Multi-objective design method based on evolution game and its application for suspension

Through research and bionics of biology survival mode, game players with competition, cooperation and self-adaptation capacity are introduced in the multi-objective design. The dynamic behavior and bounded rationality in game processes for players are considered according to Chinese saying “In success, commit oneself to the welfare of the society; in distress, maintain one’s own integrity”. An evolution rule, Poor-Competition-Rich-Cooperation (short for PCRC), is proposed. Then, the corresponding payoff functions of competition and cooperation behavior are established and a multi-objective design method based on evolution game is proposed. The calculation steps are as follows: 1) Taking the design objectives as different game players, and calculating factors of the design variables to objective and fuzzy clustering. The design variables are divided into multiple strategy subsets owned by each game player. 2) According to the evolution rule, each player determines its behavior and payoff function in this game round. 3) In their own strategy subsets, each game player takes their payoff as mono-objective for optimization. It gives the best strategy upon other players. And so the best strategies of all players conform the group strategy in this round. The final equilibrium solution is obtained through multi-round game based on convergence criterion. The validity and reliability of this method are shown by the results of an example of a tri-objective optimization design of passive suspension parameters.     

Evolving Nash-optimal poker strategies using evolutionary computation

This paper focuses on the development of a competitive computer player for the one versus one Texas Hold’em poker using evolutionary algorithms (EA). A Texas Hold’em game engine is first constructed where an efficient odds calculator is programmed to allow for the abstraction of a player’s cards, which yield important but complex information. Effort is directed to realize an optimal player that will play close to the Nash equilibrium (NE) by proposing a new fitness criterion. Preliminary studies on a simplified version of poker highlighted the intransitivity nature of poker. The evolved player displays strategies that are logical but reveals insights that are hard to comprehend e.g., bluffing. The player is then benchmarked against Poki and PSOpti, which is the best heads-up Texas Hold’em artificial intelligence to date and plays closest to the optimal Nash equilibrium. Despite the much constrained chromosomal strategy representation, simulated results verified that evolutionary algorithms are effective in creating strategies that are comparable to Poki and PSOpti in the absence of expert knowledge.     

Reaching pareto-optimality in prisoner’s dilemma using conditional joint action learning

We consider the learning problem faced by two self-interested agents repeatedly playing a general-sum stage game. We assume that the players can observe each other’s actions but not the payoffs received by the other player. The concept of Nash Equilibrium in repeated games provides an individually rational solution for playing such games and can be achieved by playing the Nash Equilibrium strategy for the single-shot game in every iteration. Such a strategy, however can sometimes lead to a Pareto-Dominated outcome for games like Prisoner’s Dilemma. So we prefer learning strategies that converge to a Pareto-Optimal outcome that also produces a Nash Equilibrium payoff for repeated two-player, n-action general-sum games. The Folk Theorem enable us to identify such outcomes. In this paper, we introduce the Conditional Joint Action Learner (CJAL) which learns the conditional probability of an action taken by the opponent given its own actions and uses it to decide its next course of action. We empirically show that under self-play and if the payoff structure of the Prisoner’s Dilemma game satisfies certain conditions, a CJAL learner, using a random exploration strategy followed by a completely greedy exploitation technique, will learn to converge to a Pareto-Optimal solution. We also show that such learning will generate Pareto-Optimal payoffs in a large majority of other two-player general sum games. We compare the performance of CJAL with that of existing algorithms such as WOLF-PHC and JAL on all structurally distinct two-player conflict games with ordinal payoffs.     

Concurrent reachability games

We consider concurrent two-player games with reachability objectives. In such games, at each round, player 1 and player 2 independently and simultaneously choose moves, and the two choices determine the next state of the game. The objective of player 1 is to reach a set of target states; the objective of player 2 is to prevent this. These are zero-sum games, and the reachability objective is one of the most basic objectives: determining the set of states from which player 1 can win the game is a fundamental problem in control theory and system verification. There are three types of winning states, according to the degree of certainty with which player 1 can reach the target. From type-1 states, player 1 has a deterministic strategy to always reach the target. From type-2 states, player 1 has a randomized strategy to reach the target with probability 1. From type-3 states, player 1 has for every real ε>0$\epsilon >0$ a randomized strategy to reach the target with probability greater than 1−ε$1-\epsilon$. We show that for finite state spaces, all three sets of winning states can be computed in polynomial time: type-1 states in linear time, and type-2 and type-3 states in quadratic time. The algorithms to compute the three sets of winning states also enable the construction of the winning and spoiling strategies.     

On characterization of credibilistic equilibria of fuzzy-payoff two-player zero-sum game

Harsanyi pointed out that “the player may lack full information about the other players’ (or even his own) payoffs, etc.” In this paper, we investigate the two-player zero-sum game, in which the payoffs are interpreted as fuzzy variables due to incomplete information. Based on the credibility theory, we employ three decision criteria to define the behaviors of the players in different decision situations. Accordingly, three definitions of Nash equilibria, called credibilistic equilibria, are proposed. Besides the existence theorem of the three credibilistic equilibria, we also discuss their relationships to illustrate the significance of the proposed credibilistic equilibria.     

Novel Optimal Design Approach for Output‐Feedback H∞ Control of Vehicle Active Seat‐Suspension System

In this paper, the output‐feedback control problem of a vehicle active seat‐suspension system is investigated. A novel optimal design approach for an output‐feedback H_∞ controller is proposed. The main objective of the controller is to minimize the seat vertical acceleration to improve vehicle ride comfort. First, the human body and the seat are considered in the modeling of a vehicle active suspension system, which makes the model more precise. Other constraints, such as tire deflection, suspension deflection and actuator saturation, are also considered. Then the output‐feedback control strategy is adopted since some state variables, such as body acceleration and body deflection, are unavailable. A concise and effective approach for an output‐feedback H_∞ optimal control is presented. The desired controller is obtained by solving the corresponding linear matrix inequalities (LMIs) and by the calculation of equations proposed in this paper. Finally, a numerical example is presented to show the effectiveness and advantages of the proposed controller design approach.     

Improving behavior of computer game bots using fictitious play

In modern computer games, "bots" - intelligent realistic agents play a prominent role in the popularity of a game in the market. Typically, bots are modeled using finite-state machine and then programmed via simple conditional statements which are hard-coded in bots logic. Since these bots have become quite predictable to an experienced games player, a player might lose interest in the game. We propose the use of a game theoretic based learning rule called fictitious play for improving behavior of these computer game bots which will make them less predictable and hence, more a enjoyable game.     

On the Complexity of Computing Winning Strategies for Finite Poset Games

This paper is concerned with the complexity of computing winning strategies for poset games. While it is reasonably clear that such strategies can be computed in PSPACE, we give a simple proof of this fact by a reduction to the game of geography. We also show how to formalize the reasoning about poset games in Skelley’s theory W₁¹\mathbf{W}_{1}^{1} for PSPACE reasoning. We conclude that W₁¹\mathbf{W}_{1}^{1} can use the “strategy stealing argument” to prove that in poset games with a supremum the first player always has a winning strategy.     

Transports of delight? What the experience of receiving (mobile) phone calls can tell us about design

This paper takes a phenomenological approach to analysing people’s accounts of receiving phone calls, drawing on Heidegger and Feenberg. Accounts of calls received on a mobile phone are compared with those on landlines, charting progress from location-centred to person-centred phoning. A range of naturally-occurring contexts are discussed in terms of the experience of balancing the activities of talking on the phone with activities in the immediate environment, and the enchantment sustained or sacrificed. The study suggests that recipients’ enchantment with phoning is affected by their freedom and desire to project towards the caller and create shared spaces, and reveals some factors that impact on the transitions of attention required to do so. It concludes with the design implications of taking this view of interactions with and through phones.     

Children’s Application of Theory of Mind in Reasoning and Language

Many social situations require a mental model of the knowledge, beliefs, goals, and intentions of others: a Theory of Mind (ToM). If a person can reason about other people’s beliefs about his own beliefs or intentions, he is demonstrating second-order ToM reasoning. A standard task to test second-order ToM reasoning is the second-order false belief task. A different approach to investigating ToM reasoning is through its application in a strategic game. Another task that is believed to involve the application of second-order ToM is the comprehension of sentences that the hearer can only understand by considering the speaker’s alternatives. In this study we tested 40 children between 8 and 10 years old and 27 adult controls on (adaptations of) the three tasks mentioned above: the false belief task, a strategic game, and a sentence comprehension task. The results show interesting differences between adults and children, between the three tasks, and between this study and previous research.     

Stable controller design of MIMO systems in real Grassmann space

A new design method of a stable dynamic output feedback (DOF) controller in linear MIMO systems is presented on the frame of real Grassmann spaces. For the analysis, the DOF systems are decomposed into augmented static output feedback (SOF) systems using signal flow graph analysis of all DOF loops. For synthesis and design, the characteristic polynomial of the augmented SOF system for the system’s stable poles and the sub-characteristic polynomial of the sub-SOF system for the controller’s stable poles are parametrized within their Grassmann invariants in real Grassmann spaces, whose coordinates are defined in the real coefficient function spaces of their augmented SOF variables. The numerical parametrization and computation algorithm for a stable controller design is illustrated over a MIMO plant of a practical aircraft carrier model.     

A Comparative Study of Game Theoretic and Evolutionary Models of Bargaining for Software Agents

Most of the existing work in the study of bargaining behavior uses techniques from game theory. Game theoretic models for bargaining assume that players are perfectly rational and that this rationality is common knowledge. However, the perfect rationality assumption does not hold for real-life bargaining scenarios with humans as players, since results from experimental economics show that humans find their way to the best strategy through trial and error, and not typically by means of rational deliberation. Such players are said to be boundedly rational. In playing a game against an opponent with bounded rationality, the most effective strategy of a player is not the equilibrium strategy but the one that is the best reply to the opponents strategy. The evolutionary model provides a means for studying the bargaining behaviour of boundedly rational players. This paper provides a comprehensive comparison of the game theoretic and evolutionary approaches to bargaining by examining their assumptions, goals, and limitations. We then study the implications of these differences from the perspective of the software agent developer.     

AN EVOLUTIONARY STRATEGY FOR A COMPUTER TEAM GAME

Computer team games have attracted many players in recent years. Most of them are rule‐based systems because they are simple and easy to implement. However, they usually cause a game agent to be inflexible, and it may repeat a failure. Some studies investigated the learning of a single game agent, and its learning capability has been improved. However, each agent in a team is independent and it does not cooperate with others in a multiplayer game. This article explores an evolution strategy for a computer team game based on Quake III Arena. The Particle Swarm Optimization (PSO) algorithm will be applied to evolve a non‐player character (NPC) team in Quake III to be more efficient and intelligent. The evolution of a single NPC, which accommodates to its team and, moreover, the team has learning and cooperating abilities, will be discussed. An efficient team is composed of various members with their own specialties, and the leader is capable of evaluating the performance of a member and assigning it a proper job. Furthermore, the leader of an intelligent team will adapt a strategy appropriate for various circumstances and obtain the team's best performance. Instead of considering the tactic of an individual bot, this article takes the strategy of a team into account.     

Games Played under Fuzzy Constraints

Psychological experiment studies reveal that human interaction behaviors are often not the same as what game theory predicts. One of important reasons is that they did not put relevant constraints into consideration when the players choose their best strategies. However, in real life, games are often played in certain contexts where players are constrained by their capabilities, law, culture, custom, and so on. For example, if someone wants to drive a car, he/she has to have a driving license. Therefore, when a human player of a game chooses a strategy, he/she should consider not only the material payoff or monetary reward from taking his/her best strategy and others' best responses but also how feasible to take the strategy in that context where the game is played. To solve such a game, this paper establishes a model of fuzzily constrained games and introduces a solution concept of constrained equilibrium for the games of this kind. Our model is consistent with psychological experiment results of ultimatum games. We also discuss what will happen if Prisoner's Dilemma and Stag Hunt are played under fuzzy constraints. In general, after putting constraints into account, our model can reflect well the human behaviors of fairness, altruism, self‐interest, and so on, and thus can predict the outcomes of some games more accurate than conventional game theory.     

Automatic fuzzy decision making system with learning for competing and connected businesses

We study uncertainties surrounding competition on business networks and board games. We investigate these uncertainties using concepts of fuzzy logic and game theory. We investigate how the payoff of the players is affected by a number of factors. These include the level of connectivity or number of links, the number of competitors, possible constraints on the networks and on the boards, as well as choice of strategy adopted by competitors. We introduce one fuzzy player in the game. This player uses fuzzy rules to make strategic decisions. We introduce learning to train and analyze how the fuzzy player adapts over time during the game.     

Hierarchical games with deliberately distorted information

Consideration was given to the hierarchical two-person game where the lower-level player informs the principal about his decisions, and at that it can communicate invalid information. The principal, however, can verify at random the presented information and penalize its partner for the distorted information. Calculation of the maximal guaranteed result in a corresponding game is a complicated variational game. The present paper reduced this problem to calculation of multiple maximins in the “finite-dimensional” spaces. This result enables one to gain an insight into the logical structure of the optimal strategy of the upper-level player.     

Designing playful interactions for social interaction and physical play

This paper describes three design values that we apply for designing playful interactions. Interactive play objects can stimulate social interaction and physical play by providing motivating feedback to players’ behavior; they can allow players to create their own game goals and rules in an open-ended play context and support social player interaction patterns. This design approach is illustrated by six design cases in which our assumptions were examined in various play contexts. The results show that the application of these design values can lead to rich and appealing innovative play concepts. Players can create a wide range of (physical) games using open-ended play objects, and properties of the play objects, such as being personal or shared, influence the type of social interaction.     

Learning through reinforcement for N-person repeated constrained games

The design and analysis of an adaptive strategy for N-person averaged constrained stochastic repeated game are addressed. Each player is modeled by a stochastic variable-structure learning automaton. Some constraints are imposed on some functions of the probabilities governing the selection of the player's actions. After each stage, the payoff to each player as well as the constraints are random variables. No information concerning the parameters of the game is a priori available. The "diagonal concavity" conditions are assumed to be fulfilled to guarantee the existence and uniqueness of the Nash equilibrium. The suggested adaptive strategy which uses only the current realizations (outcomes and constraints) of the game is based on the Bush-Mosteller reinforcement scheme in connection with a normalization procedure. The Lagrange multipliers approach with a regularization is used. The asymptotic properties of this algorithm are analyzed. Simulation results illustrate the feasibility and the performance of this adaptive strategy.     

Flipping the winner of a poset game

Partially-ordered set games, also called poset games, are a class of two-player combinatorial games. The playing field consists of a set of elements, some of which are greater than other elements. Two players take turns removing an element and all elements greater than it, and whoever takes the last element wins. Examples of poset games include Nim and Chomp. We investigate the complexity of computing which player of a poset game has a winning strategy. We give an inductive procedure that modifies poset games to change the nim-value which informally captures the winning strategies in the game. For a generic poset game G, we describe an efficient method for constructing a game ¬G such that the first player has a winning strategy if and only if the second player has a winning strategy on G. This solves the long-standing problem of whether this construction can be done efficiently. This construction also allows us to reduce the class of Boolean formulas to poset games, establishing a lower bound on the complexity of poset games.     

E-MEMO: a method to support the development of customized electronic commerce systems

In many industries structural change through E-Commerce is challenging firms to re-align their strategies as well as re-engineer their business processes with new competitive environments while taking advantage of technological opportunities. This article presents E-MEMO, a method for multi-perspective enterprise modelling with special emphasis on processes and technologies for E-Commerce. It serves to analyse and design corporate information systems that are balanced with a company’s E-Commerce strategy and its organisation. E-MEMO offers specific languages for modelling strategies, business processes, and related resources. In addition to that, it provides a library of reference models including strategy networks to guide strategic planning and models of business processes. In order to further support the implementation of information systems a transformation has been defined that allows for generating workflow schemata from business process models. Since design-oriented research is not predominant in the Information Systems field, the epistemological challenges of the chosen research approach are discussed, too.