Rosane Maria Maffei Vallim

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Mining data streams poses many challenges to existing Machine Learning algorithms. Algorithms designed to learn in this scenario need to constantly update their decision models in accordance with current data behavior. Therefore, the ability to detect when the behavior of the stream is changing is an important feature of any learning technique approaching(More)
Learning Classifier Systems (LCSs) are rule-based systems that can be manipulated by a genetic algorithm. LCSs were first designed by Holland to solve classification problems and a lot of effort has been made since then, resulting in a broad number of different algorithms. One of these is called Organizational Classifier System (OCS), a LCSs that tries to(More)
Learning Classifier Systems (LCSs) are a class of expert systems that use a knowledge base of decision rules and a genetic algorithm (GA) [9] as a discovery mechanism. The set of decision rules allows the LCS to represent and learn control strategies, while the robust search ability of the GA allows it to search for new rules based on the performance of(More)
Computer games are attracting increasing interest in the Artificial Intelligence (AI) research community, mainly because games involve reasoning, planning and learning. One area of particular interest in the last years is the creation of adaptive game AI. Adaptive game AI is the implementation of AI in computer games that holds the ability to adapt to(More)
The mining of data streams has been attracting much attention in the recent years, specially from Machine Learning researchers. One important task in learning from data streams is to correctly detect changing data characteristics over time, since this is critical to the correct modeling of data behavior. With the understanding that many applications(More)
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