Robotic Soccer is a multi-agent test bed, which requires the designer to address most of the issues of multi-agent research. Social insect behaviors observed in nature when adopted to solve problems they are giving promissing results. The domains like computers, electronics, electrical, mechanical etc., are inspired in adopting these behaviors. This paper addresses the ant intelligence in robotic soccer to evolve the best team of players. The simulation team evolved (PUTeam) was tested with teams of soccerbots in teambots (a simulation tool for Robotic Soccer) and the experimental results clearly shows the performance of the evolved team against the opponent teams are more effective.

Biometric based authentication systems use particular person characteristics which might be based on either behavior like voice, signature etc. or body structure like face, iris, palm print, fingerprint, etc. The performance of any unimodal biometric arrangement is depending on elements like surroundings, atmosphere, and sensor precision. Also, there are numerous trait unique demanding situations which include pose, expression, growing old and so forth for face reputation, occlusion and acquisition related problems for iris and terrible high-quality and social popularity related troubles for fingerprint. Hence, fusion of more than one biometric samples, traits or algorithms to achieve quality performance is another way to reap the better overall performance. In current scenario many researchers concentrating on Multimodal Biometrics with new fusion techniques ideas. We propose a new method of feature level fusion which uses Modified Adaptive Bilinear Interpolation (MABI) method to increase the resolution of data sample, which gives better features for fusion which gives more accurate results. In this work, experiment is done on AT&T face Cambridge University Computer Laboratory and MCYT signature Biometric Recognition Group datasets with combination of both unimodal and multimodal traits. K Nearest Neighbor (KNN) and Ensemble methods are used for classification. The proposed biometric system can be used in biometric surveillance, biometric screening for secured places, forensic applications etc.

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