000 04151nam a22006015i 4500
001 978-3-540-32274-0
003 DE-He213
005 20160624101925.0
007 cr nn 008mamaa
008 100705s2005 gw | s |||| 0|eng d
020 _a9783540322740
_9978-3-540-32274-0
024 7 _a10.1007/b106974
_2doi
050 4 _aQ334-342
050 4 _aTJ210.2-211.495
072 7 _aUYQ
_2bicssc
072 7 _aTJFM1
_2bicssc
072 7 _aCOM004000
_2bisacsh
082 0 4 _a006.3
_223
245 1 0 _aAdaptive Agents and Multi-Agent Systems II
_h[electronic resource] :
_bAdaptation and Multi-Agent Learning /
_cedited by Daniel Kudenko, Dimitar Kazakov, Eduardo Alonso.
260 1 _aBerlin, Heidelberg :
_bSpringer Berlin Heidelberg,
_c2005.
264 1 _aBerlin, Heidelberg :
_bSpringer Berlin Heidelberg,
_c2005.
300 _aVIII, 313 p. Also available online.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aLecture Notes in Computer Science,
_x0302-9743 ;
_v3394
505 0 _aGödel Machines: Towards a Technical Justification of Consciousness -- Postext – A Mind for Society -- Comparing Resource Sharing with Information Exchange in Co-operative Agents, and the Role of Environment Structure -- Baselines for Joint-Action Reinforcement Learning of Coordination in Cooperative Multi-agent Systems -- SMART (Stochastic Model Acquisition with ReinforcemenT) Learning Agents: A Preliminary Report -- Towards Time Management Adaptability in Multi-agent Systems -- Learning to Coordinate Using Commitment Sequences in Cooperative Multi-agent Systems -- Reinforcement Learning of Coordination in Heterogeneous Cooperative Multi-agent Systems -- Evolving the Game of Life -- The Strategic Control of an Ant-Based Routing System Using Neural Net Q-Learning Agents -- Dynamic and Distributed Interaction Protocols -- Advice-Exchange Between Evolutionary Algorithms and Reinforcement Learning Agents: Experiments in the Pursuit Domain -- Evolving Strategies for Agents in the Iterated Prisoner’s Dilemma in Noisy Environments -- Experiments in Subsymbolic Action Planning with Mobile Robots -- Robust Online Reputation Mechanism by Stochastic Approximation -- Learning Multi-agent Search Strategies -- Combining Planning with Reinforcement Learning for Multi-robot Task Allocation -- Multi-agent Reinforcement Learning in Stochastic Single and Multi-stage Games -- Towards Adaptive Role Selection for Behavior-Based Agents.
520 _aAdaptive agents and multi-agent systems is an emerging and exciting interdisciplinary area of research and development involving artificial intelligence, software engineering, and developmental biology, as well as cognitive and social science. This book presents 17 revised and carefully reviewed papers taken from two workshops on the topic as well as 2 invited papers by leading researchers in the area. The papers deal with various aspects of machine learning, adaptation, and evolution in the context of agent systems and autonomous agents.
650 0 _aComputer science.
650 0 _aComputer Communication Networks.
650 0 _aSoftware engineering.
650 0 _aLogic design.
650 0 _aArtificial intelligence.
650 1 4 _aComputer Science.
650 2 4 _aArtificial Intelligence (incl. Robotics).
650 2 4 _aSoftware Engineering.
650 2 4 _aComputer Communication Networks.
650 2 4 _aLogics and Meanings of Programs.
650 2 4 _aProgramming Languages, Compilers, Interpreters.
650 2 4 _aUser Interfaces and Human Computer Interaction.
700 1 _aKudenko, Daniel.
_eeditor.
700 1 _aKazakov, Dimitar.
_eeditor.
700 1 _aAlonso, Eduardo.
_eeditor.
710 2 _aSpringerLink (Online service)
773 0 _tSpringer eBooks
776 0 8 _iPrinted edition:
_z9783540252603
786 _dSpringer
830 0 _aLecture Notes in Computer Science,
_x0302-9743 ;
_v3394
856 4 0 _uhttp://dx.doi.org/10.1007/b106974
942 _2EBK3940
_cEBK
999 _c33234
_d33234