协同作战能力简析

协同作战能力(CEC)是一种充分利用网络技术的新型海岸防空技术，给系统的作战能力带来了革命性的交破。本文对协同作战能力的产生、功能、组成以及未来的发展趋势作了详细的介绍，阐述了CEC系统的总体设计思想。在此基础上，将CEC系统与传统的作战系统的性能和设计思想在各方面进行了比较，交出了CEC系统各方面的优越性能。协同作战能力是现代和未来作战系统的发展方向，CEC系统的发展必将成为衡量各国国防科技现代化的重要指标。     

移动运营商手机游戏业务的战略定位和发展策略探讨

分析了韩国、日本及国内运营商手机游戏业务的发展状况．探讨了手机游戏业务的模式．论述了该业务的战略定位和市场前景。从地区提供给移动通信市场对外部环境、产业链的准备是否充分以及运营商内部的资源和运营状况等三个方面阐明了手机游戏业务发展的关键因素。     

On Quantum Team Games

Recently Liu and Simaan (2004) convex static multi-team classical games have been introduced. Here they are generalized to both nonconvex, dynamic and quantum games. Puu's incomplete information dynamical systems are modified and applied to Cournot team game. The replicator dynamics of the quantum prisoner's dilemma game is also studied.     

Maximum Shannon Entropy, Minimum Fisher Information, and an Elementary Game

We formulate an elementary statistical game which captures the essence of some fundamental quantum experiments such as photon polarization and spin measurement. We explore and compare the significance of the principle of maximum Shannon entropy and the principle of minimum Fisher information in solving such a game. The solution based on the principle of minimum Fisher information coincides with the solution based on an invariance principle, and provides an informational explanation of Malus' law for photon polarization. There is no solution based on the principle of maximum Shannon entropy. The result demonstrates the merits of Fisher information, and the demerits of Shannon entropy, in treating some fundamental quantum problems. It also provides a quantitative example in support of a general philosophy: Nature intends to hide Fisher information, while obeying some simple rules.     

凸合成模糊对策的模糊稳定集

本建立了凸合成模糊对策的模型，并得到了凸合成模糊对策的模糊稳定集，可由子对策的模糊稳定集表达出来。从而解决了凸合成模糊对策的解的结构问题。     

基于CSCW的协同编著系统的设计

讨论了计算机支持的协同工作(CSCW)的主要技术，其中重点讨论了协同编著系统设计中的并发控制技术，指出了其与传统的并发控制技术的区别。根据CSCW的基本原理设计出了一个基于Browse／Server结构的协同编著系统的系统模型。该系统模型比传统的模型更加方便了用户之间通过Internet的浏览器进行协作。     

An approach to deciding the observational equivalence of Algol-like languages

We prove that the observational equivalence of third-order finitary (i.e. recursion-free) Idealized Algol (IA) is decidable using Game Semantics. By modelling the state explicitly in our games, we show that the denotation of a term M of this fragment of IA is a compactly innocent strategy-with-state, i.e. the strategy is generated by a finite view function f_M. Given any such f_M, we construct a real-time deterministic pushdown automaton (DPDA) that recognizes the complete plays of the knowing-strategy denotation of M. Since such plays characterize observational equivalence, and there is an algorithm for deciding whether any two DPDAs recognize the same language, we obtain a procedure for deciding the observational equivalence of third-order finitary IA. Restricted to second-order terms, the DPDA representation cuts down to a deterministic finite automaton; thus our approach gives a new proof of Ghica and McCusker’s regular-expression characterization for this fragment. Our algorithmic representation of program meanings, which is compositional, provides a foundation for model-checking a wide range of behavioural properties of IA and other cognate programming languages. Another result concerns second-order IA with full recursion: we show that observational equivalence for this fragment is undecidable.     

逆（反）对策问题与“奇门遁甲”预测理论（术）

本文首先提出逆（反）对策这一新问题，给出了数学模型；探讨了“奇门遁甲”预测理论（术）中的数学问题；通过系统分析“专门遁甲”预测过程，可知它的预测过程隐含着一个特殊的逆（反）对策问题；最后指出逆（反）对策问题的广泛存在并给出案例分析．     

Action learning versus strategy learning

This article seeks to ascertain whether the strategy‐learning model of Hanaki, Sethi, Erev, and Peterhansl (2003) better accounts for observed behavior than do the various action‐learning models. It does so by measuring the goodness‐of‐fit of the models' predictions against published experimental results for such games as Coordination, Prisoner's Dilemma, and Chicken. The fit is measured via the mean squared deviation (MSD) between the observed behavior and the one predicted by the model. The results show that, for Chicken, the strategy‐learning model fits the observed data much better than do the action‐learning models. The best action‐learning model, on the other hand, fits the observed data well in Coordination. Overall, the strength of the strategy‐learning model is best shown in games where alternations between the two stage‐game Nash equilibria are often observed in the laboratory experiments. © 2004 Wiley Periodicals, Inc. Complexity 9: 41–50, 2004