We present an online technique to estimate the generality of classifiers conditions. We show that this technique can be extended to gather some basic information about the effect of classifier actions in the environment. The approach we present is minimalist in that it is aimed at obtaining as much information as possible from online experience, with as few modifications as possible to the clas...

Many robotic sensor estimation problems can characterized in terms of nonlinear measurement systems. These systems are contaminated with noise and may be underdetermined from a single observation. In order to get reliable estimation results, the system must choose views which result in an overdetermined system. This is the sensor control problem. Accurate and reliable sensor control requires an...

Recent findings in the domain of combining classifiers provide a surprising revision of the usefulness of diversity for modelling combined performance. Although there is a common agreement that a successful fusion system should be composed of accurate and diverse classifiers, experimental results show very weak correlations between various diversity measures and combining methods. Effectively n...

Pattern recognition systems are increasingly being used in adversarial environments like network intrusion detection, spam filtering and biometric authentication and verification systems, in which an adversary may adaptively manipulate data to make a classifier ineffective. Current theory and design methods of pattern recognition systems do not take into account the adversarial nature of such k...

In this study, we propose a new approach to realize a reinforcement learning scheme for heterogeneous multiagent systems. In our approach, we treat the collective agents systems in which there are multiple autonomous mobile robots, and given tasks are achieved based on the collective behavior approach. Also, each agent organizes and refines its knowledge for executing its own behaviors by reinf...

Extended Abstract The classifier system framework is a general-purpose approach to learning and representation designed to exhibit non-brittle behavior in complex, continually varying environments. Broadly speaking, classifier systems are expected to avoid brittle behavior because they implement processes that build and refine models of the environment. One of the most important of these proces...

In adversarial classification tasks like spam filtering, intrusion detection in computer networks and biometric authentication, a pattern recognition system must not only be accurate, but also robust to manipulations of input samples made by an adversary to mislead the system itself. It has been recently argued that the robustness of a classifier could be improved by avoiding to overemphasize o...

Learning Classifier Systems (LCSs) are rule-based systems that automatically build their ruleset. At the origin of Holland’s work, LCSs were seen as a model of the emergence of cognitive abilities thanks to adaptive mechanisms, particularly evolutionary processes. After a renewal of the field more focused on learning, LCSs are now considered as sequential decision problem-solving systems endowe...

Symbiosis is the phenomenon in which organisms of different species live together in close association, resulting in a raised level of fitness for one or more of the organisms. Symbiogenesis is the name given to the process by which symbiotic partners combine and unify, that is, become genetically linked, giving rise to new morphologies and physiologies evolutionarily more advanced than their c...