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The decision-making process, which is regarded as cognitive and ubiquitous, has been exploited in diverse fields, such as psychology, economics, and artificial intelligence. This paper considers the problem of modeling agent cognition in a class of game-theoretic decision-making scenarios called extensive games. We present a novel framework in which artificial neural networks are incorporated to simulate agent cognition regarding the structure of the underlying game and the goodness of the game situations therein. An algorithmic procedure is investigated to describe the process for solving games with cognition, and then, a new equilibrium concept is proposed as a refinement of the classical one—subgame perfect equilibrium—by involving players’ cognitive reasoning. Moreover, a series of results concerning the computational complexity, soundness, and completeness of the algorithm, as well as the existence of an equilibrium solution, is obtained. This framework, which is shown to be general enough to model the way in which AlphaGo plays Go, may offer a means for bridging the gap between theoretical models and practical problem-solving. [ABSTRACT FROM AUTHOR]

Abstract: Global exponential stability of nonautonomous cellular neural networks with unbounded delays is considered in this paper. By applying Lyapunov functional method, some new sufficient conditions are given for global exponential stability of solutions of the networks. The stability conditions obtained here improve and extend some of the previous conditions. An example is presented to illustrate the applicability of these conditions. [Copyright &y& Elsevier]

Abstract: In this paper, by utilizing a delay differential inequality and combining with inequality analysis technique, we investigate global exponential stability for nonautonomous neural networks with variable delays. Some new sufficient conditions ensuring global exponential stability are obtained. An example is also given to demonstrate the effectiveness of the obtained results. [Copyright &y& Elsevier]

Abstract: Delay-dependent exponential stability of non-autonomous cellular neural networks with delays is considered in this paper. Based on the differential inequality technique as well as a fact about Young inequality, some new sufficient conditions are given for global exponential stability of non-autonomous cellular neural networks with delays. The conditions rely on the size of time-delay. Since the results presented here do not require the differentiability of variable delay, they are less conservative than those established in the earlier references. An example is given to illustrate the applicability of these conditions. [Copyright &y& Elsevier]

Abstract: Global asymptotic stability and exponential stability of delayed cellular neural networks is considered in this paper. Based on the Lyapunov stability theorem as well as a fact about the elemental inequality, some new sufficient conditions are given for global asymptotic stability and exponential stability of delayed cellular neural networks. The results are less conservative than those established in the earlier references. Three examples are given to illustrate the applicability of these conditions. [Copyright &y& Elsevier]

Abstract: Some sufficient conditions for the global exponential stability of cellular neural networks with variable delay are obtained by means of a method based on delay differential inequality. The method, which does not make use of Lyapunov functionals, is simple and effective for the stability analysis of neural networks with delay. Some previously established results in the literature are shown to be special cases of the presented result. [Copyright &y& Elsevier]

Abstract: Global exponential stability of nonautonomous delayed neural networks is considered. Without assuming the differentiability of delay, several criteria are derived by making use of a differential inequality. An example is illustrated to show the applicability of our results. [Copyright &y& Elsevier]

Abstract: The global exponential stability of cellular neural networks (CNNs) with time-varying delays is analyzed. Two new sufficient conditions ensuring global exponential stability for delayed CNNs are obtained. The conditions presented here are related to the size of delay. The stability results improve the earlier publications. Two examples are given to demonstrate the effectiveness of the obtained results. [Copyright &y& Elsevier]