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It is well known that recognition performance degrades signiicantly when moving from a speaker-dependent to a speaker-independent system. Traditional hidden Markov model (HMM) systems have successfully applied speaker-adaptation approaches to reduce this degradation. In this paper we present a n d e v aluate some techniques for speaker-adaptation of a(More)
One of the main questions concerning learning in a Multi-Agent Sys-tem's environment is: " (How) can agents benefit from mutual interaction during the learning process? " This paper describes a technique that enables a heterogeneous group of Learning Agents (LAs) to improve its learning performance by exchanging advice. This technique uses supervised(More)
This work aims at defining and testing a set of techniques that enables agents to use information from several sources during learning. In Multiagent Systems (MAS) it is frequent that several agents need to learn similar concepts in parallel. In this type of environment there are more possibilities for learning than in classical Machine Learning. Exchange(More)
Coping with dynamic changes in traffic volume has been the object of recent publications. Recently, a method was proposed, which is capable of learning in non-stationary scenarios via an approach to detect context changes. For particular scenarios such as the traffic control one, the performance of that method is better than a greedy strategy, as well as(More)
This research aims at studying the effects of exchanging information during the learning process in Multiagent Systems. The concept of advice-exchange, introduced in (Nunes and Oliveira, 2002b), consists in enabling an agent to request extra feedback, in the form of episodic advice, from other agents that are solving similar problems. The work that was(More)
Artificial evolution of robot behavior is commonly conducted in environments containing a single robot or multiple robots that are all controlled by evolving behavioral logic. In this paper , we take a novel approach and study how the presence of preprogrammed robots affects the evolutionary process and the solutions evolved. We evolve behavioral control(More)