In this paper we investigate the use of reinforcement learning to address the multiagent coalition formation problem in dynamic, uncertain, real-time, and noisy environments. To adapt to the complex environmental factors, we equip each agent with the case-based reinforcement learning ability which is the integration of case-based reasoning and reinforcement learning. The agent can use case-base...

The article describes a method combining two widely-used empirical approaches: rule induction and instance-based learning. In our algorithm (RIONA) decision is predicted not on the basis of the whole support set of all rules matching a test case, but the support set restricted to a neighbourhood of a test case. The size of the optimal neighbourhood is automatically induced during the learning p...

In RoboCup 2004 we participated in “classical” 2DSimulation League as well as in the new introduced and first time hold 3D-competition. Our team “AT Humboldt” placed 10th while “AT Humboldt 3D” became vice world champion. Like in the past years we used our soccer agents as a research testbed for long-term deliberation and realtime reasoning, cooperation and coordination in MAS, CBR-aided decisi...

Web Service discovery and selection deal with the retrieval of the most suitable Web Service, given a required functionality. Addressing an effective solution remains difficult when only functional descriptions of services are available. In this paper, we propose a solution by applying Case-based Reasoning, in which the resemblance between a pair of cases is quantified through a similarity func...

This research examines the metaphor of goal-driven planning as a tool for performing the integration of multiple learning algorithms. In case-based reasoning systems, several learning techniques may apply to a given situation. In a failure-driven learning environment, the problems of strategy construction are to choose and order the best set of learning algorithms or strategies that recover fro...

This paper presents a case-based reasoning system TA3. We address the exibility of the case-based reasoning process, namely exible retrieval of relevant experiences, by using a novel similarity assessment theory. To exemplify the advantages of such an approach, we have experimentally evaluated the system and compared its performance to the performance of non-exible version of TA3 and to other m...

Case based learning CBL algorithms are CBR systems that focus on the topic of learning This paper notes why CBL algorithms are good choices for many supervised learning tasks describes a framework for CBL algorithms outlines a progression of CBL algorithms for tackling learning applications characterized by challenging problems i e noisy cases poor similarity functions contextual importance of ...

Binary classification is one of the most common problem in machine learning. It consists in predicting whether a given element is of a particular class. In this paper, a new algorithm for binary classification is proposed using a hypergraph representation. Each element to be classified is partitioned according to its interactions with the training set. For each class, the total support is calcu...

Both human and multi-agent societies are prone to best function with the inclusion of regulations. Human societies have developed jurisprudence as the theory and philosophy of law. Within it, utilitarianism has the view that laws should be crafted so as to produce the best consequences. Following this same objective, we propose an approach to enhance a multi-agent system with a regulatory autho...

Case-based reasoning can be a particularly useful problem solving strategy when combined with other artificial intelligence reasoning paradigms or with some other computational problem solving method. An approach is presented in which the machine learning capabilities of an artificial neural network are used to enhance the reuse of past experience in the case-based reasoning cycle. This approac...