A Framework for Incremental Query Formulation in Mixed-Initiative Case-Based Reasoning Kalyan Moy Gupta1 and David W. Aha2 1
ITT Industries, AES Division, Alexandria, VA 22303 Navy Center for Applied Research in Artificial Intelligence, Naval Research Laboratory (Code 5515), Washington, DC 20375
[email protected] 2
Abstract. Query formulation is a primary task in the retrieval phase of the case-based reasoning (CBR) cycle, and incremental variants of this task are a distinguishing characteristic of some mixed-initiative CBR approaches (e.g., those that implement a conversational CBR methodology). However, it has rarely been the focus of analysis, which complicates comparing these approaches. We identify the primitive decisions that a CBR system or its user can make during a constrained form of incremental query formulation, and discuss how initiative can be shared when sequencing these decisions. We use this framework to compare some existing mixed-initiative CBR approaches that incrementally formulate queries.
1. Introduction Case-Based Reasoning (CBR) systems retrieve cases from a repository of problem solving experiences and reuse them to analyze and/or solve similar new problems (Kolodner, 1993; Aamodt & Plaza, 1994). Human and/or software agents often interact with the CBR system, which can also be construed as an agent, to solve or analyze problems and make decisions. This interaction is characterized as mixedinitiative when the agents share control over the reasoning process, at times taking the initiative to contribute to the interaction as required (Allen, 1999). Several types of mixed-initiative CBR (MI-CBR) approaches exist and have been found useful for a variety of performance tasks (e.g., Veloso et al., 1997; Mitchell, 1997; Aha & Muñoz-Avila, 2001). However, no principled framework for categorizing MI-CBR systems exists, which complicates selecting an appropriate mixed-initiative process for a given performance task. We introduce such a framework, and demonstrate its utility by applying it to selected systems. Although agents may interact with a CBR system during any phase(s) of the CBR cycle, we address only the retrieval phase, and assume one agent is a (human) user. In particular, we focus on MI-CBR approaches that execute an incremental query formulation subtask during case retrieval. In Section 2, we briefly review the CBR cycle’s retrieval phase and discuss incremental query formulation. In Section 3, we present our framework for categorizing mixed-initiative interactions during incremental query formulation. We apply this framework to selected MI-CBR systems in Section 4, and conclude in Section 5 with future research issues.
Retrieve Identify Features
Collect Interpret Descriptors Problem
Search
Match
Select
Infer Descriptors
Figure 1: Retrieve sub-processes in the CBR cycle, adapted from (Aamodt & Plaza, 1994).
2. Mixed-Initiative Query Formulation in Case-Based Reasoning We assume the availability of a case base of potentially relevant problem-solving experiences, where each record (i.e., case) denotes a pair. Aamodt and Plaza (1994) detailed four top-level processes in the CBR cycle: 1. 2. 3. 4.
Retrieve a set C of most similar cases for a given problem query q, Reuse the information in C to solve q, generating a solution s, Revise s, creating s', to account for differences among q and problems in C, and Retain parts of
