Sachiyo Arai

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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(More)
The point w e w ant t o m a k e in this paper is that Proot-sharingg a reinforcement learning approach i s v ery appropriate to realize the adap-tive behaviors in a multi-agent e n vironment. We discuss the eeectiveness of Proot-sharing theoretically and empirically within a Pursuit Game where there exist multiple preys and multiple hunters. In our context(More)
In this paper, we discuss Proot-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 eeective actions within uncertain, dynamic, multi-agent s y s t e m s. W e i n-troduce the cut-loop routine that discards looping behavior, and demonstrate(More)
In this paper, we i n troduce FirstVisit Proot-Sharing (FVPS) as a credit assignment procedure , an important issue in classiier systems and reinforcement learning frameworks. FVPS reinforces eeective rules to make a n agent acquire stochastic policies that cause it to behave v ery robustly within uncertain domains, without pre-deened knowledge or subgoals.(More)