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Abstract. In this paper, we discuss Pro t-sharing, an experience-based reinforcement learning approach (which is similar to a Monte-Carlo based reinforcement learning method) that can be used to learn robust and e ective actions within uncertain, dynamic, multi-agent s y s t e m s . W e i ntroduce the cut-loop routine that discards looping behavior, and demonstrate its e ectiveness empirically within a simpli ed NEO (non-combatant evacuation o p eration) domain. This domain consists of several agents which ferry groups of evacuees to one of several shelters. We demonstrate that the cut-loop routine makes the Pro t-sharing approach adaptive a n d robust within a dynamic and uncertain domain, without the need for prede ned knowledge or subgoals. We also compare it empirically with the popular Q-learning approach.

Abstract. Reasoning plays a central role in intelligent systems that operate in complex situations that involve time constraints. In this paper, we present the Adaptive Logic Interpreter, a reasoning system that acquires a controlled inference strategy adapted to the scenario at hand, using a variation on relational reinforcement learning. Employing this inference mechanism in a reactive agent architecture lets the agent focus its reasoning on the most rewarding parts of its knowledge base and hence perform better under time and computational resource constraints. We present experiments that demonstrate the benefits of this approach to reasoning in reactive agents, then discuss related work and directions for future research.

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