1. Large-Scale Case-Based Reasoning: Opportunity and Questions David Leake School of Informatics and Computing Indiana University

2. Overview Intro to case-based reasoning Appeal of CBR for large scale data Some challenges Questions for the audience

3. What is CBR? Reasoning by remembering (and analogizing and adapting…) Common in human planning, programming, problem-solving, diagnosis, decision-making

4. The CBR Cycle From Leake, Maguitman, and Reichherzer, 2005

5. 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

6. 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).http://d2i.indiana.edu/provenance_karma

7. 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?

8. 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

9. 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?

10. 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)

11. 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

12. 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

13. 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

14. Questions to Discuss For what large-scale tasks CBR could provide an edge? What are opportunities for facilitating computations underlying large-scale CBR?