Case-based reasoning and system design: An integrated approach based on ontology and preference modeling

Abstract

International audienceThis paper addresses the fulfillment of requirements related to case-based reasoning (CBR) processes for system design. Considering that CBR processes are well suited for problem solving, the proposed method concerns the definition of an integrated CBR process in line with system engineering principles. After the definition of the requirements that the approach has to fulfill, an ontology is defined to capitalize knowledge about the design within concepts. Based on the ontology, models are provided for requirements and solutions representation. Next, a recursive CBR process, suitable for system design, is provided. Uncertainty and designer preferences as well as ontological guidelines are considered during the requirements definition, the compatible cases retrieval, and the solution definition steps. This approach is designed to give flexibility within the CBR process as well as to provide guidelines to the designer. Such questions as the following are conjointly treated: how to guide the designer to be sure that the requirements are correctly defined and suitable for the retrieval step, how to retrieve cases when there are no available similarity measures, and how to enlarge the research scope during the retrieval step to obtain a sufficient panel of solutions. Finally, an example of system engineering in the aeronautic domain illustrates the proposed method. A testbed has been developed and carried out to evaluate the performance of the retrieval algorithm and a software prototype has been developed in order to test the approach. The outcome of this work is a recursive CBR process suitable to engineering design and compatible with standards. Requirements are modeled by means of flexible constraints, where the designer preferences are used to express the flexibility. Similar solutions can be retrieved even if similarity measures between features are not available. Simultaneously, ontological guidelines are used to guide the process and to aid the designer to express her/his preferences

Enhancing predictive maintenance architecture process by using ontology-enabled Case-Based Reasoning

“…Another example uses an ontology to build the case base of a CBR system for decision support on manufacturing process selection [18]. Specifically in systems design, [4] proposes an integrated approach using an ontology and a CBR system to propose design solutions based on the initial requirements and preferences. The authors propose a generic design approach in which the cases are built from requirement attributes and the solution obtained from previous experiences with similar requirements.…”

“…The proposed approach aims at allowing the architect to save time when exploring the solution space and at the same time providing inspiration by offering diverse possible solutions to fulfill the logical components. In contrast to other implementations of CBR for systems design [4], [5], the proposed tool incorporates a domain ontology that serves as terminology framework for the CBR system. It helps to model the case structure, store the case base and compute semantic similarity for retrieval purposes in CBR.…”

Readiness, feasibility and confidence: how to help bidders to better develop and assess their offers

“…. Felfernig et al (2003) and Romero Bejarano et al (2014) proposed a methodology based on ontology and associated inference techniques for the configuration problem. Mida and Vernadat (2009) and Barták, Salido, and Rossi (2010) presented a constraint-satisfaction problem or CSP approach for process planning and scheduling.…”

Analysis of semantic features in free-form objects reconstruction

“…An ontology may provide a formal semantic representation of the objects for structured case representation in CBR methodologies (Lau et al, 2009), as well as methods for similarity assessment (Cordi et al, 2005; Batet et al, 2011). In the approach presented by Romero Bejarano et al (2014), the CBR process for system design in the aeronautic domain is based on an ontology to assist requirements definition, the retrieval of compatible cases, and the solutions definition. To take into account uncertainty and the unavailability of similarity measures between attributes values and to enlarge the scope of retrieval, requirements are modeled using flexible constraints defined upon the designer's preferences.…”

“…Knowledge for case adaptation is harder to acquire and demands a significant knowledge engineering effort (Policastro et al, 2006). According to Romero Bejarano et al (2014) only the tacit knowledge of the designer can be used for adaptation. This kind of knowledge is tied to experiences, intuition, unarticulated models, and implicit rules of thumb (Chandrasegaran et al, 2013).…”