Multiagent systems: algorithmic, game-theoretic, and logical foundations
Gespeichert in:
| Hauptverfasser: | , |
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| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
New York, NY
Cambridge University Press
2009
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| Schlagworte: | |
| Online-Zugang: | Contributor biographical information Publisher description Table of contents only Volltext Inhaltsverzeichnis |
| Beschreibung: | Literaturverzeichnis: Seite 459-471 |
| Beschreibung: | xx, 483 Seiten Diagramme |
| ISBN: | 9780521899437 |
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Contents Credits and Acknowledgments page xv Introduction xvii Distributed Constraint Satisfaction 1.1 Defining distributed constraint satisfaction problems 1.2 Domain-pruning algorithms 1.3 Heuristic search algorithms 1.3.1 The asynchronous backtracking algorithm 1.3.2 A simple example 1.3.3 An extended example: the four queens problem 1.3.4 Beyond the ABT algorithm 1.4 History and references 1 2 4 8 9 11 2 Distributed Optimization 2.1 Distributed dynamic programming for path planning 2.1.1 Asynchronous dynamic programming 2.1.2 Learning real-time A* 2.2 Action selection in multiagent MDPs 2.3 Negotiation, auctions, and optimization 2.3.1 From contract nets to auction-like optimization 2.3.2 The assignment problem and linear programming 2.3.3 The scheduling problem and integer programming 2.4 Social laws and conventions 2.5 History and references 19 19 19 21 23 27 27 29 36 43 45 3 Introduction to Noncooperative Game Theory: Games in Normal Form 3.1 Self-interested agents 3.1.1 Example: friends and enemies 3.1.2 Preferences and utility 3.2 Games in normal form 3.2.1 Example: the TCP user’s game 3.2.2 Definition of games in normal form 3.2.3 More examples of normal-formgames 3.2.4 Strategies in normal-form games 3.3 Analyzing games: from optimality to equilibrium 47 47 48 49 53 54 55 56 58 60 1 13 15 17 vii
Contents viii 3.3.1 Pareto optimality 3.3.2 Defining best responseand Nash equilibrium 3.3.3 Finding Nash equilibria 3.3.4 Nash’s theorem: proving the existence of Nash equilibria Further solution concepts for normal-form games 3.4.1 Maxmin and minmax strategies 3.4.2 Minimax regret 3.4.3 Removal of dominated strategies 3.4.4 Rationalizability 3.4.5 Con-elated equilibrium 3.4.6 Trembling-hand perfect equilibrium 3.4.7 e-Nash equilibrium History and references 60 61 62 64 71 72 75 77 79 81 83 83 85 Computing Solution Concepts of Normal-Form Games 4.1 Computing Nash equilibria of two-player, zero-sum games 4.2 Computing Nash equilibria of two-player, general-sum games 4.2.1 Complexity of computing a sample Nash equilibrium 4.2.2 An LCP formulation and the Lemke-Howson algorithm 4.2.3 Searching the space of supports 4.2.4 Beyond sample equilibrium computation 4.3 Computing Nash equilibria of n-player, general-sum games 4.4 Computing maxmin and minmax strategies for two-player, general-sum games 105 4.5 Identifying dominated strategies 4.5.1 Domination by a pure strategy 4.5.2 Domination by a mixed strategy 4.5.3 Iterated dominance 4.6 Computing correlated equilibria 4.7 History and references 87 87 89 89 91 99 101 102 3.4 3.5 4 5 106 106 107 109 110 111 Games with Sequential Actions: Reasoning and Computing with the Extensive Form 113 5.1 Perfect-information extensive-form games 113 5.1.1 Definition 113 5.1.2 Strategies and equilibria 115 5.1.3 Subgame-perfect equilibrium 117 5.1.4 Computing equilibria: backward induction 119 5.2 Imperfect-information extensive-form games 125
5.2.1 Definition 125 5.2.2 Strategies and equilibria 126 5.2.3 Computing equilibria: the sequence form 129 5.2.4 Sequential equilibrium 136 5.3 History and references 139
Contents 6 Richer Representations: Beyond the Normal and Extensive Forms 6.1 Repeated games 6.1.1 Finitely repeated games 6.1.2 Infinitely repeated games 6.1.3 “Bounded rationality”: repeatedgames played by automata 6.2 Stochastic games 6.2.1 Definition 6.2.2 Strategies and equilibria 6.2.3 Computing equilibria 6.3 Bayesian games 6.3.1 Definition 6.3.2 Strategies and equilibria 6.3.3 Computing equilibria 6.3.4 Ex post equilibrium 6.4 Congestion games 6.4.1 Definition 6.4.2 Computing equilibria 6.4.3 Potential games 6.4.4 Nonatomic congestion games 6.4.5 Selfish routing and the price of anarchy 6.5 Computationally motivated compact representations 6.5.1 The expected utility problem 6.5.2 Graphical games 6.5.3 Action-graph games 6.5.4 Multiagent influence diagrams 6.5.5 GALA 6.6 History and references 7 Learning and Teaching 7.1 Why the subject of “learning” is complex 7.1.1 The interaction between learning and teaching 7.1.2 What constitutes learning? 7.1.3 If learning is the answer, what isthe question? 7.2 Fictitious play 7.3 Rational learning 7.4 Reinforcement learning 7.4.1 Learning in unknown MDPs 7.4.2 Reinforcement learning in zero-sum stochastic games 7.4.3 Beyond zero-sum stochastic games 7.4.4 Belief-based reinforcement learning 7.5 No-regret learning and universal consistency 7.6 Targeted learning 7.7 Evolutionary learning and other large-population models 7.7.1 The replicator dynamic ix 141 142 143 144 147 153 153 154 155 156 157 160 163 165 166 166 167 168 170 172 176 177 179 181 183 186 187 189 189 189 190 192 195 200 204 205 205 208 209 209 211 212 213
Contents X 7.8 7.7.2 Evolutionarily stable strategies 7.7.3 Agent-based simulation and emergent conventions History and references 216 219 221 8 Communication 8.1 “Doing by talking” I: cheap talk 8.2 “Talking by doing”: signaling games 8.3 “Doing by talking” II: speech-act theory 8.3.1 Speech acts 8.3.2 Rules of conversation 8.3.3 A game-theoretic view of speech acts 8.3.4 Applications 8.4 History and references 223 223 227 229 229 230 232 235 238 9 Aggregating Preferences: Social Choice 9.1 Introduction 9.1.1 Example: plurality voting 9.2 A formal model 9.3 Voting 9.3.1 Voting methods 9.3.2 Voting paradoxes 9.4 Existence of social functions 9.4.1 Social welfare functions 9.4.2 Social choice functions 9.5 Ranking systems 9.6 History and references 241 241 241 242 244 244 246 247 248 251 255 258 10 Protocols for Strategic Agents: Mechanism Design 10.1 Introduction 10.1.1 Example: strategic voting 10.1.2 Example: buying a shortest path 10.2 Mechanism design with unrestricted preferences 10.2.1 Implementation 10.2.2 The revelation principle 10.2.3 Impossibility of general, dominant-strategy implementation 10.3 Quasilinear preferences 10.3.1 Risk attitudes 10.3.2 Mechanism design in the quasilinear setting 10.4 Efficient mechanisms 10.4.1 Groves mechanisms 10.4.2 The VCG mechanism 10.4.3 VCG and individual rationality 10.4.4 VCG and weak budget balance 10.4.5 Drawbacks of VCG 10.4.6 Budget balance and efficiency 261 261 261 262 263 264 265 267 268 269 271 276 276 280 282 283 284 288
Contents 10.5 10.6 10.7 10.8 xi 10.4.7 The AGV mechanism Beyond efficiency 10.5.1 What else can be implemented in dominant strategies? 10.5.2 Tractable Groves mechanisms Computational applications of mechanism design 10.6.1 Task scheduling 10.6.2 Bandwidth allocation in computer networks 10.6.3 Multicast cost sharing 10.6.4 Two-sided matching Constrained mechanism design 10.7.1 Contracts 10.7.2 Bribes 10.7.3 Mediators History and references 11 Protocols for Multiagent Resource Allocation: Auctions 11.1 Single-good auctions 11.1.1 Canonical auction families 11.1.2 Auctions as Bayesian mechanisms 11.1.3 Second-price, Japanese, and Englishauctions 11.1.4 First-price and Dutch auctions 11.1.5 Revenue equivalence 11.1.6 Risk attitudes 11.1.7 Auction variations 11.1.8 “Optimal” (revenue-maximizing) auctions 11.1.9 Collusion 11.1.10 Interdependent values 11.2 Multiunit auctions 11.2.1 Canonical auction families 11.2.2 Single-unit demand 11.2.3 Beyond single-unit demand 11.2.4 Unlimited supply: random samplingauctions 11.2.5 Position auctions 11.3 Combinatorial auctions 11.3.1 Simple combinatorial auction mechanisms 11.3.2 The winner determination problem 11.3.3 Expressing a bid: bidding languages 11.3.4 Iterative mechanisms 11.3.5 A tractable mechanism 11.4 Exchanges 11.4.1 Two-sided auctions 11.4.2 Prediction markets 11.5 History and references 288 289 290 292 293 294 296 298 301 307 308 309 310 311 315 315 316 318 319 321 323 326 327 328 330 333 336 336 337 340 342 344 346 348 349 352 357 359 361 361 362 364
xii Contents 12 Teams of Selfish Agents: An Introduction to Coalitional Game Theory 12.1 Coalitional games with transferable utility 12.1.1 Definition 12.1.2 Examples 12.1.3 Classes of coalitional games 12.2 Analyzing coalitional games 12.2.1 The Shapley value 12.2.2 The core 12.2.3 Refining the core: e-core,least core, and nucleolus 12.3 Compact representations of coalitional games 12.3.1 Weighted majority games and weighted voting games 12.3.2 Weighted graph games 12.3.3 Capturing synergies: a representation for superadditive games 384 12.3.4 A decomposition approach: multi-issue representation 12.3.5 A logical approach: marginal contribution nets 12.4 Further directions 12.4.1 Alternative coalitional game models 12.4.2 Advanced solution concepts 12.5 History and references 385 386 388 388 389 390 13 Logics of Knowledge and Belief 13.1 The partition model of knowledge 13.1.1 Muddy children and warring generals 13.1.2 Formalizing intuitions about the partition model 13.2 A detour to modal logic 13.2.1 Syntax 13.2.2 Semantics 13.2.3 Axiomatics 13.2.4 Modal logics with multiple modal operators 13.2.5 Remarks about first-order modal logic 13.3 55: An axiomatic theory of the partition model 13.4 Common knowledge, and an application to distributed systems 13.5 Doing time, and an application to robotics 13.5.1 Termination conditions for motion planning 13.5.2 Coordinating robots 13.6 From knowledge to belief 13.7 Combining knowledge and belief (and revisiting knowledge) 13.8 History and references 393 393 393 394 396 398 398 399 399 400 400 403 406 407 410 412 413 418 14 Beyond
Belief: Probability, Dynamics, and Intention 14.1 Knowledge and probability 14.2 Dynamics of knowledge and belief 14.2.1 Belief revision 421 421 425 426 367 367 368 368 370 371 372 374 378 381 381 382
Contents xiii 14.2.2 Beyond AGM: update, arbitration, fusion, and friends 14.2.3 Theories of belief change: a summary 14.3 Logic, games, and coalition logic 14.4 Towards a logic of “intention” 14.4.1 Some preformal intuitions 14.4.2 The road to hell: elements of a formal theory of intention 14.4.3 Group intentions 14.5 History and references 430 436 436 438 438 440 443 445 Appendices: Technical Background 447 A Probability Theory A.l Probabilistic models A.2 Axioms of probability theory A.3 Marginal probabilities A.4 Conditional probabilities 449 449 449 450 450 В Linear and Integer Programming В. 1 Linear programs B.2 Integer programs 451 451 453 C Markov Decision Problems (MDPs) C.l The model C.2 Solving known MDPs via value iteration 455 455 455 D Classical Logic D.l Propositional calculus D.2 First-order logic 457 457 458 Bibliography 459 Index 473 |
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| spelling | Shoham, Yoav Verfasser (DE-588)137137915 aut Multiagent systems algorithmic, game-theoretic, and logical foundations Yoav Shoham, Stanford University ; Kevin Leyton-Brown, University of British Columbia New York, NY Cambridge University Press 2009 xx, 483 Seiten Diagramme txt rdacontent n rdamedia nc rdacarrier Literaturverzeichnis: Seite 459-471 Datenverarbeitung Intelligent agents (Computer software) Electronic data processing Distributed processing Mehragentensystem (DE-588)4389058-1 gnd rswk-swf Mehragentensystem (DE-588)4389058-1 s DE-604 Leyton-Brown, Kevin 1975- Verfasser (DE-588)140354522 aut Erscheint auch als Online-Ausgabe 978-0-511-81165-4 (DE-604)BV043942893 http://www.loc.gov/catdir/enhancements/fy0834/2008012063-b.html Contributor biographical information http://www.loc.gov/catdir/enhancements/fy0834/2008012063-d.html Publisher description http://www.loc.gov/catdir/enhancements/fy0834/2008012063-t.html Table of contents only http://www.masfoundations.org/download.html kostenfrei Volltext Digitalisierung UB Passau - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=016657070&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Shoham, Yoav Leyton-Brown, Kevin 1975- Multiagent systems algorithmic, game-theoretic, and logical foundations Datenverarbeitung Intelligent agents (Computer software) Electronic data processing Distributed processing Mehragentensystem (DE-588)4389058-1 gnd |
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| title | Multiagent systems algorithmic, game-theoretic, and logical foundations |
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| title_exact_search | Multiagent systems algorithmic, game-theoretic, and logical foundations |
| title_exact_search_txtP | Multiagent systems algorithmic, game-theoretic, and logical foundations |
| title_full | Multiagent systems algorithmic, game-theoretic, and logical foundations Yoav Shoham, Stanford University ; Kevin Leyton-Brown, University of British Columbia |
| title_fullStr | Multiagent systems algorithmic, game-theoretic, and logical foundations Yoav Shoham, Stanford University ; Kevin Leyton-Brown, University of British Columbia |
| title_full_unstemmed | Multiagent systems algorithmic, game-theoretic, and logical foundations Yoav Shoham, Stanford University ; Kevin Leyton-Brown, University of British Columbia |
| title_short | Multiagent systems |
| title_sort | multiagent systems algorithmic game theoretic and logical foundations |
| title_sub | algorithmic, game-theoretic, and logical foundations |
| topic | Datenverarbeitung Intelligent agents (Computer software) Electronic data processing Distributed processing Mehragentensystem (DE-588)4389058-1 gnd |
| topic_facet | Datenverarbeitung Intelligent agents (Computer software) Electronic data processing Distributed processing Mehragentensystem |
| url | http://www.loc.gov/catdir/enhancements/fy0834/2008012063-b.html http://www.loc.gov/catdir/enhancements/fy0834/2008012063-d.html http://www.loc.gov/catdir/enhancements/fy0834/2008012063-t.html http://www.masfoundations.org/download.html http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=016657070&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| work_keys_str_mv | AT shohamyoav multiagentsystemsalgorithmicgametheoreticandlogicalfoundations AT leytonbrownkevin multiagentsystemsalgorithmicgametheoreticandlogicalfoundations |