LEADER 00875nam0-22003371i-450- 001 990003134310403321 005 20091127111908.0 010 $a0-631-15271-7 035 $a000313431 035 $aFED01000313431 035 $a(Aleph)000313431FED01 035 $a000313431 100 $a20030910d1989----km-y0itay50------ba 101 0 $aeng 102 $aUS 200 1 $aModern Business Cycle Theory$fedited by Robert J.Barro. 205 $a1. ed. 210 $aNew York$cBasil Blackell$d1989. 215 $a337 p.$d23 cm 676 $aB/1.2 676 $aF/5 702 1$aBarro,$bRobert J.$f<1944- > 801 0$aIT$bUNINA$gRICA$2UNIMARC 901 $aBK 912 $a990003134310403321 952 $aE3.157$b4174$fDECTS 952 $aB/1.2.2 BAR$b7981$fSES 959 $aDECTS 959 $aSES 996 $aModern Business Cycle Theory$9456950 997 $aUNINA LEADER 04682nam 22006135 450 001 9910768468003321 005 20251226202212.0 010 $a3-540-33059-3 024 7 $a10.1007/11691839 035 $a(CKB)1000000000232881 035 $a(SSID)ssj0000318637 035 $a(PQKBManifestationID)11211588 035 $a(PQKBTitleCode)TC0000318637 035 $a(PQKBWorkID)10310453 035 $a(PQKB)11014580 035 $a(DE-He213)978-3-540-33059-2 035 $a(MiAaPQ)EBC3067578 035 $a(PPN)123132673 035 $a(EXLCZ)991000000000232881 100 $a20100301d2006 u| 0 101 0 $aeng 135 $aurnn|008mamaa 181 $ctxt 182 $cc 183 $acr 200 10$aLearning and Adaption in Multi-Agent Systems $eFirst International Workshop, LAMAS 2005, Utrecht, The Netherlands, July 25, 2005, Revised Selected Papers /$fedited by Karl Tuyls, Pieter Jan 't Hoen, Katja Verbeeck, Sandip Sen 205 $a1st ed. 2006. 210 1$aBerlin, Heidelberg :$cSpringer Berlin Heidelberg :$cImprint: Springer,$d2006. 215 $a1 online resource (X, 217 p.) 225 1 $aLecture Notes in Artificial Intelligence,$x2945-9141 ;$v3898 300 $a"This book contains selected and revised papers of the International Workshop on Learning and Adaptation in Multi-Agent Systems (LAMAS 2005), held at the AAMAS 2005 Conference"--Pref. 311 08$a3-540-33053-4 320 $aIncludes bibliographical references and index. 327 $aAn Overview of Cooperative and Competitive Multiagent Learning -- Multi-robot Learning for Continuous Area Sweeping -- Learning Automata as a Basis for Multi Agent Reinforcement Learning -- Learning Pareto-optimal Solutions in 2x2 Conflict Games -- Unifying Convergence and No-Regret in Multiagent Learning -- Implicit Coordination in a Network of Social Drivers: The Role of Information in a Commuting Scenario -- Multiagent Traffic Management: Opportunities for Multiagent Learning -- Dealing with Errors in a Cooperative Multi-agent Learning System -- The Success and Failure of Tag-Mediated Evolution of Cooperation -- An Adaptive Approach for the Exploration-Exploitation Dilemma and Its Application to Economic Systems -- Efficient Reward Functions for Adaptive Multi-rover Systems -- Multi-agent Relational Reinforcement Learning -- Multi-type ACO for Light Path Protection. 330 $aThis book contains selected and revised papers of the International Workshop on Lea- ing and Adaptation in Multi-Agent Systems (LAMAS 2005), held at the AAMAS 2005 Conference in Utrecht, The Netherlands, July 26. An important aspect in multi-agent systems (MASs) is that the environment evolves over time, not only due to external environmental changes but also due to agent int- actions. For this reason it is important that an agent can learn, based on experience, and adapt its knowledge to make rational decisions and act in this changing environment autonomously. Machine learning techniques for single-agent frameworks are well established. Agents operate in uncertain environments and must be able to learn and act - tonomously. This task is, however, more complex when the agent interacts with other agents that have potentially different capabilities and goals. The single-agent case is structurally different from the multi-agent case due to the added dimension of dynamic interactions between the adaptive agents. Multi-agent learning, i.e., the ability of the agents to learn how to cooperate and compete, becomes crucial in many domains. Autonomous agents and multi-agent systems (AAMAS) is an emerging multi-disciplinary area encompassing computer science, software engineering, biology, as well as cognitive and social sciences. A t- oretical framework, in which rationality of learning and interacting agents can be - derstood, is still under development in MASs, although there have been promising ?rst results. 410 0$aLecture Notes in Artificial Intelligence,$x2945-9141 ;$v3898 606 $aArtificial intelligence 606 $aComputer networks 606 $aArtificial Intelligence 606 $aComputer Communication Networks 615 0$aArtificial intelligence. 615 0$aComputer networks. 615 14$aArtificial Intelligence. 615 24$aComputer Communication Networks. 676 $a006.3 701 $aTuyls$b Karl$01224423 712 12$aInternational Workshop on Learning and Adaptation in Multi-Agent Systems 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910768468003321 996 $aLearning and adaption in multi-agent systems$94187876 997 $aUNINA