How to apply deep reinforcement learning to multi-dimensional intelligent reasoning

Guobin Wu; Jian Liang; Xi Jin; Xiao Wang; Zhewen Zhang

doi:10.1117/12.2645930

23 August 2022 How to apply deep reinforcement learning to multi-dimensional intelligent reasoning

Guobin Wu, Jian Liang, Xi Jin, Xiao Wang, Zhewen Zhang

Proceedings Volume 12305, International Symposium on Artificial Intelligence Control and Application Technology (AICAT 2022); 123050W (2022) https://doi.org/10.1117/12.2645930
Event: International Symposium on Artificial Intelligence Control and Application Technology (AICAT 2022), 2022, Hangzhou, China

Abstract

With the rapid development of artificial intelligence, the research on perceptual intelligence is becoming more and more mature. For the next stage of cognitive intelligence research, knowledge graph (KG) is one of the key directions. Knowledge reasoning is an important part of knowledge graph and has a wide range of application requirements. To solve the problems of current large-scale KG, including poor interpretability, low accuracy and efficiency of knowledge reasoning, this paper proposes a method RLPTransE which combines knowledge representation, relational path with deep reinforcement learning. This method generates a path vector by semantically combining the vectors of all relations on the path. Then, a reinforcement learning environment is established in the vector space, and through training a multi-step inference policy network based on diverse reward functions, the reinforcement learning agent can efficiently complete inference in the process of interacting with the environment. This paper conducted link prediction and fact prediction experiments with the RLPTransE method on NELL-995 and FB15K-237 datasets, and compared with knowledge representation-based, relational path-based and fusion-based methods, the experimental results show that RLPTransE method achieves better performance on large-scale dataset inference tasks.

Citation Download Citation

Guobin Wu, Jian Liang, Xi Jin, Xiao Wang, and Zhewen Zhang "How to apply deep reinforcement learning to multi-dimensional intelligent reasoning", Proc. SPIE 12305, International Symposium on Artificial Intelligence Control and Application Technology (AICAT 2022), 123050W (23 August 2022); https://doi.org/10.1117/12.2645930

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KEYWORDS

Data modeling

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Performance modeling

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Vector spaces

Neural networks

Reliability

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