Abstract.A generic predator/prey pursuit scenario is used to validate a common learning approach using Wilson's eXtended Learning Classifier System (XCS). The predators, having only local information, should independently learn and act while at the same time they are urged to collaborate and to capture the prey. Since learning from scratch is often a time consuming process, the common learning approach, as investigated here, is compared to an individual learning approach of selfish learning agents. A special focus is set on the performance of how quickly the team goal is achieved in both learning scenarios. This paper provides new insights of how agents with local information could learn collaboratively in a dynamically changing multi-agent environment. Furthermore, the concept of a common rule base based on Wilson's XCS is investigated. The results based on the common rule base approach show a significant speed up in the learning performance but may be significantly inferior on the long run, in particular in situations with a moving prey.Keywords: Multi-agent learning, predator/prey pursuit scenario, emergent behavior, collaboration, and XCS. MotivationDue to the increasing scale and complexity of strongly interconnected application systems there is a need for intelligent distributed information processing and control. The design of multi-agent systems (MASs) has addressed this need, using concepts from machine learning and distributed artificial intelligence [1]. MASs have been utilized successfully in a range of application scenarios: Guiding automated machines in collaborative industry scenarios [2], trading energy on market platforms [3], seeking smallest distance routes for delivery services [4], or managing air conditioners in buildings [5], are some examples of problems which are solved (completely or partially) using MAS approaches.A MAS consists of a collection of agents acting autonomously within their common environment in order to meet their objectives. They take sensory inputs from the environment, match them on actions, and then perform some actions, M. Hinchey et al. (Eds.): DIPES

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This paper provides an extension of the rule combining (RC) technique in the Accuracy-based Learning Classifier System (XCS) to handle continuous-valued input. Previously implemented to cope with binaries, the suitability of the newly introduced algorithm is investigated in further tasks. Several experiments are run and the results are compared to previous work using the real-valued multiplexer problem. The comparison shows that by implementing the RC technique, real value XCS is capable of producing a compact population of rules through proper generalizations. Moreover, the learning rate of Real-value XCS-RC (RXCS-RC) is comparable or even superior, in some cases.

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