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Population dynamics have been widely used in the design of learning and control systems for networked engineering applications, where the information dependency among elements of the network has become a relevant issue. Classic population dynamics (e.g., replicator, logit choice, Smith, and projection) require full information to evolve to the solution (Nash equilibrium). The main reason is that classic population dynamics are deduced by assuming well-mixed populations, which limits the applications where this theory can be implemented. In this paper, we extend the concept of population dynamics for nonwell-mixed populations in order to deal with distributed information structures that are characterized by noncomplete graphs. Although the distributed population dynamics proposed in this paper use partial information, they preserve similar characteristics and properties of their classic counterpart. Specifically, we prove mass conservation and convergence to Nash equilibrium. To illustrate the performance of the proposed dynamics, we show some applications in the solution of optimization problems, classic games, and the design of distributed controllers. [ABSTRACT FROM PUBLISHER]

In recent years, electric vehicle charging stations (EVCSs) have been deployed very fast, which offer a platform for the energy coordination between electric vehicles (EVs) and the electricity distribution system (DS). This paper presents an energy management method for the operation of EVCSs and DS while considering their interaction. In this method, we first use a quality-of-service constrained decision model for EVCSs, which is formulated based on the network calculus theory. Then, the DS aims to maximize its utility by scheduling charging plans for EVCSs. Afterwards, a game based on the supply function equilibrium (SFE) model is adopted to analyze the energy interaction, in which each EVCS is regarded as the leader, and the DS as the follower. In this way, an optimization problem with equilibrium constraints is established. Moreover, a hybrid optimization algorithm based on differential evolution and interior point method is used to obtain the equilibrium solution of the SFE game. Finally, simulation studies verify the effectiveness of proposed interactive energy management method. [ABSTRACT FROM AUTHOR]

In this paper, we address the problem of associating mobile stations (MSs) with base stations (BSs) in an energy-efficient manner. We take a population game approach, which allows tractable analysis of many selfish mobiles without growing mathematical complexity. From our game-theoretical analysis, we prove that a simple power-dependent pricing by operators leads a Nash equilibrium to be equal to the optimal solution of a social optimization problem (i.e., no price-of-anarchy). We study three evolution dynamics of associating MSs, each expressed as a differential equation, all of which provably and/or numerically converge to the Nash equilibrium. Based on several considerations regarding implementation of association algorithms in practice, we found that asynchronicity and fast load tracking are the key components to practical algorithms. Motivated by this, we propose a practical energy-efficient user association mechanism, named BRUTE. To evaluate the performance of BRUTE, we implement a cellular network simulator using an event-driven simulator, SimPy, and perform extensive simulations under various scenarios including a real BS topology in U.K. Our simulation results show that BRUTE outperforms other conventional user association techniques. [ABSTRACT FROM PUBLISHER]

One weakness of coevolutionary algorithms observed in knowledge-free learning of strategies for adversarial games has been their poor scalability with respect to the number of parameters to learn. In this paper, we investigate to what extent this problem can be mitigated by using Covariance Matrix Adaptation Evolution Strategy, a powerful continuous optimization algorithm. In particular, we employ this algorithm in a competitive coevolutionary setup, denoting this setting as Co-CMA-ES. We apply it to learn position evaluation functions for the game of Othello and find out that, in contrast to plain (co)evolution strategies, Co-CMA-ES learns faster, finds superior game-playing strategies and scales better. Its advantages come out into the open especially for large parameter spaces of tens of hundreds of dimensions. For Othello, combining Co-CMA-ES with experimentally-tuned derandomized systematic n-tuple networks significantly improved the current state of the art. Our best strategy outperforms all the other Othello 1-ply players published to date by a large margin regardless of whether the round-robin tournament among them involves a fixed set of initial positions or the standard initial position but randomized opponents. These results show a large potential of CMA-ES-driven coevolution, which could be, presumably, exploited also in other games. [ABSTRACT FROM PUBLISHER]

Agents for playing iterated prisoner's dilemma are commonly trained using a coevolutionary system in which a player's score against a selection of other members of an evolving population forms the fitness function. In this study we examine instead a version of evolutionary iterated prisoner's dilemma in which an agent's fitness is measured as the average score it obtains against a fixed panel of opponents called an examination board. The performance of agents trained using examination boards is compared against agents trained in the usual coevolutionary fashion. This includes assessing the relative competitive ability of players evolved with evolution and coevolution. The difficulty of several experimental boards as optimization problems is compared. A number of new types of strategies are introduced. These include sugar strategies which can be exploited with some difficulty and treasure hunt strategies which have multiple trapping states with different levels of exploitability. The degree to which strategies trained with different examination boards produce different agents is investigated using fingerprints. [ABSTRACT FROM PUBLISHER]