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Multi-robot systems (MRS) are a very active and important research topic nowadays. One of the main problems of these systems is the large number of variables to take into account. Due to this, robot behaviors are sometimes learnt instead of calculated via analytical expressions. A typical learning mechanism, specially for biomimetic robots, is Learning from demonstration (LfD). This paper proposes a LfD approach for implicit coordinated navigation using combination of CaseBased Reasoning (CBR) behaviors. During a training stage, CBR is used to learn simple behaviors that associate positions of other robots and/or objects to motion commands for each robot. Thus, human operators only need to concentrate on achieving their robot's goal as efficiently as possible in the operating conditions. Then, in running stage, each robot will achieve a different coordinate navigation strategy depending on the triggered behaviors. This system has been successfully tested with three Aibo-ERS7 robots in a RobCup-like environment.

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A Neural Call Admission Control (NCAC) decides whether a new connection can be accepted, according to a neural network decision. Such a decision is based on traffic information (e.g cell loss rate (CLR) ) measured in real time., at tlie ATM node, which ensures continual training of an artificial neural network (ANN) during NCAC performance, allowing the CAC to adapt to possible changes in traffic conditions. This paper addresses the problem of providing accurate CLR information to pcrforin optimal ANN training , which results in efficient NCAC performance. This paper proposes a novel estimation method based on measuring tlie CLR at virtual links with different and slower output rates. This information can be related to the real CLR, by incans of an ANN, thus solving tlie accuracy and estimation-duration problems of real-link estimates. Prior inforination which allows tlie ANN to interpolate the real CLR is also rcquircd to establish the relationship between the virtual and tlie real CLRs. This information lias been named Zero Loss Bandwidth patterns. INTRODUCTIONCall Admission Control (CAC) is a key element for traffic control i n ATM networks CAC must decide which combinations of conneclions of the different traffic types existing In tlie network can be supported without any loss in the quality of service (QoS) required by each type The only QoS parameter considered in this case is the cell loss rate associated uitli the trafic aggregate accessing to tlie ATM node Many attempts have been made to compute such a loss rate analytically, [1][2], but have always come up against the same problem. namely that tlie algorithms developed have been so eoinplex as to render their rcal-time application impossible, in inany cases for computational reasons; and secondly, that inany approxiinations are accomplished with the traffic and queue models used to develop these algorithms The advent of new traMic services means that tliese formulae must be continuously altered, and new forinulae developed Other adinission control methods, based on artificial intelligence systeins such as artificial neural networks (ANNs), have emerged as an alternative to analytical methods Thcsc neural admission controls have