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In both research fields, Case-Based Reasoning and Reinforcement Learning, the system under consideration gains its expertise from experience. Utilizing this fundamental common ground as well as further characteristics and results of these two disciplines, in this paper we develop an approach that facilitates the distributed learning of behaviour policies in cooperative multi-agent domains without...
The problem of defining robot behaviors to completely address a large and complex set of situations is very challenging. We present an approach for robot’s action selection in the robot soccer domain using Case-Based Reasoning techniques. A case represents a snapshot of the game at time t and the actions the robot should perform in that situation. We basically focus our work on the retrieval and reuse...
This paper describes the implementation of a distributed case-agent system where a case-base is comprised of a set of agents, where each computational agent is a case, rather than the standard case-base reasoning model where a single computational agent accesses a single case-base. This paper demonstrates a set of features that can be modelled in a case-agent system focusing on distributed self-organising...
In this paper, we describe a cooperative P2P bibliographical data management and recommendation system (COBRAS). In COBRAS, each user is assisted by a personal software agent that helps her/him to manage bibliographical data and to recommend new bibliographical references that are known by peer agents. Key problems are: – how to obtain relevant references? – how to choose a set of peer...
Most CBR systems in operation today are ‘retrieval-only’ in that they do not adapt the solutions of retrieved cases. Adaptation is, in general, a difficult problem that often requires the acquisition and maintenance of a large body of explicit domain knowledge. For certain machine-learning tasks, however, adaptation can be performed successfully using only knowledge contained within the case base...
The success of a company more and more depends on its ability to flexibly and quickly react to changes. Combining process management techniques and conversational case-based reasoning (CCBR) allows for flexibly aligning the business processes to new requirements by providing integrated process life cycle support. This includes the adaptation of business processes to changing needs by allowing deviations...
The complexity and high construction cost of case bases make it very difficult, if not impossible, to evaluate a CBR system, especially a knowledge-intensive CBR system, using statistical evaluation methods on many case bases. In this paper, we propose an evaluation strategy, which uses both many simple case bases and a few complex case bases to evaluate a CBR system, and show how this strategy may...
We show how case bases can be compiled into Decision Diagrams, which represent the cases with reduced redundancy. Numerous computations can be performed efficiently on the Decision Diagrams. The ones we illustrate are: counting characteristics of the case base; computing the distance between a user query and all cases in the case base; and retrieving the k best cases from the case base. Through empirical...
The present paper describes a case-based reasoning solution for solving the task of selecting adequate templates for realizing messages describing actions in a given domain. This solution involves the construction of a case base from a corpus of example texts, using information from WordNet to group related verbs together. A case retrieval net is used as a memory model. A taxonomy of the concepts...
Feature selection algorithms can reduce the high dimensionality of textual cases and increase case-based task performance. However, conventional algorithms (e.g., information gain) are computationally expensive. We previously showed that, on one dataset, a rough set feature selection algorithm can reduce computational complexity without sacrificing task performance. Here we test the generality of...
In this paper, we present a framework for Experience Management (EM) which is populated with case-based assistant systems for EM. The framework follows the building block model of Probst et al [28] which has been developed as a guidance for knowledge management activities. We taylor the building blocks for the special needs of EM and discuss for each building block the support and automation opportunities...
While much of the research in the area of recommender systems has focused on making recommendations to the individual, many recommendation scenarios involve groups of inter-related users. In this paper we consider the challenges presented by the latter scenario. We introduce a (case-based) group recommender designed to meet these challenges through a variety of recommendation features, including the...
Ambient Intelligence is a research area that has gained a lot of attention in recent years. One of the most important issues for ambient intelligent systems is to perceive the environment and assess occurring situations, thus allowing systems to behave intelligently. As the ambient intelligence area has been largely technology driven, the abilities of systems to understand their surroundings have...
This paper describes our work in textual Case-Based Reasoning within the context of Semantic Web. Semantic Annotation of plain texts is one of the core challenges for building the Semantic Web. We have used different techniques to annotate web pages with domain ontologies to facilitate semantic retrieval over the web. Typical similarity matching techniques borrowed from CBR can be applied to retrieve...
There are problems that present a huge volume of information or/and complex data as imprecision and approximated knowledge. Consequently, a Case-Based Reasoning system requires two main characteristics. The first one consists of offering a good computational time without reducing the accuracy rate of the system, specially when the response time is critical. On the other hand, the system needs soft...
Collaborative Web Search (CWS) proposes a case-based approach to personalizing search results for the needs of a community of like-minded searchers. The search activities of users are captured as a case base of search cases, each corresponding to community search behaviour (the results selected) for a given query. When responding to a new query, CWS selects a set of similar cases and promotes their...
While reasoning with cases is usually done in a similarity-based manner, additional general knowledge is often represented in rules, constraints, or ontology definitions and is applied in a deductive reasoning process. This paper presents a new view on the combination of deductive and similarity-based reasoning, which is embedded in the CBR context. The basic idea is to view general knowledge and...
We present a CBR approach to musical playlist recommendation. A good playlist is not merely a bunch of songs, but a selected collection of songs, arranged in a meaningful sequence, e.g. a good DJ creates good playlists. Our CBR approach focuses on recommending new and meaningful playlists, i.e. selecting a collection of songs that are arranged in a meaningful sequence. In the proposed approach, the...
In this paper we study the performance of a catalogue-based image classifier after applying different methods for performance improvement, such as feature-subset selection and feature weighting. The performance of the image catalogues is assessed by studying the reduction of the prototypes after applying Chang‘s prototype-selection algorithm. We describe the results that could be achieved and give...
Conversational CBR has been used successfully for several years but building a new system demands a great cognitive effort of knowledge engineers and using it demands a similar effort of users. In this paper we use ontologies as the driving force to structure a development methodology where previous design efforts may be reused. We review the main issues of current CCBR models and their specific solutions...
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