随着深度学习和强化学习而来的人工智能新浪潮, 为智能体从感知输入到行动决策输出提供了“
端到端” 解决方案. 多智能体学习是研究智能博弈对抗的前沿课题, 面临着对抗性环境 …

Game theory studies the mathematical models for self-interested individuals. Nash
equilibrium is arguably the most central solution in game theory. While finding the Nash …

Multi-agent evaluation aims at the assessment of an agent's strategy on the basis of
interaction with others. Typically, existing methods such as $\alpha $-rank and its …

Rating strategies in a game is an important area of research in game theory and artificial
intelligence, and can be applied to any real-world competitive or cooperative setting …

The Elo rating system is widely adopted to evaluate the skills of (chess) game and sports
players. Recently it has been also integrated into machine learning algorithms in evaluating …

Nash Equilibrium (NE) is the canonical solution concept of game theory, which provides an
elegant tool to understand the rationalities. Though mixed strategy NE exists in any game …

Currently, decarbonization has become an emerging trend in the power system arena.
However, the increasing number of photovoltaic units distributed into a distribution network …

Under the purpose of achieving the optimal voltage control strategy in power grid system,
multi-agent evaluation algorithms like a-rank are widely used. However, in large-scale …

Game theory largely rests on the availability of cardinal utility functions. In contrast, only
ordinal preferences are elicited in fields such as matching under preferences. The literature …

Recent successes in two-player, purely competitive (so-called zero-sum) games has made
headlines around the world. Initially, perfect information games, with vast state spaces, such …