Case-Based Reasoning for Diagnosis and Solution Planning* Sima Soltani† Supervisor: Dr. Patrick Martin‡ Technical Report No. 2013-611 School of computing Queen’s university Kingston, Ontario, Canada October 2013 * This technical report is based on the author’s depth paper † [email protected] ‡ [email protected] Table of Contents 1. Introduction ...................................................................................................................................................... 2 2. Knowledge representation in CBR ..................................................................................................................... 4 3. Case memory models ........................................................................................................................................ 4 3.1 Flat memory................................................................................................................................................. 4 3.2 Hierarchy or shared-feature network ......................................................................................................... 5 3.3 Network based memory models ................................................................................................................. 8 4. CBR cycle .......................................................................................................................................................... 12 4.1. Retrieval .................................................................................................................................................. 12 4.1.1. Feature selection .......................................................................................................................... 12 4.1.2. Retrieval techniques ..................................................................................................................... 13 4.2. Reuse ....................................................................................................................................................... 23 4.2.1. Adaptation .................................................................................................................................... 23 4.3. Revision ................................................................................................................................................... 25 4.4. Retention ................................................................................................................................................. 25 4.4.1. Case base size reduction .............................................................................................................. 26 4.4.2. Indexing ........................................................................................................................................ 31 5. Diagnosis and planning with case-based reasoning ........................................................................................ 32 5.1. Diagnosis and case-based reasoning ....................................................................................................... 32 5.2. Case-based planning................................................................................................................................ 34 5.2.1. Retrieval and organization of case base ....................................................................................... 34 5.2.2. Reuse previous solutions .............................................................................................................. 35 5.2.3. Revision of solutions ..................................................................................................................... 35 5.2.4. Retention of new cases ................................................................................................................ 36 6. Open problems ................................................................................................................................................ 36 7. Discussion ........................................................................................................................................................ 38 8. Summery.......................................................................................................................................................... 39 1 1. Introduction Artificial intelligence (AI) has emerged as a field of computer science that aims to solve problems in a manner similar to that of humans. Although AI is still far from reaching human-level intelligence, there have been many successes in different areas and different applications such as computer science, medicine, finance, industry, transportation, communication, etc. [1]. The study of knowledge-based systems (KBS) (also known as expert systems) is one of the most successful branches of AI research. In knowledge-based systems, the model of knowledge must be elicited and implemented, often in the form of rules or objects, despite the depth of the domain knowledge that has to be covered [2]. Although model-based KBSs have been successful in many domains and applications [3] [4] [5] [6], several obstacles remain. The main difficulty is the elicitation of knowledge, mainly due to the requirement that the expert knowledge must be in the form of rules, which is not typically the way experts think about their domain problems and solutions. Other problems include the difficulty in KBS implementation for large scale systems, rule-based system’s lack of memory, lack of robustness, and the difficulty of maintaining these systems [7] [2] [8]. The problems plaguing KBSs have been reduced by the emergence of case-based reasoning. Case-based reasoning (CBR) is a problem solving methodology and a theory of reasoning that is based on the way humans think, reason, and solve problems in the real world [7] [9] [2] [8] [10] [11]. People tend to make decisions based on what they have experienced directly, or indirectly, through others’ experiences. In the same way, a CBR system is an intelligent, problem solving system that reasons by first retrieving a relevant prior case from its memory of cases and then adapting solutions to prior cases (experience) to solve the new problem. Aamodt and Plaza [9] proposed a life cycle for case- based reasoning systems as shown in Figure 1. The main four steps of CBR, named as the four “RE”s, are retrieve, reuse, revise and retain [9] [10]. In the retrieve step, a new problem is compared to cases in the case library (case base) and one or more similar cases are retrieved. The solutions in the retrieved cases are reused for the new problem and the success is evaluated and noted. If the suggested solution does not satisfy the new problem, revision is required. The revised solution and its problem are retained in the case base for future use [9] [2][3][4]. 2 Figure 1:CBR cycle proposed by Aamodt and Plaza [9] There is a vast number of applications using CBR from problem solving in applications involving design [2] [11] [12] [13], planning [14] [15] [16] [17] [18] [19] [20] [21] [22] [23] [24] [25] [26] [27] [28] [29] [30] and diagnosis [11] [31] [32] [33] [34] [35] [36] [37] [38] [39] [40] [41] [42] [43] [44] [45] [46] [47,48,49,50] to interpretive tasks like understanding and justification [2]. Recently, several web based applications have been built that use CBR [51] [52] [53] [54] [55]. In this paper we present an overview of CBR, and we survey two kinds of CBR applications, diagnosis and planning, and illustrate how CBR has been used in these applications. The remainder of the paper is structured as follows. Section 2 provides an overview of case representation. In Section 3 the different types of memory models used for case base representation are studied and their advantages and disadvantages are outlined. In Section 4 we survey the different methods used in the 4 “RE” steps of the CBR cycle in the literature. Section 5 focuses on the diagnosis and planning applications. We outline the methods that have been used and provide some insights as to which methods work better in these applications. Open problems in the field of case based reasoning and diagnosis and planning are discussed in Section 6. In Section 7, we summarize the paper. 3 2. Knowledge Representation in CBR In order to use the previous experiences in the CBR cycle, cases must be represented in a structural manner. Several methods of representation can be used in case base reasoning and the decision of the representation method depends on the domain that the system is modeling and the types of similarity assessments and retrieval, which are chosen according to the requirements of the system [7] [8] [56]. The simplest format to represent the cases in the case base is to have simple feature-value vectors, which are good for cases with attributes of nominal or numeric. With this kind of representation, no relationships between the attributes in cases or relationships between the cases are shown and surface similarity-based retrievals can be used for retrieving from the case base [7] [8] [56]. In some domains, however, the attributes are complex, or there are some relationships between the attributes of a case, or relationships between the cases in the case base. Several representations can be used for cases according to the requirements of the system. Cases can be represented in the form of objects, predicates, semantic networks, scripts, frames, rules and concept maps [7] [8] [56]. In some domains with complex