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Large-Scale Case-Based Reasoning: Opportunity and Questions
David LeakeSchool of Informatics and Computing
Indiana University
Overview
• Intro to case-based reasoning• Appeal of CBR for large scale data• Some challenges• Questions for the audience
What is CBR?
• Reasoning by remembering (and analogizing and adapting…)
• Common in human planning, programming, problem-solving, diagnosis, decision-making
The CBR Cycle
From Leake, Maguitman, and Reichherzer, 2005
Motivations for Using CBR(Kolodner 1993; Aamodt & Plaza 1994; Leake, 1996)
• Easing knowledge acquisition, especially when cases are already available
• Reasoning when causal connections are complex or poorly understood
• Speedup from reuse• Explainability
CBR as AI Technology
• Classic applications include force deployment planning, diagnosis, design support, help desks,…
• IU eScience example: The Phale system (Leake & Kendall-Morwick, 2008, 2009) supports workflow construction with case-based reuse of lessons from provenance traces collected by the Karma provenance collection tool (http://d2i.indiana.edu/provenance_karma; project directed by Beth Plale).
Large-Scale Challenge for Phala
• Phala’s case retrieval depends on fast structure mapping
• Structure mapping toolkit has been developed and publicly released (Structure Access Interface, Kendall-Morwick & Leake, 2011)
• Fast structure mapping remains a key issue, especially for process-oriented case-based reasoning
• Taking a step back, how does CBR fit domains with large collections of data?
The Core of CBR:Reasoning Directly from the Data
(First approximation)
• Cases are specific episodes• Lazy learning: Learning is storage • Don’t extract rules: Reason from similar cases• Don’t generalize cases • Each problem-solving episode adds a case
Large-Scale CBR
• Most CBR systems are comparatively small scale
• Questions for today: – What are the large-scale applications which might
most benefit from CBR? – What would issues would need to be addressed to
apply it?
Reasoning Directly from the Data(Second Approximation, fleshing out core issues)
• Cases are specific episodes (not necessarily pre-delineated; could be very large)
• Lazy learning: Learning is storage (+ indexing)• Don’t extract rules: Reason from similar cases (how to find
them? How to extract indices/similarity criteria? How to integrate reasoning?)
• Don’t generalize cases (adaptation)• Each problem-solving episode adds a case (scale issues,
maintenance, and case base sharing may be needed)
Scale-Up as Opportunity: Example of Potential for Big Data to Ease Case Adaptation
(Jalali & Leake, 2013)
• Problem: How to gather/generate the knowledge to adapt prior cases to new needs
• For numerical prediction, adaptations can be generated by comparing case differences
Case Difference Heuristic [Hanney & Keane, 1997]
• A knowledge-light method for adaptation acquisition• Adaptations are generated by pairwise case comparison
Extending Case Adaptation with Automatically-Generated Ensembles of Adaptation Rules Vahid Jalali and David Leake
Approaches to Instance-Based Adaptation Generation and Application
• Generation: Selecting cases from which generate adaptations
• Application: Selecting source cases to adapt
Extending Case Adaptation with Automatically-Generated Ensembles of Adaptation Rules Vahid Jalali and David Leake
Questions to Discuss
• For what large-scale tasks CBR could provide an edge?
• What are opportunities for facilitating computations underlying large-scale CBR?