Research In Multi-Agent Learning Algorithms

涉及多智能体的广泛理论问题 将研究学习系统。 合作是关键 成功完成任务时，多个代理， 涉案 注意力将局限于学习系统 在多个代理合作学习概念（或 功能）。 最终目标是设计模板， 合作学习策略，希望可以应用 其他实际问题。 目标将通过以下方式实现： 调查具有不同收敛性的学习者，和/或 正确性标准以及其他特征， 假设程序的总体性和资源的有限性。 这种广泛的学习者类型将有助于 确定合作的真正性质和复杂性 在多智能体学习系统中， 在 特别是，正在进行的结构调查， 概率和多元层次的FIN型（和 随后在其他类型的）学习将继续， 它最近揭露了一个新的但非常亲密的合作 战略

A broad range of theoretical issues dealing with multi-agent learning systems will be investigated. Cooperation is the key to successfully completing a task when more than one agent is involved. Attention will be restricted to learning system where multiple agents cooperate in learning a concept (or function). The ultimate goal is to devise templates for cooperating learning strategies which hopefully can be applied to other practical problems. The goal will be approached by investigating learners with differing convergence and/or correctness criteria as well as other features such as totality of hypothesized programs and resource boundedness. This wide spectrum of type of learners will help in identifying the true nature and complexity of the cooperation one should expect in multi-agent learning systems. In particular, an on-going investigation of the structure of probabilistic and pluralistic hierarchy in FIN-type (and subsequently in other types of)learning will be continued, which recently revealed a new but very intimate cooperative strategy.