合理性与随机性：两人博弈中的丰富行为模型
Reconciling Rationality and Stochasticity: Rich Behavioral Models in Two-Player Games
摘要：
在多智能体复杂系统中，通常采用两种传统的范式来描述智能体的行为。在第一种情况下，代理人被认为是完全理性的，系统被视为多人游戏。在第二种情况下，代理被认为是完全随机过程，系统本身被视为一个大的随机过程。从一个特定的代理人的角度来看-必须选择一种策略，范式的选择是至关重要的：最适当的策略取决于对其他代理人的假设。在本文中，我们专注于两个球员的游戏和他们的应用程序的反应系统的可靠控制器的自动合成-一个领域之间的交叉路口计算机科学和数学。在这种情况下，要控制的反应系统是一个玩家，而它的环境是它的对手，通常被假设为完全对抗或完全随机。我们说明了几个最近的发展，旨在打破这种狭隘的分类，提供正式的概念和数学框架的原因更丰富的行为模型。这种模型的兴趣不仅限于反应系统的综合，但扩展到其他应用领域的博弈论。我们的贡献的目标是给一个高层次的介绍关键概念和应用程序，针对广泛的受众。为了实现这一目标，我们将这些丰富的行为模型应用于日常生活中的一个经典挑战：在不确定的环境中规划旅程。
英文摘要：
Two traditional paradigms are often used to describe the behavior of agents in multi-agent complex systems. In the first one, agents are considered to be fully rational and systems are seen as multi-player games. In the second one, agents are considered to be fully stochastic processes and the system itself is seen as a large stochastic process. From the standpoint of a particular agent - having to choose a strategy, the choice of the paradigm is crucial: the most adequate strategy depends on the assumptions made on the other agents. In this paper, we focus on two-player games and their application to the automated synthesis of reliable controllers for reactive systems - a field at the crossroads between computer science and mathematics. In this setting, the reactive system to control is a player, and its environment is its opponent, usually assumed to be fully antagonistic or fully stochastic. We illustrate several recent developments aiming to breach this narrow taxonomy by providing formal concepts and mathematical frameworks to reason about richer behavioral models. The interest of such models is not limited to reactive system synthesis but extends to other application fields of game theory. The goal of our contribution is to give a high-level presentation of key concepts and applications, aimed at a broad audience. To achieve this goal, we illustrate those rich behavioral models on a classical challenge of the everyday life: planning a journey in an uncertain environment.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
