title: From a biological to a computational model for the autonomous behavior of an animat
abstract: Endowing an autonomous system like a robot with intelligent behavior is difficult for several reasons. First, behavior is such a wide topic that a general framework paradigm of inspiration must be chosen in order to obtain a consistent model. Such a framework can be, for example, biological modeling or an artificial intelligence approach. Second, a general framework is not sufficient to determine a fully specified program to be implemented in a robot. Many choices, tuning and tests must be carried out before obtaining a robust system. A biological model is presented, based on the definition of cortex-like automata, representing elementary functions in the perceptive, motor or associative domain. These automata are connected in a network whose architecture, functioning and learning rules are described in a cortical framework. Second, the computational model derived from that biological model is specified. The way units exchange and compute variables through links is explained, with reference to corresponding biological elements. It is then easier to report experiments allowing an autonomous system to learn regularities of a simple environment and to exploit them to satisfy some internal drives. Even if additional biological hints can be added, this model allow us to better understand how a biological model can be implemented and how biological properties can emerge from a distributed set of units
keyphrases: computational model autonomous behavior animat autonomous system robot intelligent behavior biological model tuning tests robust system cortex-like automata elementary functions associative domain architecture learning rules links simple environment internal drives perceptive domain motor domain variable computation variable exchange regularity learning