In recent years, two major areas of computer science have started converging. Artificial intelligence research is moving towards realistic domains requiring real-time responses, and real-time systems are moving towards more complex applications requiring intelligent behaviour. This book addresses the question of whether agents can learn to become individually skilled and also learn to co-operate in the presence of both teammates and adversaries in a complex, real-time, noisy environment with no communication. To answer this question this work starts by presenting a multi-threaded agent architecture capable of dealing with the logical and timing challenges of such an environment. The decision making process is broken down into simple modules that link together an agent's perception to its actions. The book demonstrates how a sparse distributed memory model can be used as a generalisation component for tasks that involve large state spaces. It further demonstrates how reinforcement learning can be linked to such a memory model and produce intelligent action. Experimental results demonstrate how a learned policy can outperform fixed, hand-coded ones.
Learning to Co-operate in Multi-Agent Systems Experiments with the RoboCup Simulator
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In recent years, two major areas of computer science have started converging. Artificial intelligence research is moving towards realistic domains requiring real-time responses, and real-time systems are moving towards more complex applications requiring intelligent behaviour. This book addresses the question of whether agents can learn to become individually skilled and also learn to co-operate in the presence of both teammates and adversaries in a complex, real-time, noisy environment with no communication. To answer this question this work starts by presenting a multi-threaded agent architecture capable of dealing with the logical and timing challenges of such an environment. The decision making process is broken down into simple modules that link together an agent's perception to its actions. The book demonstrates how a sparse distributed memory model can be used as a generalisation component for tasks that involve large state spaces. It further demonstrates how reinforcement learning can be linked to such a memory model and produce intelligent action. Experimental results demonstrate how a learned policy can outperform fixed, hand-coded ones.
This volume presents revised and extended versions of selected papers presented at the Joint Workshop on Multi-Agent and Multi-Agent-Based Simulation, a workshop federated with the 3rd International...
The rapidity of change in education has intensified in recent years. With the emergence of 'co-operative schools' and a new framework focusing heavily on co-operation, a direct challenge to ways of...
The discovery and development of new computational methods have expanded the capabilities and uses of simulations. With agent-based models, the applications of computer simulations are significantly...
The book highlights new trends and challenges in research on agents and the new digital and knowledge economy. It includes papers on business process management, agent-based modeling and simulation...
This volume highlights new trends and challenges in research on agents and the new digital and knowledge economy, and includes 23 papers classified into the following categories: business process...
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