Learning to Co-operate in Multi-Agent Systems Experiments with the RoboCup Simulator

Name: Learning to Co-operate in Multi-Agent Systems Experiments with the RoboCup Simulator
Brand: VDM Verlag
SKU: 9783639102598
Price: 22291 HUF
Availability: InStock

Szerző: Kostas Kostiadis

Nyelv:

Kötés: Puha kötésű

Kiadó: VDM Verlag

Elérhetőség: Beszállítói készleten

Küldés 14-21 napon belül

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In recent years, two major areas of computer science§have started converging. Artificial intelligenc...

Információk a könyvről

Szerző

Kostas Kostiadis

Nyelv

Angol

Kötés

Könyv - Puha kötésű

Kiadva

2008

oldal

156

EAN

9783639102598

ISBN

3639102592

Enbook ID

06819249

Kiadó

VDM Verlag

Súly

218

Méretek

152 x 229 x 8

Teljes leírás

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. 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.

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Learning to Co-operate in Multi-Agent Systems Experiments with the RoboCup Simulator

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