Case-Based Reasoning Davitkov Miroslav, 2011/3116 University of Belgrade Faculty of Electrical Engineering

1. Case-Based Reasoning definition Case-Based reasoning (CBR), broadly construed, is the process of solving new problems based on the solutions of similar past problems. CBR is reasoning by remembering: It is a starting point for new reasoning Case-Based Reasoning is a well established research field that involves the investigation of theoretical foundations, system development and practical application building of experience-based problem solving. 2 / 25

An auto mechanic who fixes an engine by recalling another car that exhibited similar symptoms A lawyer who advocates a particular outcome in a trial based on legal precedents or a judge who creates case law. An engineer copying working elements of nature (practicing biomimicry), is treating nature as a database of solutions to problems. Case-based reasoning is a prominent kind of analogy making. 3 / Case-Based Reasoning definition Everyday examples of CBR :

1.Case – previously made and stored experience item 4 / CBR problem solver 2.Case-Base – core of every case – based problem solver - collection of cases

One of the core assumptions behind CBR is that similar problems have similar solutions. A case-based problem solver solves new problems primarily by reuse of solutions from the cases in the case-base. For this purpose, one or several relevant cases are selected. 5 / CBR problem solver

6 / 25 Once similar cases are selected, the solution(s) from the case(s) are adapted to become a solution of the current problem. 2. CBR problem solver When a new (successful) solution to the new problem is found, a new experience is made, which can be stored in the case-base to increase its competence, thus implementing a learning behavior.

7 / 25 1.Structural (a common structured vocabulary, i.e. an ontology) 2.Textual (cases are represented as free text, i.e. strings) 3.Conversational (a case is represented through a list of questions that varies from one case to another ; knowledge is contained in customer / agent conversations) 3. Types of CBR There are three main types of CBR that differ significantly from one another concerning case representation and reasoning:

8 / CBR Cycle Despite the many different appearances of CBR systems, the essentials of CBR are captured in a surprisingly simple and uniform process model. The CBR cycle consists of 4 sequential steps around the knowledge of the CBR system. The CBR cycle is proposed by Aamodt and Plaza.

9 / CBR Cycle New Case Retrieved Case New Case Solved Case Tested / Repaired Case Learned Case General Knowledge Previous Cases Problem Suggested Solution Confirmed Solution RETRIEVE REUSE REVISE RETAIN

10 / 25 One or several cases from the case base are selected, based on the modeled similarity. The retrieval task is defined as finding a small number of cases from the case-base with the highest similarity to the query. This is a k-nearest-neighbor retrieval task considering a specific similarity function. When the case base grows, the efficiency of retrieval decreases => methods that improve retrieval efficiency, e.g. specific index structures such as kd-trees, case-retrieval nets, or discrimination networks Retrieve 4. CBR Cycle

11 / 25 Reusing a retrieved solution can be quite simple if the solution is returned unchanged as the proposed solution for the new problem. Adaptation (if required, e.g. for synthetic tasks). Several techniques for adaptation in CBR - Transformational adaptation - Generative adaptation Most practical CBR applications today try to avoid extensive adaptation for pragmatic reasons Reuse 4. CBR Cycle

12 / 25 In this phase, feedback related to the solution constructed so far is obtained. This feedback can be given in the form of a correctness rating of the result or in the form of a manually corrected revised case. The revised case or any other form of feedback enters the CBR system for its use in the subsequent retain phase Revise 4. CBR Cycle

13 / 25 The retain phase is the learning phase of a CBR system (adding a revised case to the case base). Explicit competence models have been developed that enable the selective retention of cases (because of the continuous increase of the case-base). The revised case or any other form of feedback enters the CBR system for its use in the subsequent retain phase Retain 4. CBR Cycle

14 / CBR and the Future Internet The development of the future internet is affected by two major factors: semantics and collaboration. Two of the most influencing developments of the Semantic Web are: - the resource description language RDF (Resource Description Framework) - the knowledge representation language OWL (Web Ontology Language), which is based on RDF Already before the development of RDF and OWL, XML has been used as a case representation within the case-based reasoning community.

15 / CBR and the Future Internet There is a notable similarity between the ontologies developed within semantic applications and the representation of cases in structural case-based reasoning. Due to this similarity RDF and OWL both lend themselves to be used as case representation languages and thus expand the possibilities of case-based reasoning within the general WWW. There are technological and methodological similarities between ontologies and structured case-based reasoning and there are synergies that can be reached by merging both approaches.

16 / 25 CaseML - an RDF based Case Markup Language (by Chen and Wu); CaseML offers a domain-independent case ontology and also aims to make case-based reasoning available within the Semantic Web. SERVOGrid (by Aktas et al.) – also uses RDF for case representation; It is embedded in a conversational case-based reasoning system that aids scientists in finding resources such as program code or data that are needed to solve a specific task by assisting them in describing the necessary resources using meta data. 5. CBR and the Future Internet

17 / 25 jCOLIBRI framework - OWL is being used as the case interchange language; It is planned to advance the already distributed framework towards an architecture consisting of Semantic Web Services (SWS) where problem solving methods are represented as Web Services; In order to use these services the whole case-based reasoning process is decomposed into single tasks, which are then carried out by according Web Services. 5. CBR and the Future Internet

18 / 25 There is a close relation between collaborative filtering and CBR and these two can benefit from each other. Example 1: Collaborative filtering is used to assess the similarity between songs in a CBR system creating custom music compilations (CoCoA) [Aguzzoli et al.]. Example 2: A community based web search that uses the results of previous web searches of similar users in order to improve web search results [Briggs and Smyth]. 5. CBR and collaborative filtering

19 / 25 During the past twenty years, many CBR applications have been developed, ranging from prototypical applications build in research labs to large-scale fielded applications developed by commercial companies. Application areas of CBR include: - help-desk and customer service - recommender systems in electronic commerce - knowledge and experience management - medical applications and applications in image processing - applications in law, technical diagnosis, design, planning - applications in the computer games and music domain. 6. CBR applications

20 / 25 We will compare CBR with the rule induction algorithm of machine learning. Like a rule-induction algorithm, CBR starts with a set of cases or training examples; it forms generalizations of these examples, albeit implicit ones, by identifying commonalities between a retrieved case and the target problem. 7. CBR compared to other methods

21 / 25 The key difference, however, between the implicit generalization in CBR and the generalization in rule induction lies in when the generalization is made. A rule-induction algorithm draws its generalizations from a set of training examples before the target problem is even known; that is, it performs eager generalization. This is in contrast to CBR, which delays (implicit) generalization of its cases until testing time – a strategy of lazy generalization. CBR therefore tends to be a good approach for rich, complex domains in which there are myriad ways to generalize a case. 7. CBR compared to other methods

22 / 25 Critics of CBR argue that it is an approach that accepts anecdotal evidence as its main operating principle. Without statistically relevant data for backing and implicit generalization, there is no guarantee that the generalization is correct. There is recent work that develops CBR within a statistical framework and formalizes case-based inference as a specific type of probabilistic inference; thus, it becomes possible to produce case-based predictions equipped with a certain level of confidence. 8. Criticism of the CBR

23 / 25 The number of CBR approaches and applications developed up to now has become quite large. There is a significant number of CBR research groups and commercial companies, which develop CBR methods, software components, and applications on a regular basis. CBR is not only a technology but also a (process oriented) method. The combination of CBR with various other technologies within a great bandwidth of applications has become increasingly attractive for researchers as well as business professionals. 9. Conclusion

24 / 25 Ralph Bergmann, Klaus-Dieter Althoff, Mirjam Minor, Meike Reichle, Kerstin Bach: Case-Based Reasoning: Introduction and Recent Developments Benjamin Heitmann, Conor Hayes: Enabling Case-Based Reasoning on the Web of Data A. Aamodt, E. Plaza: Case-Based Reasoning: Foundational Issues, Methodological Variations, and System Approaches 10. References

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