Case-Based Reasoning With Subjective Influence Knowledge

Abstract

Problems in domains that are highly dimensional, inhomogeneous, and context dependent are difficult to support by computational tools. If solutions to these problems must be devised based on little information that is highly subjective, the situation worsens. In this paper, we propose a new case-based reasoning (CBR) method for addressing such problems. The method is based on augmenting case descriptions with knowledge in the form of influence graphs. We use these influence graphs to cluster the space of problems. These clusters, in turn, are used to retrieve the most relevant cases given a new problem specified only by three to four attributes. We tested the system in a lab setting and found it very promising. The second contribution of the paper involves an analysis of the incorporation of knowledge in CBR. This analysis provides a basis for classifying future interactions between CBR and other knowledge sources.

Design Catalogs: A Systematic Approach to Design and Value Flexibility in Engineering Systems

“…Thus, this initial study sets a research agenda to improve the method of finding representative scenarios. The work on scenario planning, probability elicitation techniques, and case‐based reasoning provide good directions for further developments [Helmer‐Hirschberg, ; Morgan and Henrion, ; Schoemaker, ; Reich and Kapeliuk, ]. In step 3, only capacity expansion was analyzed for demonstration purposes, but more flexibility strategies can be studied.…”

“…The input to this step is (are) the main uncertainty driver(s), modeled as part of the performance model, and the output consists of a representative set of such scenarios that are used to stimulate flexible systems design concept generation in step 3. Finding such representative set can be done using popular methods in industry like Shell scenario planning, probability elicitation, or case‐based reasoning based on discussions with design experts within the design organization and/or with the client [Helmer‐Hirschberg, ; Morgan and Henrion, ; Schoemaker, ; Reich and Kapeliuk, ]. Here, a systematic process based on scenario planning is proposed—see Appendix for suggested elicitation items.…”

CASE-BASED REASONING FOR PREDICTING MULTIPERIOD FINANCIAL PERFORMANCES OF TECHNOLOGY-BASED SMEs

“…The selected solution is later validated through feedback information. Finally, the validated solution can be added into the case store for use in future problem solving if appropriate in the update step (Riesbeck and Schank 1989;Kolodner 1992;1993;Brown and Gupta 1994;Chi et al 1993;Morris 1994;Hansen et al 1995;Mechitov et al 1995;O'Roarty et al 1997;Shin and Han 1999;Liao 2000;Capus and Tourigny 2003;Hsu et al 2004;Reich and Kapeliuk 2004;Pedro et al 2005).'' Among the five steps, step two, case retrieval, is most critical for determining effectiveness of the CBR system.…”

“…Several recent works have applied CBR to different business domains, including business acquisitions (Pal and Palmer 2000), transfer pricing (Curt and Elliott 1997), marketing research (Ville 1997;Changchien and Lin 2005), medical applications (Wyns et al 2004), the prediction of high risk software components (Emam et al 2001), the financial classification such as bond rating and bankruptcy prediction (Bryant 1997;Shin and Han 1999), and others (Capus and Tourigny 2003;Reich and Kapeliuk 2004).…”

An Interactive Intelligent Search Engine Model Research Based on User Information Preference

“…In order to solve these problems, many researchers are focusing on research on intelligent search engine [5], [7], [8], [9], [16]. And some research results such as semantic web, semantic search, context search and synonymous search [1], [2], [4], [6], [10], [11], [12], [14], [15] etc. have been given.…”