期刊
COMPUTERS & MATHEMATICS WITH APPLICATIONS
卷 64, 期 12, 页码 3658-3665出版社
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.camwa.2012.01.079
关键词
Multiagent systems; Multiagent-based simulation; Machine learning; Inferential theory of learning; Autonomous logistics
资金
- German Research Foundation (DFG) within the Collaborative Research Centre 637
- National Science Foundation
- National Institute of Standards and Technology
Multiagent-based simulation is an approach, to realize stochastic simulation where both the behavior of the modeled multiagent system and dynamic aspects of its environment are implemented with autonomous agents. Such simulation provides an ideal environment for intelligent agents to learn to perform their tasks before being deployed in a real-world environment. The presented research investigates theoretical and practical aspects of learning by autonomous agents within stochastic agent-based simulation. The theoretical work is based on the Inferential Theory of Learning, which describes learning processes from the perspective of a learner's goal as a search through knowledge space. The theory is extended for approximate and probabilistic learning to account for the situations encountered when learning in stochastic environments. Practical aspects are exemplified by two use cases in autonomous logistics: learning predictive models for environment conditions in the future, and learning in the context of evolutionary plan optimization. (C) 2012 Elsevier Ltd. All rights reserved.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据