Titlebook: Balancing Reactivity and Social Deliberation in Multi-Agent Systems; From RoboCup to Real Markus Hannebauer,Jan Wendler,Enrico Pagello Conf

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HAC: A Unified View of Reactive and Deliberative Activity be accomplished, not the agents themselves, it is easy to incorporate multi-agent actions and planning into the architecture. In addition, HAC supports action abstraction, resource management, sensor integration, and is well suited to controlling large numbers of agents in dynamic environments. Unl

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A Hierarchy of Reactive Behaviors Handles Complexityll Size competition. Our reactive approach is based on control layers organized in a temporal hierarchy. Fast and simple behaviors reside on the bottom of the hierar- chy, while an increasing number of slower and more complex behaviors are implemented in the higher levels. In our architecture delibe

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Reinforcement Learning for Cooperating and Communicating Reactive Agents in Electrical Power Grids a purely reactive multi-agent system can learn to evolve cooperative behaviour, by means of learning from previous experiences. In particular, we describe a learning multi agent approach to the problem of controlling power flow in an electrical power-grid. The problem is formulated within the frame

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Deliberation Levels in Theoretic-Decision Approaches for Task Allocation in Resource-Bounded Agents their effect on the responsiveness of the system. Two architectures for task allocation and their level of deliberation are discussed. In both architectures the following holds: (1) Agents have limited resources and estimated distributions over resource execution tasks and (2) Agents create new tas

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HAC: A Unified View of Reactive and Deliberative Activitymechanisms used to write reactive actions can be used to implement a cognitive activity such as planning; and secondly, that the problem of integrating reactive and deliberative behavior itself can be viewed as having to simultaneously achieve multiple goals. Throughout the paper, we show how HAC an