In this paper we present ATNoSFERES, a new framework based on an indirect encoding Genetic Algorithm which builds nite-state automata controllers able to deal with perceptual aliasing. We compare it with XCSM, a memory-based extension of the most studied Learning Classi er System, XCS, through a benchmark experiment. We then discuss the assets and drawbacks of ATNoSFERES in the context of that ...

The learning complexity of niche based learning classifier systems depends both on the complexity of the problem state space and on the number of available actions. In this paper, we introduce a version of XCS with computed actions, briefly XCSCA, that can be applied to problems involving a large number of actions. We report experimental results showing that XCSCA can evolve accurate and compac...

Missing data pose a potential threat to learning and classification in that they may compromise the ability of a system to develop robust, generalized models of the environment in which they operate. This investigation reports on the effects of three approaches to covering these data using an XCS-style learning classifier system. Using fabricated datasets representing a wide range of missing va...

Much further research is needed on explore/exploit strategies. The issue is crucial for true autonomy—con-sider the Mars robot or any system in a substantially unknown environment that cannot be teleoperated. This paper suggests that strategies based on error or its rate of change can permit systems to control the tradeoff autonomously. Major desiderata now are further experi-ments—to build up ...