We present the first experimental results from a new hybrid learning architecture for maze solving in mobile robotics which attempts to draw on the best ideas from the fields of both "traditional" AI world modelling and behaviour-based robotics. It can operate in both situated geocentric, and nonsituated egocentric modes. In situated mode it learns a "fuzzy cognitive map" of its environment in order to discover a near-optimal path between start and goal position of a particular maze. It is capable of abstracting nonsituated behaviours from a number of such situated learning experiences provided that they share some common features. Then in nonsituated mode it uses the acquired behaviours to navigate through new mazes using only local information.
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