资源论文Machine Learning Techniques for MultiAgent Systems

Machine Learning Techniques for MultiAgent Systems

2019-10-29 | |  71 |   60 |   0
Research in artificial intelligence ranges over many subdisciplines, such as Natural Language Processing, Computer Vision, Machine Learning, and MultiAgent Systems. Recently, AI techniques have become increasingly robust and complex, and there has been enhanced interest in research at the intersection of seemingly disparate research areas. Such work is motivated by the observation that there is actually a great deal of commonality among areas, that can be exploited within subfields. One example of a successful combination is the intersection of machine learning and multiagent systems. For example, [Kearns et al., 2001] proposed an efficient graphical model-based algorithm for calculating Nash equilibria. Going in the other direction, [Datta et al., 2015] showed that solution concepts from cooperative game theory can be used to uniquely characterize the influence measure of classifiers.

上一篇:Logic meets Probability: Towards Explainable AI Systems for Uncertain Worlds?

下一篇:Modeling Bias Reduction Strategies in a Biased Agent

用户评价
全部评价

热门资源

  • A Mathematical Mo...

    Direct democracy, where each voter casts one vo...

  • Learning to Predi...

    Much of model-based reinforcement learning invo...

  • The Variational S...

    Unlike traditional images which do not offer in...

  • Hierarchical Task...

    We extend hierarchical task network planning wi...

  • Shape-based Autom...

    We present an algorithm for automatic detection...