Kamran Shafi

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Evolutionary Learning Classifier Systems (LCSs) combine reinforcement learning or supervised learning with effective genetics-based search techniques. Together these two mechanisms enable LCSs to evolve solutions to decision problems in the form of easy to interpret rules called classifiers. Although LCSs have shown excellent performance on some data mining(More)
Evolutionary Learning Classifier Systems (LCSs) are rule based systems that have been used effectively in concept learning. XCS is a prominent LCS that uses genetic algorithms and reinforcement learning techniques. In traditional machine learning, early stopping have been investigated extensively to an extent that it is now a default mechanism in many(More)
An emerging body of research is focusing on understanding and building artificial systems that can achieve open-ended development influenced by intrinsic motivations. In particular, research in robotics and machine learning is yielding systems and algorithms with increasing capacity for self-directed learning and autonomy. Traditional software architectures(More)
— Recently some algorithms have been proposed to clean post-training rule populations evolved by XCS, a state of the art Learning Classifier System (LCS). We present an algorithm to extract optimal rules, which we refer to as signatures, during the operation of UCS, a recent variant of XCS. In a benchmark binary valued dataset our method seconds the(More)
—Optimization problems under uncertainty have received considerable attention in recent years due to their practical implications. In real-world applications, a problem is usually confronted with multiple types of uncertainties that are incommensurable with each other. Decision makers in the real-world do not trade-off objectives alone, but also and more(More)
Adversarial learning is a recently introduced term which refers to the machine learning process in the presence of an adversary whose main goal is to cause dysfunction to the learning machine. The key problem in adversarial learning is to determine when and how an adversary will launch its attacks. It is important to equip the deployed machine learning(More)