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This paper describes the social evolution of an environment where all individuals are repeating patterns of behaviour. The paper follows Axelrod's work [1] of computer simulations of Iterated Prisoner's Dilemma (IPD), which is widely regarded as a standard model for the evolution of cooperation. Previous studies by Axelrod [2], Hirshleifer and Coll...
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... Following one tournament, Axelrod used an evolutionary algorithm to identify a strategy that was equal to or better than TFT [15]. This initial effort was followed by many other attempts to work on this problem based on evolutionary computation [5,14,[16][17][18][19][20]. ...
The iterated prisoner’s dilemma (IPD) is well known within the domain of game theory. Although it is relatively simple, it can also elucidate important problems related to cooperation and trust. Generally, players can predict their opponents’ actions when they are able to build a precise model of their behavior based on their game playing experience. However, it is difficult to make such predictions based on a limited number of games. The creation of a precise model requires the use of not only an appropriate learning algorithm and framework but also a good dataset. Active learning approaches have recently been introduced to machine learning communities. The approach can usually produce informative datasets with relatively little effort. Therefore, we have proposed an active modeling technique to predict the behavior of IPD players. The proposed method can model the opponent player’s behavior while taking advantage of interactive game environments. This experiment used twelve representative types of players as opponents, and an observer used an active modeling algorithm to model these opponents. This observer actively collected data and modeled the opponent’s behavior online. Most of our data showed that the observer was able to build, through direct actions, a more accurate model of an opponent’s behavior than when the data were collected through random actions.
... He used single-objective evolutionary algorithm for finding the optimal strategies. This is discussed in section 2. Since Axelrod, there have been several studies on this problem[9],[10],[11],[12],[13]. However, in all these studies, the problem of finding optimal strategies has been viewed as a single-objective problem. ...
In this paper, we present a new paradigm of searching optimal strategies in the game of iterated prisoner's dilemma (IPD) using multiple-objective evolutionary algorithms. This method is more useful than the existing approaches, because it not only produces strategies that perform better in the iterated game but also finds a family of nondominated strategies, which can be analyzed to decipher properties a strategy should have to win the game in a more satisfactory manner. We present the results obtained by this new method and discuss sub-strategies found to be common among nondominated strategies. The multiobjective treatment of the IPD problem demonstrated here can be applied to other similar game-playing tasks.
... Further examples of the diversity of representations possible when using EAs are Fogel (1993), who evolved finite state machines in an attempt to probe the necessary conditions for cooperation to emerge, and Jang (2004), who determined the effect on behavior of players with no memory that used fixed sequences of moves with varying lengths. ...
Many real life situations can be modeled as Prisoner's dilemma. There are various strategies in the literature. However, few of which match the design objectives of an intelligent agent - being reactive and pro-active. In this paper, we incorporate risk attitude and reputation into infinitely repeated games. In this way, we find that the original game matrix can be transformed to a new matrix, which has a kind of cooperative equilibrium. We use the proposed concepts to analyze the Iterated Prisoner's dilemma. Simulation also shows that agents, which consider risk attitude and reputation in the decision-making process, have improved performance and are reactive as well as pro-active.
