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Learning Classifier Systems (LCS) consist of the three components: function approximation , reinforcement learning, and classifier replacement. In this paper we formalize the function approximation part, by providing a clear problem definition, a formalization of the LCS function approximation architecture, and a definition of the function approximation… (More)

The development of the XCS Learning Classifier System has produced a robust and stable implementation that performs competitively in direct-reward environments. Although investigations in delayed-reward (i.e. multi-step) environments have shown promise, XCS still struggles to efficiently find optimal solutions in environments with long action-chains. This… (More)

It has been shown previously that the control of a robot arm can be efficiently learned using the XCSF learning classifier system, which is a nonlinear regression system based on evolutionary computation. So far, however, the predictive knowledge about how actual motor activity changes the state of the arm system has not been exploited. In this paper, we… (More)

The estimation of the classifier error plays a key role in accuracy-based learning classifier systems. In this paper we study the current definition of the classifier error in XCSF and discuss the limitations of the algorithm that is currently used to compute the classifier error estimate from online experience. Subsequently, we introduce a new definition… (More)

In this study we deal with the mixing problem, which concerns combining the prediction of independently trained local models to form a global prediction. We deal with it from the perspective of Learning Classifier Systems where a set of classifiers provide the local models. Firstly, we formalise the mixing problem and provide both analytical and heuristic… (More)

- Jan Drugowitsch
- 2007

Previous theoretical and experimental work on optimal decision-making was restricted to the artificial setting of a reliability of the momentary sensory evidence that remained constant within single trials. The work presented here describes the computation and characterization of optimal decision-making in the more realistic case of an evidence reliability… (More)

In this paper we explicitly identify the probabilistic model underlying LCS by linking it to a generalisation of the common Mixture-of-Experts model. Having an explicit representation of the model not only puts LCS on a strong statistical foundation and identifies the assumptions that the model makes about the data, but also allows us to use off-the-shelf… (More)

It was previously shown that the control of a robot arm can be efficiently learned using the XCSF classifier system. So far, however, the predictive knowledge about how actual motor activity changes the state of the arm system has not been exploited. In this paper, we exploit the forward velocity kinematics knowledge of XCSF to alleviate the negative effect… (More)