New algorithms for approximate Nash equilibria in bimatrix games

We consider the problem of computing additively approximate Nash equilibria in non-cooperative two-player games. We provide a new polynomial time algorithm that achieves an approximation guarantee of 0.36392. We first provide a simpler algorithm, that achieves a 0.38197-approximation, which is exactly the same factor as the algorithm of Daskalakis, Mehta and Papadimitriou. This algorithm is then tuned, improving the approximation error to 0.36392. Our method is relatively fast and simple, as it requires solving only one linear program and it is based on using the solution of an auxiliary zero-sum game as a starting point. Finally we also exhibit a simple reduction that allows us to compute approximate equilibria for multi-player games by using algorithms for two-player games.     

Comparing the notions of optimality in CP-nets,strategic games and soft constraints

The notion of optimality naturally arises in many areas of applied mathematics and computer science concerned with decision making. Here we consider this notion in the context of three formalisms used for different purposes in reasoning about multi-agent systems: strategic games, CP-nets, and soft constraints. To relate the notions of optimality in these formalisms we introduce a natural qualitative modification of the notion of a strategic game. We show then that the optimal outcomes of a CP-net are exactly the Nash equilibria of such games. This allows us to use the techniques of game theory to search for optimal outcomes of CP-nets and vice-versa, to use techniques developed for CP-nets to search for Nash equilibria of the considered games. Then, we relate the notion of optimality used in the area of soft constraints to that used in a generalization of strategic games, called graphical games. In particular we prove that for a natural class of soft constraints that includes weighted constraints every optimal solution is both a Nash equilibrium and Pareto efficient joint strategy. For a natural mapping in the other direction we show that Pareto efficient joint strategies coincide with the optimal solutions of soft constraints.      

Pure Nash equilibria in player-specific and weighted congestion games

Unlike standard congestion games, weighted congestion games and congestion games with player-specific delay functions do not necessarily possess pure Nash equilibria. It is known, however, that there exist pure equilibria for both of these variants in the case of singleton congestion games, i.e., if the players’ strategy spaces contain only sets of cardinality one. In this paper, we investigate how far such a property on the players’ strategy spaces guaranteeing the existence of pure equilibria can be extended. We show that both weighted and player-specific congestion games admit pure equilibria in the case of matroid congestion games, i.e., if the strategy space of each player consists of the bases of a matroid on the set of resources. We also show that the matroid property is the maximal property that guarantees pure equilibria without taking into account how the strategy spaces of different players are interweaved.     

On the complexity of constrained Nash equilibria in graphical games

A widely accepted rational behavior for non-cooperative players is based on the notion of Nash equilibrium. Although the existence of a Nash equilibrium is guaranteed in the mixed framework (i.e., when players select their actions in a randomized manner) in many real-world applications the existence of “any” equilibrium is not enough. Rather, it is often desirable to single out equilibria satisfying some additional requirements (in order, for instance, to guarantee a minimum payoff to certain players), which we call constrained Nash equilibria.In this paper, a formal framework for specifying these kinds of requirement is introduced and investigated in the context of graphical games, where a player p may directly be interested in some of the other players only, called the neighbors of p. This setting is very useful for modeling large population games, where typically each player does not directly depend on all the players, and representing her utility function extensively is either inconvenient or infeasible.Based on this framework, the complexity of deciding the existence and of computing constrained equilibria is then investigated, in the light of evidencing how the intrinsic difficulty of these tasks is affected by the requirements prescribed at the equilibrium and by the structure of players’ interactions. The analysis is carried out for the setting of mixed strategies as well as for the setting of pure strategies, i.e., when players are forced to deterministically choose the action to perform. In particular, for this latter case, restrictions on players’ interactions and on constraints are identified, that make the computation of Nash equilibria an easy problem, for which polynomial and highly-parallelizable algorithms are presented.     

Establishing Nash equilibria of strategic games: a multistart Fuzzy Adaptive Simulated Annealing approach

This paper's proposal is to show some significant results obtained by the application of the optimization algorithm known as Fuzzy Adaptive Simulated Annealing (Fuzzy ASA) to the task of finding all Nash equilibria of normal form games. To that end, a special version of Fuzzy ASA, that utilizes space-filling curves to find good seeds, is applied to several well-known strategic games, showing its effectiveness in obtaining all Nash equilibria in all cases. The results are compared to previous work that also used computational intelligence techniques in order to solve the same problem but could not find all equilibria in all tests. Game theory is a very important subject, modeling interactions between generic agents, and Nash equilibrium represents a powerful concept portraying situations in which joint strategies are optimal in the sense that no player can benefit from changing her/his strategy while the other players do not change their strategies as well. So, new techniques are always welcome, mainly those that can find the whole set of solutions for a given strategic game.     

Guard games on graphs: Keep the intruder out!

A team of mobile agents, called guards, tries to keep an intruder out of an assigned area by blocking all possible attacks. In a graph model for this setting, the guards and the intruder are located on the vertices of a graph, and they move from node to node via connecting edges. The area protected by the guards is an induced subgraph of the given graph. We investigate the algorithmic aspects of the guarding problem, which is to find the minimum number of guards sufficient to patrol the area. We show that the guarding problem is PSPACE-hard and provide a set of approximation algorithms. All approximation algorithms are based on the study of a variant of the game where the intruder must reach the guarded area in a single step in order to win. This variant of the game appears to be a 2-approximation for the guarding problem, and for graphs without cycles of length 5 the minimum number of required guards in both games coincides. We give a polynomial time algorithm for solving the one-step guarding problem in graphs of bounded treewidth, and complement this result by showing that the problem is W[1]-hard parameterized by the treewidth of the input graph. We also show that the problem is fixed parameter tractable (FPT) parameterized by the treewidth and maximum degree of the input graph. Finally, we turn our attention to a large class of sparse graphs, including planar graphs and graphs of bounded genus, namely apex-minor-free graphs. We prove that the one-step guarding problem is FPT and possess a PTAS on apex-minor-free graphs.     

Complexity of reachability problems for finite discrete dynamical systems

Sequential Dynamical Systems (SDSs) are a special type of finite discrete dynamical systems that can be used to model simulation systems. We focus on the computational complexity of testing several phase space properties of SDSs. Our main result is a sharp delineation between classes of SDSs whose behavior is easy to predict and those whose behavior is hard to predict. Specifically, we show the following.

1.

Several state reachability problems for SDSs are PSPACE-complete, even when restricted to SDSs whose underlying graphs are of bounded bandwidth (and hence of bounded pathwidth and treewidth), and the function associated with each node is symmetric. Moreover, this result holds even when the underlying graph is d-regular for some constant d and all the nodes compute the same symmetric Boolean function. An immediate corollary of this result is a PSPACE-hard lower bound on the complexity of reachability problems for regular generalized 1D-Cellular Automata and undirected systolic networks with Boolean totalistic local transition functions.

2.

In contrast, the above reachability problems are solvable in polynomial time for SDSs when the Boolean function associated with each node is symmetric and monotone.

The PSPACE-completeness results follow as corollaries of simulation results which show for several classes of SDSs, how one class of SDSs can be efficiently simulated by another (more restricted) class of SDSs. We also prove several structural properties concerning the phase space of an SDS. SDSs are closely related to Cellular Automata (CA), concurrent transition systems, discrete Hopfield networks and systolic networks. This observation in conjunction with our lower bounds for SDSs, yields new PSPACE-hard lower bounds on the complexity of state reachability problems for these models, extending some of the earlier results in [K. Culik II, J. Karhumäki, On totalistic systolic networks, Inform. Process. Lett. 26 (5) (1988) 231-236; P. Floréen, E. Goles, G. Weisbuch, Transient length in sequential iterations of threshold functions, Discrete Appl. Math. 6 (1983) 95-98; P. Floréen, P. Orponen, Complexity issues in discrete Hopfield networks, Research Report No. A-1994-4, Department of Computer Science, University of Helsinki, 1994. Also appears in: I. Parberry (Ed.), Comp. and Learning Complexity of Neural Networks: Advanced Topics, 1999; D. Harel, O. Kupferman, M.Y. Vardi, On the complexity of verifying concurrent transition systems, Inform. and Comput. 173 (2002) 143-161; S.K. Shukla, H.B. Hunt III, D.J. Rosenkrantz, R.E. Stearns, On the complexity of relational problems for finite state processes, in: International Colloquium on Automata Programming and Languages, ICALP, 1996, pp. 466-477; A. Rabinovich, Complexity of equivalence problems for concurrent systems of finite agents, Inform. and Comput. 127 (2) (1997) 164-185].     

Complexity of cyclic scheduling problems: A state-of-the-art survey

In this survey we review the current complexity status of basic cyclic scheduling models. We start with the formulations of three fundamental cyclic scheduling problems, namely the cyclic jobshop, cyclic flowshop, and cyclic project scheduling problems. We present state-of-the-art results on the computational complexity of the problems, paying special attention to recent results on the unsolvability (NP-hardness) of various cyclic problems arising from the scheduling of robotic cells.     

基于事件驱动控制的混杂动态博弈系统的纳什均衡分析

本文研究了基于事件驱动控制的混杂动态博弈系统的纳什均衡分析问题. 首先, 分析了事件驱动机制对混 杂动态博弈过程的影响, 进而, 在进行状态空间描述的基础上, 给出了混杂动态博弈的纳什均衡的定义, 并建立了对 应博弈系统的策略型模型. 其次, 结合Lanchester方程, 分别讨论了两类混杂动态博弈系统的均衡问题, 包括事件驱 动策略设计和固定的情况, 获得了均衡解存在的必要条件. 最后, 通过数值模拟进行了应用分析, 验证了所取得结果 的合理性和科学性, 并总结了混杂动态博弈研究的未来工作.     

基于多灾点非合作博弈的资源调度建模与仿真

当突发事件发生后,在应急资源有限的情况下,对多个灾点进行合理的资源调度是一个非常现实而复杂的问题。从多灾点所需应急资源的角度出发,提出了基于非合作博弈的应急资源调度模型和算法。在该调度模型中,各个灾点被映射为博弈模型的局中人,可能的资源调度方案映射为策略集,资源调度成本的倒数映射为效用函数,将应急资源的调度问题转化为对非合作博弈调度模型的Nash均衡点求解问题,接着介绍了一种求解Nash均衡点的迭代算法。最后对模型的仿真测试验证了该模型的有效性和可行性。     

Discrete-time dynamic graphical games: model-free reinforcement learning solution

This paper introduces a model-free reinforcement learning technique that is used to solve a class of dynamic games known as dynamic graphical games. The graphical game results from multi-agent dynamical systems, where pinning control is used to make all the agents synchronize to the state of a command generator or a leader agent. Novel coupled Bellman equations and Hamiltonian functions are developed for the dynamic graphical games. The Hamiltonian mechanics are used to derive the necessary conditions for optimality. The solution for the dynamic graphical game is given in terms of the solution to a set of coupled Hamilton-Jacobi-Bellman equations developed herein. Nash equilibrium solution for the graphical game is given in terms of the solution to the underlying coupled Hamilton-Jacobi-Bellman equations. An online model-free policy iteration algorithm is developed to learn the Nash solution for the dynamic graphical game. This algorithm does not require any knowledge of the agents’ dynamics. A proof of convergence for this multi-agent learning algorithm is given under mild assumption about the inter-connectivity properties of the graph. A gradient descent technique with critic network structures is used to implement the policy iteration algorithm to solve the graphical game online in real-time.     

Piracy on the simulated seas: the computer games industry's non-legal approaches to fighting illegal downloads of games

The computer games industry suffers from high rates of piracy, with many people downloading illegal copies of games. People in the industry are aware of the problem, and the gravity of it has been acknowledged and commented on by many publishers and developers. Yet unlike the Recording Industry Association of America, Motion Picture Association of America, independent movie studios, and other rights holders, computer game developers and publishers have shied away from attempting to sue pirates or otherwise enforce their rights through the legal system. Instead, the industry has looked to a variety of self-help remedies, a fact that has largely not been commented on by legal scholars. This paper examines the successes and pitfalls of these varied non-legal approaches, broadly classifying them into four categories: digital rights management, online-only offerings, making legal easier, and community engagement.     

Multi-player non-zero-sum games: Online adaptive learning solution of coupled Hamilton–Jacobi equations

In this paper we present an online adaptive control algorithm based on policy iteration reinforcement learning techniques to solve the continuous-time (CT) multi player non-zero-sum (NZS) game with infinite horizon for linear and nonlinear systems. NZS games allow for players to have a cooperative team component and an individual selfish component of strategy. The adaptive algorithm learns online the solution of coupled Riccati equations and coupled Hamilton–Jacobi equations for linear and nonlinear systems respectively. This adaptive control method finds in real-time approximations of the optimal value and the NZS Nash-equilibrium, while also guaranteeing closed-loop stability. The optimal-adaptive algorithm is implemented as a separate actor/critic parametric network approximator structure for every player, and involves simultaneous continuous-time adaptation of the actor/critic networks. A persistence of excitation condition is shown to guarantee convergence of every critic to the actual optimal value function for that player. A detailed mathematical analysis is done for 2-player NZS games. Novel tuning algorithms are given for the actor/critic networks. The convergence to the Nash equilibrium is proven and stability of the system is also guaranteed. This provides optimal adaptive control solutions for both non-zero-sum games and their special case, the zero-sum games. Simulation examples show the effectiveness of the new algorithm.     

Bounded Budget Connection (BBC) games or how to make friends and influence people,on a budget

Motivated by applications in social and peer-to-peer networks, we introduce the Bounded Budget Connection (BBC) game and study its pure Nash equilibria. We have a collection of n   players, each with a budget for purchasing links. Each link has a cost and a length. Each node has a preference weight for each node, and its objective is to purchase outgoing links within its budget to minimize its sum of preference-weighted distances to the nodes. We show that determining if a BBC game has pure Nash equilibria is NP-hard. We study (n,k)$(n,k)$-uniform BBC games, where all link costs, lengths, and preferences are equal and every budget equals k  . We show that pure Nash equilibria exist for all (n,k)$(n,k)$-uniform BBC games and all equilibria are essentially fair. We construct a family of equilibria spanning the spectrum from minimum to maximum social cost. We also analyze best-response walks and alternative node objectives.     

Chameleon: online learning for believable behaviors based on humans imitation in computer games

In some video games, humans and computer programs can play together, each one controlling a virtual humanoid. These computer programs usually aim at replacing missing human players; however, they partially miss their goal, as they can be easily spotted by players as being artificial. Our objective is to find a method to create programs whose behaviors cannot be told apart from players when observed playing the game. We call this kind of behavior a believable behavior. To achieve this goal, we choose models using Markov chains to generate the behaviors by imitation. Such models use probability distributions to find which decision to choose depending on the perceptions of the virtual humanoid. Then, actions are chosen depending on the perceptions and the decision. We propose a new model, called Chameleon , to enhance expressiveness and the associated imitation learning algorithm. We first organize the sensors and motors by semantic refinement and add a focus mechanism in order to improve the believability. Then, we integrate an algorithm to learn the topology of the environment that tries to best represent the use of the environment by the players. Finally, we propose an algorithm to learn parameters of the decision model. Copyright © 2013 John Wiley & Sons, Ltd.     

Analysis of critical factors for social games based on extended technology acceptance model: a DEMATEL approach

In recent years, social games such as ‘Farmville’ and ‘Pokémon Go’ have become a major game type in the gaming industry. This study examines the importance of different factors in social games using the Technology Acceptance Model (TAM) and DEMATEL. The result shows ‘social norm’ as the most important factor overall. It is also found that ‘pleasure’ and ‘sociability’ are the most important aspects in ‘perceived enjoyment’. Regarding key aspects in each factor, ‘flow experience’ is crucial in ‘perceived attractiveness’, ‘game fairness’ largely influential in ‘social norm’, and ‘reputation of platform and service provider’ a decisive aspect in ‘platform service and corporate image’. These findings and analyses are apt references for social game providers to improve their services.     

The influence of virtual presence: Effects on experienced cognitive load and learning outcomes in educational computer games

Does the immersive design of an educational gaming environment affect learners’ virtual presence and how much do they learn? Does virtual presence affect learning? This study tries to answer these questions by examining the differences in virtual presence and learning outcomes in two different computer-based multimedia environments: a gaming environment with high immersive design vs. hypertext learning environment with low immersive design. As the main focus, the effect of virtual presence on learning is also explained and tested. By identifying virtual presence as a variable that may determine learning, it is argued that computer gaming environments present a new challenge for researchers to investigate, particularly, the effects of virtual presence on the immersive design of games in order to help designers to predict which instructional configurations will maximize learning performance. In general, results revealed that the high-immersive gaming environment leads to the strongest form of virtual presence but also decreased learning. Although regression analyses indicate that virtual presence positively influences trivial- and non-trivial learning outcomes, learners who learned in a low-immersive environment outperformed the gaming group. A mediation analysis showed that the relation between virtual presence and non-trivial learning outcomes is partly mediated through increased cognitive load.     

Knowledge Sharing on Enterprise Social Media: Practices to Cope With Institutional Complexity

This study examines the use of enterprise social media (ESM) for organizational knowledge sharing and shows that professionals face ambiguities because their knowledge sharing behavior is informed by an institutional complexity that consists of 2 dissimilar institutional logics: logics of the profession, and logics of the corporation. Our qualitative case study of an ESM at an IT consultancy organization shows that professionals find ways to manage the ambiguities they experience by engaging the affordances of ESM in such a way as to develop coping practices: connection management, reputation management, and information management. By complementing the affordance perspective with an institutional logics perspective, we are able to advance scholarly understanding on how ESM can facilitate but also frustrate knowledge sharing.