## A Concise Introduction to Multiagent Systems and Distributed Artificial IntelligenceMultiagent systems is an expanding field that blends classical fields like game theory and decentralized control with modern fields like computer science and machine learning. This monograph provides a concise introduction to the subject, covering the theoretical foundations as well as more recent developments in a coherent and readable manner. The text is centered on the concept of an agent as decision maker. Chapter 1 is a short introduction to the field of multiagent systems. Chapter 2 covers the basic theory of singleagent decision making under uncertainty. Chapter 3 is a brief introduction to game theory, explaining classical concepts like Nash equilibrium. Chapter 4 deals with the fundamental problem of coordinating a team of collaborative agents. Chapter 5 studies the problem of multiagent reasoning and decision making under partial observability. Chapter 6 focuses on the design of protocols that are stable against manipulations by self-interested agents. Chapter 7 provides a short introduction to the rapidly expanding field of multiagent reinforcement learning. The material can be used for teaching a half-semester course on multiagent systems covering, roughly, one chapter per lecture. |

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### Contents

II | 1 |

III | 3 |

IV | 5 |

VI | 7 |

IX | 8 |

X | 10 |

XI | 13 |

XII | 15 |

XXIV | 35 |

XXVII | 36 |

XXVIII | 39 |

XXIX | 40 |

XXX | 43 |

XXXI | 45 |

XXXIV | 49 |

XXXV | 50 |

XV | 16 |

XVI | 18 |

XVII | 19 |

XVIII | 21 |

XIX | 23 |

XX | 24 |

XXI | 25 |

XXII | 26 |

XXIII | 32 |

XXXVI | 52 |

XXXVII | 53 |

XL | 56 |

XLI | 59 |

XLII | 60 |

XLIII | 63 |

XLIV | 71 |

### Common terms and phrases

action sets actions of agent agent chooses agent knows argmax Artificial Intelligence assignment assume Bayesian game Bertsekas best-response function Boutilier Chapter choose an action collaborative agents common knowledge Conitzer convergence coordination game coordination graph defined Definition deterministic distributed Distributed Artificial Intelligence dominant strategies elimination order environment example fully observable game of Fig game theory global IESDA implement information set instance interact involve joint action machine learning Markov decision process Markov game Markov property max-plus maximize multiagent decision multiagent systems NOTES AND FURTHER number of agents observation model optimal action optimal Nash equilibrium optimal policy Osborne and Rubinstein outcome Pareto optimal Nash partial observability partitions payment functions payoff function possible predict prisoner's dilemma protocols puzzle Qi(s Qjlearning rational agent reinforcement learning revelation principle reward RoboCup robot soccer role sensor social choice function software agents solution concept strategic game strictly dominated actions subgame transition model value iteration variable elimination

### Popular passages

Page 66 - In Becker, S., Thrun, S., and Obermayer, K., editors, Advances in Neural Information Processing Systems 15, MIT Press, Cambridge, MA.

### References to this book

Software Agents, Surveillance, and the Right to Privacy: A Legislative ... Bart Willem Schermer No preview available - 2007 |