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Research Aamodt & Plaza, 1994

Research Aamodt & Plaza, 1994. Robin Burke CSC 594 4/7/2004. Outline. Context Authors Venue Purpose Content foundational issues. Authors. Agnar Aamodt Norway Enric Plaza Spain. Venue. AI Communications Main European Journal for Artificial Intelligence. Purpose. Overview of CBR

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Research Aamodt & Plaza, 1994

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  1. ResearchAamodt & Plaza, 1994 Robin Burke CSC 594 4/7/2004

  2. Outline • Context • Authors • Venue • Purpose • Content • foundational issues

  3. Authors • Agnar Aamodt • Norway • Enric Plaza • Spain

  4. Venue • AI Communications • Main European Journal for Artificial Intelligence

  5. Purpose • Overview of CBR • Methodology • European results • Three goals • foundational issues • methodological variations • system approaches

  6. Foundational Issues • Characterizing CBR • Case representation • Issues related to each step of CBR cycle • knowledge-weak • knowledge-intensive

  7. Characterizing CBR • 4 Rs • Retrieve • Reuse • Revise • Retain

  8. Decomposition • Retrieve • identify features • search • match • select • Reuse • copy • adapt • Revise • evaluate solution • repair • Retain • integrate • index • extract

  9. Memory organization • Dynamic memory model • cognitively-based • generalized episodes • cases discriminated by feature values • Category / exemplar model • also cognitively-based • categories defined by exemplars • prototypical examples

  10. Retrieval • feature extraction • simple – use features present • complex – infer (deep) features • matching • simple – find nearby cases • complex – reason about similarity

  11. Reuse • Transformational reuse • adapt the case • emphasis on experience with rules as a guide • Derivational reuse • adapt the problem-solving process • emphasis on rule-based problem-solving with experience as a guide

  12. Revise • Evaluation • was the proposed solution successful? • simple – user critiques • complex – system generates and processes feedback • Repair • another adaptation step guided by evaluation • simple – discard failures • complex – explain failure and adjust accordingly

  13. Retain • Extract • package problem-solving episode as a case • simple – save features of problem situation and solution • complex – save explanation of why/how the solution solved the problem • Index • decide how to label the case • simple – all input features • complex – use diagnostic / predictive features • Integrate • put the case in memory • simple – just store it • complex – adjust indexing mechanism and/or background knowledge

  14. Bottom Line • Review of the state-of-the-art in 1994 • Indicates some of the design tradeoffs still important in case-based systems • Knowledge management field did not exist • discussion couched in AI terms (planning, problem-solving) • both fielded examples we would now consider as KM

  15. KM Implications • Should an organizations just store stuff? • value of identifying and organizing cases • How should retrieval work? • syntactic: keywords • semantic: reasoning about the domain • What else is needed besides cases?

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