Tensor decomposition for multi-agent predictive state representation. (1st March 2022)

• Record Type: Journal Article
• Authors: Ma, Biyang; Chen, Bilian; Zeng, Yifeng; Tang, Jing; Cao, Langcai
• Is Part Of: Expert systems with applications. Volume 189(2022)
• Publication Date: 2022-03-01
• Subjects: Predictive state representations -- Tensor optimization -- Learning approaches; Expert systems (Computer science) -- Periodicals; Systèmes experts (Informatique) -- Périodiques; Electronic journals; 006.33
• DOI: 10.1016/j.eswa.2021.115969
• Languages: English
• ISSNs: 0957-4174

Abstract

Abstract: Predictive state representation (PSR) uses a vector of action-observation sequence to represent the system dynamics and subsequently predicts the probability of future events. It is a concise knowledge representation that is well studied in a single-agent planning problem domain. To the best of our knowledge, there is no existing work on using PSR to solve multi-agent planning problems. Learning a multi-agent PSR model is quite difficult especially with the increasing number of agents, not to mention the complexity of a problem domain. In this paper, we resort to tensor techniques to tackle the challenging task of multi-agent PSR model development problems. By first focusing on a two-agent setting, we construct the system dynamics matrix as a high order tensor for a PSR model, learn the prediction parameters and deduce state vectors directly through two different tensor decomposition methods respectively, and derive the transition parameters via linear regression. Subsequently we generalize the PSR learning approaches in a multi-agent setting. Experimental results show that our methods can effectively solve multi-agent PSR modelling problems in multiple problem domains. Highlights: Propose a tensor-based predictive state representation (PSR). Develop the new multi-agent PSR model based on two tensor decompositions methods. Use a system dynamics tensor for learning a multi-agent PSR model. Compare the new model with state-of-the-art algorithms on multiple problem domains.