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Inter-firm knowledge sharing within industrial cluster causes the formation, development and evolution of industrial cluster. This paper analyzes the evolution of industrial cluster by means of evolutionary game theory from the perspective of knowledge sharing. It argues that enterprises in the industrial cluster should learn more, enlarge knowledge stores and enhance the capability of knowledge assimilation...
We present a self-learning evolutionary Prisoner's Dilemma game model to study the evolution of cooperation in network-structured populations. During the evolutionary process, each agent updates its current strategy with a probability depending on the difference feedback between its actual score and score aspiration. Each agent's score is a weighed mean of its payoff coming from its neighbors (social...
Using a game-theoretic model combined with the evolutionary model, we investigate the conditions under which the desirable interaction rules will evolve and sustain in various social interaction settings. The direction of the research to come is to understand how the interaction structure, the network topology, determines the path of evolutionary dynamics. For the emergence of desirable outcomes at...
We previously proposed evolutionary fuzzy systems of playing Ms. PacMan for the competitions. As a consequence of the evolution, reflective action rules such that PacMan tries to eat pills effectively until ghosts come close to PacMan are acquired. Such rules works well. However, sometimes it is too reflective so that PacMan go toward ghosts by herself in longer corridors. In this paper, a critical...
The game model of prisonerpsilas dilemma is applied to describe the survival game of altruistic behaviors and selfish behaviors. The game model is extended to an evolutionary game. The learning strategies of altruistic and selfish agents, and their survival, update and movement rules are described in detail. Simulation results show that altruists can outperform selfish agents under certain conditions...
Evidently, any learning algorithm can only learn on the basis of the information given to it. This paper presents a first attempt to place an upper bound on the information rates attainable with standard co-evolution and with TDL. The upper bound for TDL is shown to be much higher than for co-evolution. Under commonly used settings for learning to play Othello for example, TDL may have an upper bound...
Texas Hold'em Poker is similar to other poker variants in that our decision process is controlled by outside factors as much as the cards themselves. Factors such as our seating position, stack size, the stage of the tournament and prior bets can strongly influence a players decision to bet or fold on a given hand of cards. Previous research has explored the use of these factors as means of betting...
Cooperative spectrum sensing has been shown to greatly improve the sensing performance in cognitive radio networks. However, if the cognitive users belong to different service providers, they tend to contribute less in sensing in order to achieve a higher throughput. In this paper, we propose an evolutionary game framework to study the interactions between selfish users in cooperative sensing. We...
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