DOCSLIB.ORG

Enhancing AI-Based Game Playing Using Adaptive Data Structures

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Enhancing AI-Based Game Playing Using Adaptive Data Structures Enhancing AI-based Game Playing Using Adaptive Data Structures By Spencer Polk A thesis submitted to the Faculty of Graduate Studies and Research in partial fulfilment of the requirements for the degree of Doctor of Philosophy Ottawa-Carleton Institute for Computer Science School of Computer Science Carleton University Ottawa, Ontario June 2016 c Copyright 2016, Spencer Polk The undersigned hereby recommend to the Faculty of Graduate Studies and Research acceptance of the thesis, Enhancing AI-based Game Playing Using Adaptive Data Structures submitted by Spencer Polk Dr. Douglas Howe (Director, School of Computer Science) Dr. B. John Oommen (Thesis Supervisor) (External Examiner) Carleton University June 2016 ii Abstract The design and analysis of strategies for playing strategic board games, is a core area of Artificial Intelligence (AI), that has been studied extensively since the in- ception of the field. However, while two-player board games are very well known, comparatively little research has been done on Multi-Player games, where the num- ber of self-interested, competing players is greater than two. Furthermore, known strategies for multi-player games have difficulties performing on a level of sophistica- tion comparable to their two-player counterparts. When progress in a field is stymied, promising approaches can be discovered through drawing inspiration from other, completely unrelated areas of Computer Sci- ence. The premise of this thesis is the hypothesis that game playing in general, and the problem of multi-player games in particular, can benefit from efficient ranking mechanisms, for moves, board positions, or even players, in the multi-player scenario. The research done in this work confirms the hypothesis. Indeed, we have discov- ered that this information can be applied to improve game tree pruning, through the concept of move ordering, and within other possibilities. In this thesis, we observe that the formerly-unrelated field of Adaptive Data Structures (ADSs), which provide mechanisms by which a data structure can reorganize itself internally in response to queries, can provide a natural ranking mechanism. The primary motivation of this thesis is to demonstrate that the low-cost ADS-based data structures can provide this ranking mechanism to game-playing engines, and furthermore generate statistically significant improvements to their efficiency. In this work, we will conclusively prove that ADS-based techniques are able to enhance existing multi-player game playing strategies, and perform competitively with state-of-the-art two-player techniques, as well. We demonstrate, through two general-use, domain independent move ordering heuristics, the Threat-ADS heuristic for multi-player games, and the History-ADS heuristic for both two-player and multi- player games, that ADSs are, indeed, capable of achieving this improvement. We present an examination of their performance in a very wide range of game models, configurations, and by employing differing ADSs and refinements. We thus conclu- sively demonstrate that ADSs are able to achieve strong performance, in game playing engines, in the vast majority of cases. Our work in this thesis provides not only these domain-independent, formal move ordering heuristics, but furthermore serves as a strong example for future investigation into combinations between the fields of ADSs and game playing. ii Dedicated with all my love to my parents and grandparents Nancy, Jeff, Elaine, and Leo Acknowledgements Above all, I express my utmost gratitude to my thesis advisor, Professor John Oommen, for his continuing patience, support, and kindness during this part of my voyage through life. Without his encouragement throughout this process, I would be looking back on what I had done with doubt, and forward to my future in academia and beyond, with fear. Through his generous guidance and advice, the experience has rather been one of exploration and discovery. I would also like to sincerely thank him for providing me with the opportunity to augment my studies through lecturing. I will be grateful to him for all he has done for me for the rest of my life. I would like to thank the members of the thesis proposal committee, for their feedback on earlier drafts of this work. I am grateful for their experienced advice, which has helped guide me in my research and the design of my experiments, as well as the formulation of this final work. I would also like to express my thanks to my co-workers and and supervisors at Purple Forge for allowing me unprecedented leeway in shaping my work schedule to fit my academic goals. I would like to especially thank the founder and CEO, Brian Hurley, who not only accommodated my unpredictable work/school schedule, but also provided me with valuable advice on the commercial applications of my research in the industry, as well as an opportunity to experience “real-world” AI systems. I am grateful to the School of Computer Science, and its administrative staff, and faculty, for all of their support throughout these long years. In particular, I am thankful to Dr. Douglas Howe, for providing me with the opportunity to teach a course during my time as a graduate student. Finally, I thank my family for encouraging and supporting me well beyond any- thing I could have asked for through this process, and for showing me infinite patience when it concerned my tendency to ramble on about my research. I am sincerely grate- ful for being shown such love and patience. Thank you all from the bottom of my heart! ii Contents 1 Introduction 1 1.1 Problem Overview and Motivation . 2 1.2 ProblemApproach ............................ 4 1.2.1 Verification of the Approach . 7 1.2.2 Complexity of the Verification . 7 1.3 ContributionsoftheThesis ....................... 9 1.4 StructureoftheThesis .......................... 10 2 Survey of the Field 13 2.1 ChapterOverview............................. 13 2.2 Families of Games . 14 2.3 Game Playing: Overview . 17 2.3.1 Historical Overview . 17 2.3.2 GameTrees ............................ 18 i 2.3.3 Branch and Bound . 19 2.4 Two-Player Game Playing . 20 2.4.1 The Mini-Max Algorithm . 20 2.4.2 Alpha-Beta Search . 21 2.4.3 MoveOrdering .......................... 24 2.4.3.1 Killer Moves and the History Heuristic . 25 2.4.4 Other Alpha-Beta Related Techniques . 27 2.4.4.1 Transposition Tables . 27 2.4.4.2 The Horizon Effect and Quiescence Search . 29 2.4.4.3 Stochastic Improvements . 29 2.4.4.4 Handling Imperfect Information . 30 2.4.5 Non-Alpha-Beta Strategies for Two-Player Games . 31 2.5 Multi-Player Game Playing . 33 2.5.1 Challenges of Multi-Player Games . 33 2.5.2 The Paranoid Algorithm . 34 2.5.3 The Max-N Algorithm . 36 2.5.3.1 Extensions to Max-N . 38 2.5.4 The Best-Reply Search . 39 2.6 StochasticMethods............................ 41 2.6.1 MonteCarloTreeSearch. .. .. 42 ii 2.6.2 The UCT Algorithm . 43 2.7 The Improving Agent: Adaptive Data Structures . 44 2.7.1 OverviewofADSs ........................ 44 2.7.2 List-Based ADSs . 46 2.7.2.1 Move-to-Front ..................... 46 2.7.2.2 Transposition . 47 2.7.2.3 Move-Ahead-k ..................... 48 2.7.2.4 POS(k) . 49 2.7.2.5 Stochastic Variants . 50 2.7.3 Tree-BasedADSs ......................... 51 2.8 ChapterSummary ............................ 53 3 Improving the Best-Reply Search using ADS 54 3.1 ChapterOverview............................. 54 3.2 MoveOrdering .............................. 55 3.2.1 Benefits of Move Ordering . 55 3.2.2 Examples of Move Ordering . 56 3.3 OpponentThreatLevel.......................... 58 3.3.1 PropertiesofOpponentThreat . 59 3.3.2 OpponentThreatandMoveOrdering . 60 iii 3.4 Managing Opponent Threat Level Using an ADS . 62 3.5 TheThreat-ADSHeuristic ........................ 67 3.5.1 Developing the Threat-ADS Heuristic . 67 3.5.2 Salient Features of the Threat-ADS Heuristic . 69 3.6 ExperimentalModel ........................... 71 3.7 Prima Facie Experiments ........................ 73 3.8 VariationontheNumberofPlayers . 75 3.8.1 Results............................... 76 3.9 VariationontheStartingStateoftheGame . 78 3.9.1 Results............................... 79 3.10EvaluationofResults........................... 82 3.11ChapterConclusions ........................... 85 4 Improvements to the Threat-ADS Heuristic 86 4.1 ChapterOverview............................. 86 4.2 OpenQuestions.............................. 87 4.3 Threat-ADS’ Behaviour Using Different ADSs . 88 4.4 ErgodicversusAbsorbingADSs . 90 4.5 Investigating the Threat-ADS’ Performance at Different Ply Levels . 92 4.6 ExperimentalModel ........................... 93 iv 4.7 Initial Board Position Experiments . 95 4.8 Midgame Starting Position Experiments . 99 4.9 Discussion................................. 102 4.10 ChapterConclusions ........................... 105 5 History-Based Move Ordering using ADSs 106 5.1 ChapterOverview............................. 106 5.2 Motivation................................. 107 5.3 MoveOrderingUsingMoveHistory . 108 5.4 ManagingMoveHistoryUsinganADS . 109 5.5 TheHistory-ADSHeuristic. 112 5.5.1 Developing the History-ADS Heuristic . 112 5.5.2 Salient Features of the History-ADS Heuristic . 113 5.6 ExperimentalModel ........................... 115 5.7 ResultsforTwo-PlayerGames. 118 5.8 Results for Multi-Player Games . 121 5.9 Discussion................................. 123 5.10 ChapterConclusions ........................... 126 6 Refinements of the History-ADS Heuristic 127 6.1 ChapterOverview............................. 127 v 6.2 OpenQuestions.............................
Load more

Recommended publications
  • Minimax TD-Learning with Neural Nets in a Markov Game
    Minimax TD-Learning with Neural Nets in a Markov Game Fredrik A. Dahl and Ole Martin Halck Norwegian Defence Research Establishment (FFI) P.O. Box 25, NO-2027 Kjeller, Norway {Fredrik-A.Dahl, Ole-Martin.Halck}@ffi.no Abstract. A minimax version of temporal difference learning (minimax TD- learning) is given, similar to minimax Q-learning. The algorithm is used to train a neural net to play Campaign, a two-player zero-sum game with imperfect information of the Markov game class. Two different evaluation criteria for evaluating game-playing agents are used, and their relation to game theory is shown. Also practical aspects of linear programming and fictitious play used for solving matrix games are discussed. 1 Introduction An important challenge to artificial intelligence (AI) in general, and machine learning in particular, is the development of agents that handle uncertainty in a rational way. This is particularly true when the uncertainty is connected with the behavior of other agents. Game theory is the branch of mathematics that deals with these problems, and indeed games have always been an important arena for testing and developing AI. However, almost all of this effort has gone into deterministic games like chess, go, othello and checkers. Although these are complex problem domains, uncertainty is not their major challenge. With the successful application of temporal difference learning, as defined by Sutton [1], to the dice game of backgammon by Tesauro [2], random games were included as a standard testing ground. But even backgammon features perfect information, which implies that both players always have the same information about the state of the game.
    [Show full text]
  • Bayesian Games Professors Greenwald 2018-01-31
    Bayesian Games Professors Greenwald 2018-01-31 We describe incomplete-information, or Bayesian, normal-form games (formally; no examples), and corresponding equilibrium concepts. 1 A Bayesian Model of Interaction A Bayesian, or incomplete information, game is a generalization of a complete-information game. Recall that in a complete-information game, the game is assumed to be common knowledge. In a Bayesian game, in addition to what is common knowledge, players may have private information. This private information is captured by the notion of an epistemic type, which describes a player’s knowledge. The Bayesian-game formalism makes two simplifying assumptions: • Any information that is privy to any of the players pertains only to utilities. In all realizations of a Bayesian game, the number of players and their actions are identical. • Players maintain beliefs about the game (i.e., about utilities) in the form of a probability distribution over types. Prior to receiving any private information, this probability distribution is common knowledge: i.e., it is a common prior. After receiving private information, players conditional on this information to update their beliefs. As a consequence of the com- mon prior assumption, any differences in beliefs can be attributed entirely to differences in information. Rational players are again assumed to maximize their utility. But further, they are assumed to update their beliefs when they obtain new information via Bayes’ rule. Thus, in a Bayesian game, in addition to players, actions, and utilities, there is a type space T = ∏i2[n] Ti, where Ti is the type space of player i.There is also a common prior F, which is a probability distribution over type profiles.
    [Show full text]
  • Outline for Static Games of Complete Information I
    Outline for Static Games of Complete Information I. Definition of a game II. Examples III. Definition of Nash equilibrium IV. Examples, continued V. Iterated elimination of dominated strategies VI. Mixed strategies VII. Existence theorem on Nash equilibria VIII. The Hotelling model and extensions Copyright © 2004 by Lawrence M. Ausubel Definition: An n-player, static game of complete information consists of an n-tuple of strategy sets and an n-tuple of payoff functions, denoted by G = {S1, … , Sn; u1, … , un} Si, the strategy set of player i, is the set of all permissible moves for player i. We write si ∈ Si for one of player i’s strategies. ui, the payoff function of player i, is the utility, profit, etc. for player i, and depends on the strategies chosen by all the players: ui(s1, … , sn). Example: Prisoners’ Dilemma Prisoner II Remain Confess Silent Remain –1 , –1 –5 , 0 Prisoner Silent I Confess 0, –5 –4 ,–4 Example: Battle of the Sexes F Boxing Ballet Boxing 2, 1 0, 0 M Ballet 0, 0 1, 2 Definition: A Nash equilibrium of G (in pure strategies) consists of a strategy for every player with the property that no player can improve her payoff by unilaterally deviating: (s1*, … , sn*) with the property that, for every player i: ui (s1*, … , si–1*, si*, si+1*, … , sn*) ≥ ui (s1*, … , si–1*, si, si+1*, … , sn*) for all si ∈ Si. Equivalently, a Nash equilibrium is a mutual best response. That is, for every player i, si* is a solution to: ***** susssssiiiiin∈arg max{ (111 , ... ,−+ , , , ... , )} sSii∈ Example: Prisoners’ Dilemma Prisoner II
    [Show full text]
  • Efficiency and Welfare in Economies with Incomplete Information∗
    Efficiency and Welfare in Economies with Incomplete Information∗ George-Marios Angeletos Alessandro Pavan MIT, NBER, and FRB of Minneapolis Northwestern University September 2005. Abstract This paper examines a class of economies with externalities, strategic complementarity or substitutability, and incomplete information. We first characterize efficient allocations and com- pare them to equilibrium. We show how the optimal degree of coordination and the efficient use of information depend on the primitives of the environment, and how this relates to the welfare losses due to volatility and heterogeneity. We next examine the social value of informa- tion in equilibrium. When the equilibrium is efficient, welfare increases with the transparency of information if and only if agents’ actions are strategic complements. When the equilibrium is inefficient, additional effects are introduced by the interaction of externalities and informa- tion. We conclude with a few applications, including investment complementarities, inefficient fluctuations, and market competition. Keywords: Social value of information, coordination, higher-order beliefs, externalities, transparency. ∗An earlier version of this paper was entitled “Social Value of Information and Coordination.” For useful com- ments we thank Daron Acemoglu, Gadi Barlevi, Olivier Blanchard, Marco Bassetto, Christian Hellwig, Kiminori Matsuyama, Stephen Morris, Thomas Sargent, and especially Ivàn Werning. Email Addresses: [email protected], [email protected]. 1Introduction Is coordination socially desirable? Do markets use available information efficiently? Is the private collection of information good from a social perspective? What are the welfare effects of the public information disseminated by prices, market experts, or the media? Should central banks and policy makers disclose the information they collect and the forecasts they make about the economy in a transparent and timely fashion, or is there room for “constructive ambiguity”? These questions are non-trivial.
    [Show full text]
  • Lecture 3 1 Introduction to Game Theory 2 Games of Complete
    CSCI699: Topics in Learning and Game Theory Lecture 3 Lecturer: Shaddin Dughmi Scribes: Brendan Avent, Cheng Cheng 1 Introduction to Game Theory Game theory is the mathematical study of interaction among rational decision makers. The goal of game theory is to predict how agents behave in a game. For instance, poker, chess, and rock-paper-scissors are all forms of widely studied games. To formally define the concepts in game theory, we use Bayesian Decision Theory. Explicitly: • Ω is the set of future states. For example, in rock-paper-scissors, the future states can be 0 for a tie, 1 for a win, and -1 for a loss. • A is the set of possible actions. For example, the hand form of rock, paper, scissors in the game of rock-paper-scissors. • For each a ∈ A, there is a distribution x(a) ∈ Ω for which an agent believes he will receive ω ∼ x(a) if he takes action a. • A rational agent will choose an action according to Expected Utility theory; that is, each agent has their own utility function u :Ω → R, and chooses an action a∗ ∈ A that maximizes the expected utility . ∗ – Formally, a ∈ arg max E [u(ω)]. a∈A ω∼x(a) – If there are multiple actions that yield the same maximized expected utility, the agent may randomly choose among them. 2 Games of Complete Information 2.1 Normal Form Games In games of complete information, players act simultaneously and each player’s utility is determined by his actions as well as other players’ actions. The payoff structure of 1 2 GAMES OF COMPLETE INFORMATION 2 the game (i.e., the map from action profiles to utility vectors) is common knowledge to all players in the game.
    [Show full text]
  • Bayesian Games with Intentions
    Bayesian Games with Intentions Adam Bjorndahl Joseph Y. Halpern Rafael Pass Carnegie Mellon University Cornell University Cornell University Philosophy Computer Science Computer Science Pittsburgh, PA 15213 Ithaca, NY 14853 Ithaca, NY 14853 [email protected] [email protected] [email protected] We show that standard Bayesian games cannot represent the full spectrum of belief-dependentprefer- ences. However, by introducing a fundamental distinction between intended and actual strategies, we remove this limitation. We define Bayesian games with intentions, generalizing both Bayesian games and psychological games [5], and prove that Nash equilibria in psychological games correspond to a special class of equilibria as defined in our setting. 1 Introduction Type spaces were introduced by John Harsanyi [6] as a formal mechanism for modeling games of in- complete information where there is uncertainty about players’ payoff functions. Broadly speaking, types are taken to encode payoff-relevant information, a typical example being how each participant val- ues the items in an auction. An important feature of this formalism is that types also encode beliefs about types. Thus, a type encodes not only a player’s beliefs about other players’ payoff functions, but a whole belief hierarchy: a player’s beliefs about other players’ beliefs, their beliefs about other players’ beliefs about other players’ beliefs, and so on. This latter point has been enthusiastically embraced by the epistemic game theory community, where type spaces have been co-opted for the analysis of games of complete information. In this context, types encode beliefs about the strategies used by other players in the game as well as their types.
    [Show full text]
  • Part II Dynamic Games of Complete Information
    Part II Dynamic Games of Complete Information 83 84 This is page 8 Printer: Opaq 10 Preliminaries As we have seen, the normal form representation is a very general way of putting a formal structure on strategic situations, thus allowing us to analyze the game and reach some conclusions about what will result from the particular situation at hand. However, one obvious drawback of the normal form is its difficulty in capturing time. That is, there is a sense in which players’ strategy sets correspond to what they can do, and how the combination of their actions affect each others payoffs, but how is the order of moves captured? More important, if there is a well defined order of moves, will this have an effect on what we would label as a reasonable prediction of our model? Example: Sequencing the Cournot Game: Stackelberg Competition Consider our familiar Cournot game with demand P = 100 − q, q = q1 + q2,and ci(qi)=0for i ∈{1, 2}. We have already solved for the best response choices of each firm, by solving their maximum profit function when each takes the other’s quantity as fixed, that is, taking qj as fixed, each i solves: max (100 − qi − qj)qi , qi 86 10. Preliminaries and the best response is then, 100 − q q = j . i 2 Now lets change a small detail of the game, and assume that first, firm 1 will choose q1, and before firm 2 makes its choice of q2 it will observe the choice made by firm 1. From the fact that firm 2 maximizes its profit when q1 is already known, it should be clear that firm 2 will follow its best response function, since it not only has a belief, but this belief must be correct due to the observation of q1.
    [Show full text]
  • Ex-Post Stability in Large Games (Draft, Comments Welcome)
    EX-POST STABILITY IN LARGE GAMES (DRAFT, COMMENTS WELCOME) EHUD KALAI Abstract. The equilibria of strategic games with many semi-anonymous play- ers have a strong ex-post Nash property. Even with perfect hindsight about the realized types and selected actions of all his opponents, no player has an incentive to revise his own chosen action. This is illustrated for normal form and for one shot Baysian games with statistically independent types, provided that a certain continuity condition holds. Implications of this phenomenon include strong robustness properties of such equilibria and strong purification result for large anonymous games. 1. Introduction and Summary In a one-shot game with many semi-anonymous players all the equilibria are approximately ex-post Nash. Even with perfect hindsight about the realized types and actions of all his opponents, no player regrets, or has an incentive to unilaterally change his own selected action. This phenomenon holds for normal form games and for one shot Bayesian games with statistically independent types, provided that the payoff functions are continuous. Moreover, the ex-post Nash property is obtained uniformly, simultaneously for all the equilibria of all the games in certain large classes, at an exponential rate in the number of players. When restricted to normal form games, the above means that with probability close to one, the play of any mixed strategy equilibrium must produce a vector of pure strategies that is an epsilon equilibrium of the game. At an equilibrium of a Bayesian game, the vector of realized pure actions must be an epsilon equilibrium of the complete information game in which the realized vector of player types is common knowledge.
    [Show full text]
  • The Role of Imperfect Information
    Chapter 3.1 The Role of Imperfect Information An obvious source of approaches to strategy formulation is the field of classical strategic games. Classical game-tree search techniques have been highly success- ful in classical games of strategy such as chess, checkers, othello, backgammon, and the like. However, all of these games are perfect-information games: each player has perfect information about the current state of the game at all points during the game. Unlike classical strategic games, practical adversarial reason- ing problems force the decision-maker to solve the problem in the environment of highly imperfect information. Some of the game-tree search techniques used for perfect-information games can also be used in imperfect-information games, but only with substantial modifications. In this chapter,1 we classify and describe techniques for game-tree search in imperfect information games. In addition, we offer case studies of how these techniques have been applied to two imperfect-information games: Texas Hold’em, which is a well-known poker variant, and kriegspiel chess, an imperfect- information variant of chess that is the progenitor of modern military wargaming [15]. Classical Game-Tree Search In order to understand how to use game-tree search in imperfect-information games, it is first necessary to understand how it works in perfect-information games. Most game-tree search algorithms have been designed for use on games that satisfy the following assumptions: 1This work was supported by the following grants, contracts, and awards: ARO grant DAAD190310202, ARL grants DAAD190320026 and DAAL0197K0135, the ARL CTAs on Telecommunications and Advanced Decision Architectures, NSF grants IIS0329851, 0205489 and IIS0412812, UC Berkeley contract number SA451832441 (subcontract from DARPA’s REAL program).
    [Show full text]
  • On Information Design in Games
    On Information Design in Games Laurent Mathevet Jacopo Perego Ina Taneva New York University Columbia University University of Edinburgh February 6, 2020 Abstract Information provision in games influences behavior by affecting agents' be- liefs about the state, as well as their higher-order beliefs. We first characterize the extent to which a designer can manipulate agents' beliefs by disclosing in- formation. We then describe the structure of optimal belief distributions, including a concave-envelope representation that subsumes the single-agent result of Kamenica and Gentzkow(2011). This result holds under various solution concepts and outcome selection rules. Finally, we use our approach to compute an optimal information structure in an investment game under adversarial equilibrium selection. Keywords: information design, belief manipulation, belief distributions, ex- tremal decomposition, concavification. JEL Classification: C72, D82, D83. We are grateful to the editor, Emir Kamenica, and to three anonymous referees for their comments and suggestions, which significantly improved the paper. Special thanks are due to Anne-Katrin Roesler for her insightful comments as a discussant at the 2016 Cowles Foundation conference, and to Philippe Jehiel, Elliot Lipnowski, Stephen Morris, David Pearce, J´ozsefS´akovics, and Siyang Xiong for their helpful comments and conversations. We are also grateful to Andrew Clausen, Olivier Compte, Jon Eguia, Willemien Kets, Matt Jackson, Qingmin Liu, Efe Ok, Ennio Stacchetti, and Max Stinchcombe, as well as to seminar audiences at Columbia University, Institut d’An`alisiEcon`omica,New York University, Michigan State University, Paris School of Economics, Stony Brook University, University of Cambridge, University of Edinburgh, UT Austin, the SIRE BIC Workshop, the 2016 Decentralization conference, the 2016 Canadian Economic Theory con- ference, the 2016 Cowles Foundation conference, the 2016 North American Summer Meeting of the Econometric Society, and SAET 2017.
    [Show full text]
  • Bayesian Action-Graph Games
    Bayesian Action-Graph Games Albert Xin Jiang Kevin Leyton-Brown Department of Computer Science Department of Computer Science University of British Columbia University of British Columbia [email protected] [email protected] Abstract Games of incomplete information, or Bayesian games, are an important game- theoretic model and have many applications in economics. We propose Bayesian action-graph games (BAGGs), a novel graphical representation for Bayesian games. BAGGs can represent arbitrary Bayesian games, and furthermore can compactly express Bayesian games exhibiting commonly encountered types of structure in- cluding symmetry, action- and type-specific utility independence, and probabilistic independence of type distributions. We provide an algorithm for computing ex- pected utility in BAGGs, and discuss conditions under which the algorithm runs in polynomial time. Bayes-Nash equilibria of BAGGs can be computed by adapting existing algorithms for complete-information normal form games and leveraging our expected utility algorithm. We show both theoretically and empirically that our approaches improve significantly on the state of the art. 1 Introduction In the last decade, there has been much research at the interface of computer science and game theory (see e.g. [19, 22]). One fundamental class of computational problems in game theory is the computation of solution concepts of a finite game. Much of current research on computation of solution concepts has focused on complete-information games, in which the game being played is common knowledge among the players. However, in many multi-agent situations, players are uncertain about the game being played. Harsanyi [10] proposed games of incomplete information (or Bayesian games) as a mathematical model of such interactions.
    [Show full text]
  • Externalities
    7 Externalities 7.1 Introduction An externality is a link between economic agents that lies outside the price system of the economy. Everyday examples include the pollution from a factory that harms a local fishery and the envy that is felt when a neighbor proudly displays a new car. Such externalities are not controlled directly by the choices of those a¤ected—the fishery cannot choose to buy less pollution nor can you choose to buy your neighbor a worse car. This prevents the e‰ciency theorems described in chapter 2 from applying. Indeed, the demonstration of market e‰ciency was based on the following two presumptions: f The welfare of each consumer depended solely on her own consumption decision. f The production of each firm depended only on its own input and output choices. In reality, these presumptions may not be met. A consumer or a firm may be directly a¤ected by the actions of other agents in the economy; that is, there may be external e¤ects from the actions of other consumers or firms. In the pres- ence of such externalities the outcome of a competitive market is unlikely to be Pareto-e‰cient because agents will not take account of the external e¤ects of their (consumption/production) decisions. Typically the economy will generate too great a quantity of ‘‘bad’’ externalities and too small a quantity of ‘‘good’’ externalities. The control of externalities is an issue of increasing practical importance. Global warming and the destruction of the ozone layer are two of the most signif- icant examples, but there are numerous others, from local to global environmental issues.
    [Show full text]