This paper describes techniques for integrating neural networks and symbolic components into powerful hybrid systems. Neural networks have unique processing characteristics that enable tasks to be performed that would be di cult or intractable for a symbolic rule-based system. However, a stand-alone neural network requires an interpretation either by ahuman or a rulebased system. This motivates the integration of neural/symbolic techniques within a hybrid system. Anumber of integration possibilities exist: some systems consist of neural network components performing symbolic tasks while other systems are composed of several neural networks and symbolic components, each component acting as a self-contained module communicating with the others. Other hybrid systems are able to transform subsymbolic representations into symbolic ones and vice-versa. This paper providesanoverview and evaluation of the state of the artofseveral hybrid neural systems for rule-based processing. 1

One of the perceived benefits of Case-Based Reasoning (CBR) is the potential to use retrieved cases to explain predictions. Surprisingly, this aspect of CBR has not been much researched. There has been some early work on knowledge-intensive approaches to CBR where the cases contain explanation patterns (e.g. SWALE). However, a more knowledge-light approach where the case similarity is the basis for explanation has received little attention. To explore this, we have developed a CBR system for predicting blood-alcohol level. We compare explanations of predictions produced with this system with alternative rule-based explanations. The casebased explanations fare very well in this evaluation and score significantly better than the rule-based alternative.

In this paper, we describe a system called TagAssist that provides tag suggestions for new blog posts by utilizing existing tagged posts. The system is able to increase the quality of suggested tags by performing lossless compression over existing tag data. In addition, the system employs a set of metrics to evaluate the quality of a potential tag suggestion. Coupled with the ability for users to manually add tags, TagAssist can ease the burden of tagging and increase the utility of retrieval and browsing systems built on top of tagging data.

To run a smart E-Business or provide efficient Web service, a web services composition model is needed. Web services composition refers to the process of collaborating the heterogeneous web services. This paper presents a model of web services composition by using Case-Based Reasoning (CBR) techniques. CBR is applied in the process of service discovery, which is the crucial composition process. Our service composition model integrates the two behaviours of proactive and reactive service compositions. We will address dynamic composition and collaboration among services. The similarity feature of CBR is used for efficient service discovery .

Abstract. An important step in the solution of a target problem in case-based reasoning (CBR) is the retrieval of similar previous cases that can be used to solve the target problem. We review a selection of papers from the CBR literature on aspects of retrieval, such as approaches to the assessment of surface and structural similarity and techniques for automating the construction and maintenance of similarity measures. We also examine a number of retrieval techniques that have been developed to address the limitations of retrieval based purely on similarity. 1

Abstract. Metaheuristic approaches to examination timetabling problems are usually split up into two phases: initialisation phase in which a heuristic is employed to construct an initial solution and improvement phase which employs a metaheuristic. Different hybridisations of metaheuristics with sequential heuristics are known to lead to solutions of different quality. A Case Based Reasoning CBR methodology has been developed for selecting an appropriate hybridsation of Great Deluge metaheuristic with a sequential construction heuristic. In this paper we propose a new similarity measure between two timetabling problems that is based on fuzzy sets. The experiments were performed on a number of real-world problems and the results were also compared with other state-of-theart methods. The results obtained show the effectiveness of the developed CBR system. 1

Most users of machine-learning products are reluctant to use the systems without any sense of the underlying logic that has led to the system's predictions. Unfortunately many of these systems lack any transparency in the way they operate and are deemed to be `black boxes'. In this paper we present a Case-Based Reasoning (CBR) solution to providing supporting explanations of black-box systems. This CBR solution uses locally derived feature ranking information that reflects the importance of each feature to a prediction and a locally adjusted case retrieval mechanism. The retrieval mechanism takes advantage of the derived feature weightings to help select cases that are a better reflection of the black-box solution and thus more convincing explanations.

We have argued elsewhere that user goals should be taken into account when deciding what kind of explanation of its results a CBR system should give. In this paper, we propose the use of an Activity Theory based methodology for identifying di#erent user goals and expectations towards explanations given by a system supporting a work process.

Abstract. A key issue in case-based reasoning is how to maintain the domain knowledge in the face of a changing environment. During the case retrieval process in case-based reasoning, feature-value pairs are used to compute the ranking scores of the cases in a case base, and different feature-value pairs may have different importance measures, represented as weight values, in this computation. How to maintain a set of appropriate feature weights so that they can be used to solve future problems effectively and efficiently will be a key factor in determining the success of case-based reasoning applications. Our focus in this paper is on the dynamic maintenance of feature weights in a case base. We address a particular problem related to the feature-weight maintenance issue. In current practice, the feature weights are assigned and revised manually, not only making them highly informal and inaccurate, but also involving intensive labor. We would like to introduce a semi-automatic introspective learning method to partially address this issue. Our approach is to construct a network architecture on the case base that supports introspective learning. Weight learning and weight-evolution are accomplished in the background through the integration of a learning network into case-based reasoning, in which, while the reasoning part is still case based, the learning part is shouldered by a layered network. The computation in the network follows well-known neural network algorithms with well known properties. We demonstrate the effectiveness of our approach through experiments.

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