Abstract.Simbad is an open source Java 3d robot simulator for scientific and educational purposes. It is mainly dedicated to researchers and programmers who want a simple basis for studying Situated Artificial Intelligence, Machine Learning, and more generally AI algorithms, in the context of Autonomous Robotics and Autonomous Agents. It is is kept voluntarily readable and simple for fast implementation in the field of Research and/or Education. Moreover, Simbad embeds two stand-alone additional packages : a Neural Network library (feed-forward NN, recurrent NN, etc.) and an Artificial Evolution Framework for Genetic Algorithm, Evolutionary Strategies and Genetic Programming. These packages are targeted towards Evolutionary Robotics. The Simbad
SUMMARYIP networks, and particularly the Internet, were proposed to be a simple and robust support for homogeneous communications. This implies that only basic control mechanisms have to be performed by network elements. Communication management has to be performed by the terminals. However, the integration of new services and the increasing need for QoS require the network to be increasingly more fl exible and adaptive. New algorithms and protocols have been proposed by many research teams to address these issues, but these new algorithms tend to make network management and control more fl exible. Thus, manual confi guration of such fl exible and adaptive network architectures is very complex, if not impossible. Self-management is then a good opportunity to address this new complexity, and then to integrate more easily new services into the network. However, this self-management requires the equipment to carry much more knowledge and information than the actual control and management planes do. Global knowledge management schemes are therefore necessary to achieve this, including new policies for knowledge gathering, computing, sharing and providing. To address this particular need for knowledge management, several studies have proposed building a new plane, called the 'Knowledge Plane' (KP). This paper studies different propositions for this KP, and presents an original vision of what this KP should be. Our vision of the KP relies on the paradigm of situatedness. This paradigm was developed by research studies in the fi eld of multi-agent systems, which tend to solve complex problems using collaborative and autonomous agents (multi-agent technology has been largely described in Artifi cial Intelligence literature). These agents in our proposition are embedded within the network elements themselves. Their role is to share local and situated knowledge composing the global KP. We have also developed, as an illustration, a distributed intrusion detection system (IDS) based on the local IDS Snort.
This paper addresses the problem of the acquisition of robot's behaviors for real environments. It insists on the interest of learning behaviors during robot's interaction with the environment under the control of a human tutor. The paper presents a learning model conceived to synthesize behaviors from a set of few examples and relying on a distributed representation of perception/action relations. The model is experienced on a real robot to learn a slalom task without giving any a priori information about the task or any element of the environment. The model exhibits properties that are well adapted to the interactive learning of concrete behaviors.
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