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While similarity and retrieval in case-based reasoning (CBR) have received a lot of attention in the literature, other aspects of CBR, such as case reuse are less un- derstood. Specifically, we focus on one of such, less understood, problems: knowledge transfer. The issue we intend to elucidate can be expressed as follows: what knowledge present in a source case is transferred to a target problem in case-based inference? This pa- per presents a preliminary formal model of knowledge transfer and relates it to the classical notion of analogy.

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The goal of Knowledge Discovery is to extract knowledge from a set of data. Most common techniques used in knowledge discovery are based on clustering methods whose goal is to analyze a set of objects and to obtain clusters based on the similarity between these objects. A desirable characteristic of clustering results is that they should be easily understandable by domain experts. In this paper we introduce LazyCL, a procedure using a lazy learning method to produce explanations on clusters of unlabeled cases. These explanations are the basis on which experts can perform knowledge discovery. Here we use LazyCL to generate a domain theory for classification of melanomas in situ.

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Similarity assessment is a key operation in many artificial intelligence fields, such as case-based reasoning, instance-based learning, ontology matching, clustering, etc. This paper presents a novel measure for assessing similarity between individuals represented using Description Logic (DL). We will show how the ideas of {\em refinement operators} and {\em refinement graph}, originally introduced for inductive logic programming, can be used for assessing similarity in DL and also for abstracting away from the specific DL being used. Specifically, similarity of two individuals is assessed by first computing their {\em most specific concepts}, then the {\em least common subsumer} of these two concepts, and finally measuring their distances in the refinement graph

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ICCBR2011-Similarity-DL.pdf

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In this paper we focus on how to use CBR for making collective decisions in groups of agents. Moreover, we show that using CBR allows us to dispense with standard but unrealistic assumptions taken in these kind of tasks. Typically, social choice studies voting methods but assumes complete knowledge over all possible alternatives. We present a more general scenario called {\em open-ended deliberative agreement with rational ignorance (ODARI)}, and show how can CBR be used to deal with rational ignorance. We will apply this approach to the {\em Banquet Agreement} scenario, where two agents deliberate and jointly agree on a two course meal. Rational ignorance makes sense in this scenario, since it would be unreasonable for the agents to know all the alternatives. Unknown alternatives, as well as a strategy to increase chances of reaching an agreement, are problems addressed using case-based methods

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odari-iccbr11-crc.pdf

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Different agents in a multiagent system might have different solution quality or preference criteria. Therefore, when solving problems collaboratively using CBR, case reuse must take this into account. In this paper we propose \abarc , a model for multiagent case reuse, which divides case reuse in two stages: {\em individual reuse}, where agents generate full solutions internally, and {\em multiagent reuse}, where agents engage in a deliberation process in order to reach an agreement on a final solution. Specifically, \abarc\ is based on the idea of {\em amalgam}, which is a way to generate solutions by combining multiple solutions into one. We illustrate \abarc\ in the domain of interior room design

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ABARC.pdf

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Similarity assessment plays a key role in lazy learning methods such as k-nearest neighbor or case-based reasoning. In this paper we will show how refinement graphs, that were originally introduced for inductive learning, can be employed to assess and reason about similarity. We will define and analyze two similarity measures, $S_{\lambda}$ and $S_{\pi}$, based on refinement graphs. The \emph{anti-unification-based similarity}, $S_{\lambda}$, assesses similarity by finding the anti-unification of two instances, which is a description capturing all the information common to these two instances. The \emph{property-based similarity}, $S_{\pi}$, is based on a process of disintegrating the instances into a set of {\em properties}, and then analyzing these property sets.
Moreover these similarity measures are applicable to any representation language for which a refinement graph that satisfies the requirements we identify can be defined. Specifically, we present a refinement graph for feature terms, in which several languages of increasing expressiveness can be defined. The similarity measures are empirically evaluated on relational data sets belonging to languages of different expressiveness.

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Humans have developed jurisprudence as a mechanism to solve conflictive situations by using past experiences. Following this principle, we propose an approach to enhance a multi-agent system by adding an authority which is able to generate new
regulations whenever conflicts arise. Regulations are generated by learning from previous similar situations, using a machine learning technique (based on Case-Based Reasoning) that solves new problems using previous experiences. This approach requires: to be able to gather and evaluate experiences; and to be described in such a way that similar social situations require similar regulations. As a scenario to evaluate our proposal, we use a simplified version of a traffic scenario, where agents are traveling cars. Our goals are to avoid collisions
between cars and to avoid heavy traffic. These situations, when happen, lead to the synthesis of new regulations. At each simulation step, applicable regulations are evaluated in terms of their effectiveness and necessity. Overtime the system generates a set of regulations that, if followed, improve system performance (i.e. goal achievement).

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Existing organisational centred multi-agent systems regulate agents’ activities. However, population/environmental changes may lead to a poor fulfilment of system’s goals, and therefore, adapting the whole organisation becomes key. In this paper, we propose to use Case-Based Reasoning learning to adapt norms that regulate agents’ behaviour.Moreover,
we empirically evaluate this approach in a P2P scenario.

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