Decision-Theoretic Planning in Multi-Agent Environments

这项工作将开发交互式部分可观察马尔可夫决策过程（ipomdp），它为智能体在与其他智能体交互时可以使用的最佳规划提供了框架。i - pomdp将具有不完全信息的博弈元素的决策理论方法统一起来。决策理论规划，形式化为传统的pomdp，在控制理论、人工智能和认知科学中有着广泛的影响。然而，pomdp本身并不适用于交互环境，因为它们会强迫假设其他代理是随机和静态的。另一方面，博弈论的核心是理性主体之间的互动。然而，纳什均衡的博弈论解概念及其变化导致了非唯一和不完整的解。i - pomdp解决了上述决策与博弈论之间的脱节。它们包括在交互过程中对其他代理模型的贝叶斯更新，产生对其他代理预期行为的最佳响应的策略，并且在代理单独行动时减少到经典的pomdp。

This work will develop interactive partially observable Markov decision processes (IPOMDPs), which provide a framework for optimal planning that agents can use while interacting with other agents. I-POMDPs unify decision-theoretic approach to planning with elements of games with incomplete information. Decision-theoretic planning, formalized as traditional POMDPs, has had a wide impact in control theory, artificial intelligence, and cognitive science. However, POMDPs by themselves are not suitable in interactive settings because they would force the assumption that other agents are random and static. Game theory, on the other hand, is centered on interaction among rational agents. However, the game-theoretic solution concept of Nash equilibria and their variations result in solutions that are non-unique and incomplete. I-POMDPs address the above disconnect between decision and game theories. They include Bayesian update over models of other agents during interactions, result in policies that are best responses to the others' expected actions, and reduce to classical POMDPs if the agent is acting alone.