate.edu) on 2012-11-26T20:40:28Z (GMT) No. of bitstreams: 1 YuanSoe-Tsyr1995.pdf: 5310892 bytes, checksum: cbf704d036dca4ed9f53d4b2aca97316 (MD5)
  • description.provenance : Approved for entry into archive by Patricia Black(patricia.black@oregonstate.edu) on 2012-11-27T19:58:11Z (GMT) No. of bitstreams: 1 YuanSoe-Tsyr1995.pdf: 5310892 bytes, checksum: cbf704d036dca4ed9f53d4b2aca97316 (MD5)

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