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Combining Reasoning Modes, Levels, and Styles

through Internal CBR?

David B. Leake and Andrew Kinley Computer Science Department
Indiana University, Bloomington, IN 47405,


This paper discusses motivations and proposes methods for integrating multiple reasoning modes, styles, and levels within a case-based reasoning system. It describes a CBR system in which rule-based internal processing is augmented with two styles of case-based reasoning, derivational and transformational CBR, and which reasons at both the domain-level and the metalevel, in order to respond to the requirements of different processing tasks. The fundamental principal is for the system to learn by monitoring, capturing, and exploiting multiple types of prior system reasoning. The paper considers the ramifications of this approach and its potential as a strategy for multimodal reasoning in other contexts.


The reasoning processes of artificial intelligence systems can be described along multiple dimensions, such as the reasoning mode or paradigm the system uses (e.g., rule-based reasoning or case-based reasoning), the style of reasoning within that paradigm (e.g., transformational or derivational approaches to case-based reasoning), and the level at which that reasoning is applied (e.g., domain-level reasoning or metareasoning). Their combination provides interesting opportunities for multimodal systems.

This paper summarizes a system combining multiple modes, styles, and levels of reasoning, describing its use of multimodal reasoning and considering the potential applicability of similar approaches to other systems. The system described is a case-based planner that uses multiple forms of reasoning to support its domain level case-based reasoning process. The system combines two reasoning paradigms, rule-based and case-based reasoning; two reasoning styles, transformational and derivational CBR; and two levels of reasoning, domain level reasoning (about plans) and

?This work was supported in part by the National Science Foundation under Grant No. IRI-9409348. David Wilson has made many contributions to the project described in this paper. We thank the anonymous reviewers for their helpful comments.

metareasoning (about guiding the process for adapting plans to fit new situations).

The system's baseline reasoning process is transformational CBR; it generates new plans by adapting prior plans to fit new circumstances. Initially, this process adapts plans by rule-based reasoning, while internal case-based reasoning components capture the reasoning process used to adapt cases. As case adaptation experience is acquired, internal case-based reasoning supplants the rule-based process for case adaptation and similarity assessment. The case-based case adaptation process uses a different reasoning style from the baseline planner: it uses derivational CBR to compile and replay the reasoning underlying an adaptation.

This paper illustrates the usefulness of this multimodal approach by describing why specific reasoning modes are particularly well-suited to certain system processing tasks, how each approach contributes to the overall function of the system, and how the multiple approaches support each other. Based on experience with this system we make a more general claim: that using CBR components to monitor, capture, and replay a system's reasoning processes is a promising approach to guiding those processes and augmenting their capabilities.

Task and System Architecture

Our testbed system, DIAL (Leake, Kinley, & Wilson 1996), is a case-based planning system. DIAL's domain is disaster response planning, the initial highlevel planning involved in deciding, for example, the basic outline for a plan to rescue and relocate the victims of a flood or earthquake. This is a domain for which no hard-and-fast rules exist, and case-based reasoning is often proposed as a reasoning paradigm for such domains. Unfortunately, in such domains it may also be difficult to formulate the knowledge required to guide the application of stored cases to new problems. Our multimodal reasoning approach is aimed at alleviating this problem by adding reasoning components that capture and replay the reasoning done to apply previous plan cases.
DIAL's basic planning process, transformational