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We have developed GAMI, an approach to motif inference that uses a genetic algorithms search and is designed specifically to work with divergent species and possibly long nucleotide sequences. The system design reduces the size of the search space as compared to typical window-location approaches for motif inference. This paper describes the motivation and… (More)
Cognitive models and intelligent agents are becoming more complex and pervasive. It is time again to consider high-level behavior representation languages and development environments that make it easier to create, share, and reuse cognitive models. One of these languages is Herbal, a high-level behavior representation language. Users represent knowledge in… (More)
This paper introduces RCS, a learning classifier system designed for evolutionary robotics research. In addition to describing the system, it will present the results of RCS applied to a pursuit task. In this test, performance was good and has been improved in ongoing work.
We propose a new method for collecting information on regulatory elements found by any motif discovery program. We suggest that combining the results of n leave-one-out motif discovery runs provides additional information. By examining motifs found in n - 1 of the sequences and scoring them on the remaining sequence, we overcome some of the issues arising… (More)
This paper describes an experiment to evolve a predator-and-prey system where the primary input for each robot is a linear camera. The method of learning is a Learning Classifier System. It builds on similar work implemented with an evolved neural network, in which both predator and prey behaviors were learned based mostly upon camera input. It is designed… (More)