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This paper explores the integration of diversity in case-based reasoning (CBR) methods, emphasizing its importance alongside similarity in enhancing retrieval and adaptation processes. We discuss diverse solutions for planning problems, reference previous successes in recommender systems, and outline innovative strategies for generating distinct plans for the same challenge. Our findings suggest that prioritizing diversity can improve search efficiency and personalization in CBR, while highlighting potential pitfalls in the duality of diversity and quality. A focus on practical implementations and future research directions is also presented.
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Case-Based Solution Diversity Alexandra Coman HéctorMuñoz-Avila Dept. of Computer Science & Engineering Lehigh University • Sources: • cbrwiki.fdi.ucm.es/ • www.iiia.csic.es/People/enric/AICom.html • www.cse.lehigh.edu/~munoz/CSE335/ • www.aic.nrl.navy.mil/~aha/slides/ • http://www.csi.ucd.ie/users/barry-smyth • http://www.csi.ucd.ie/users/lorraine-mcginty
Outline • Lehigh University • The InSyTe Laboratory • Overview of Case-Based Reasoning • Similarity • Retrieval • Adaptation • Conversational Case-based reasoning • Diversity versus Similarity • General versus Episodic Knowledge • Final Remarks
Synthetizing Diversity • Showcasing diverse solutions: success story in recommender systems (Smyth, Burke, McGinty …) • Plan diversity: • Definition of the problem: quantitative vsqualitiative (Myers, AAAI-01) • Generating two or more quantitative different plans for same problem (Srivastava et al, IJCAI-07) • Synthetizing diversity: • Case-based retrieval and adaptation from plan library (Coman& Munoz-Avila, ICCBR-10; 11 – under review ) • Generating two or more qualitatively different plans for same problem (Coman & Munoz-Avila, AAAI-11) • Our common solution: • S: diverse solutions so far, s: candidate solution, P: new problem sim(s,P) + relativeDiversity(s,S) • What changes: S, s, P, sim(), D(s,s’) 11
Research Program: Synthetizing Diversity preliminary work: Plan Diversity Case-based plan diversity sim(s,P) + relativeDiversity(s,S) New insight: sim(s,P) + relativeDiversity(s,S) + cost(s) Proposed idea: • Representation scope of using D() versus qualitative diversity • Trade-offs of solution: • Diversity versus quality • Diversity versus generation • Diversity in other paradigms: search (A*) Research topics: Danger: don’t want it to be a planning proposal
Query Available case Similar case Traditional Retrieval Approach • Similarity-BasedRetrieval • Select the k most similar items to the current query. • Problem • Vague queries. • Limited coverage of search space in every cycle of the dialogue. C2 C3 C1 Q
Query Available case Retrieved case Diversity Enhancement • Diversity-EnhancedRetrieval • Select k items such that they are both similar to the current query but different from each other. • Providing a wider choice allows for broader coverage of the product space. • Allows many less relevant items to be eliminated. C1 Q C2 C3
Dangers of Diversity Enhancement • Leap-Frogging the Target • Problems occur when the target product is rejected as a retrieval candidate on diversity grounds. • Protracted dialogs. • Diversity is problematic in the region of the target product. • Use similarity for fine-grained search. • Similarity is problematic when far from the target product. • Use diversity to speed-up the search. T C1 Q C2 C3
