Large-Scale Case-Based Reasoning:
Opportunity and Questions
David Leake
School of Informatics and Computing
Indiana University
Overview
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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)
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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 predelineated; 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?