Towards Data-driven Solutions to Interactive Dynamic Influence Diagrams

Pan, Yinghui, Tang, Jing, Ma, Biyang, Zeng, Yifeng and Ming, Zhong (2021) Towards Data-driven Solutions to Interactive Dynamic Influence Diagrams. Knowledge and Information Systems. ISSN 0219-1377 (In Press)

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Abstract

With the availability of significant amount of data, data-driven decision making becomes an alternative way for solving complex multiagent decision problems. Instead of using domain knowledge to explicitly build decision models, the data driven approach learns decisions (probably optimal ones) from available data. This removes the knowledge bottleneck in the traditional knowledge-driven decision making, which requires a strong support from domain experts. In this paper, we study data-driven decision making in the context of interactive dynamic influence diagrams (I-DIDs) - a general framework for multiagent sequential decision making under uncertainty. We propose a data-driven framework to solve the I-DIDs model and focus on learning the behavior of other agents in problem domains. The challenge is on learning a complete policy tree that will be embedded in the I-DIDs models due to limited data. We propose two new methods to develop complete policy trees for the other agents in the I DIDs. The first method uses a simple clustering process while the second one employs sophisticated statistical checks. We analyze the proposed algorithms in a theoretical way and experiment them over two problem domains.

Item Type: Article
Uncontrolled Keywords: Data-driven, I-DIDs, multiagent sequential decision
Subjects: G900 Others in Mathematical and Computing Sciences
N100 Business studies
Department: Faculties > Business and Law > Newcastle Business School
Faculties > Engineering and Environment > Computer and Information Sciences
Depositing User: John Coen
Date Deposited: 02 Jul 2021 10:09
Last Modified: 02 Jul 2021 10:15
URI: http://nrl.northumbria.ac.uk/id/eprint/46589

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