Drew Mellor

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Motivated by the intention to increase the expressive power of learning classifier systems, we developed a new Xcs derivative, Fox-cs, where the classifier and observation languages are a subset of first order logic. We found that Fox-cs was viable at tasks in two relational task domains, poker and blocks world, which cannot be represented easily using(More)
BACKGROUND Several lines of evidence suggest that transcription factors are involved in the pathogenesis of Multiple Sclerosis (MS) but complete mapping of the whole network has been elusive. One of the reasons is that there are several clinical subtypes of MS and transcription factors that may be involved in one subtype may not be in others. We investigate(More)
Therapies consisting of a combination of agents are an attractive proposition, especially in the context of diseases such as cancer, which can manifest with a variety of tumor types in a single case. However uncovering usable drug combinations is expensive both financially and temporally. By employing computational methods to identify candidate combinations(More)
Profiling of the learning classifier system XCS [11] has revealed that its execution time tends to be dominated by rule matching [8], it is therefore important for rule matching to be efficient. To date, the fastest speedups for matching have been achieved by exploiting parallelism [8], but efficient sequential approaches, such as bitset and "specificity"(More)
Hex is a challenging strategy board game for two players. To enhance students' progress in acquiring understanding and practical experience with complex machine intelligence and programming concepts we developed the Machine Intelligence Hex (MIHex) project. The associated undergraduate student assignment is about designing and implementing Hex players and(More)
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