Reinforcement learning multi-hop reasoning method with GAN network

Zhicai Gao; Xiaoze Gong; Yongli Wang

doi:10.1117/12.2671176

28 April 2023 Reinforcement learning multi-hop reasoning method with GAN network

Zhicai Gao, Xiaoze Gong, Yongli Wang

Proceedings Volume 12610, Third International Conference on Artificial Intelligence and Computer Engineering (ICAICE 2022); 126105J (2023) https://doi.org/10.1117/12.2671176
Event: Third International Conference on Artificial Intelligence and Computer Engineering (ICAICE 2022), 2022, Wuhan, China

Abstract

At present, the academic community has carried out some research on knowledge reasoning using Reinforcement Learning (RL), which has achieved good results in multi-hop reasoning. However, these methods often need to manually design the reward function to adapt to a specific dataset. For different datasets, the reward function in RL-based methods needs to be manually adjusted to obtain good performance. To solve this problem, an agent training model combined with Generative Adversarial Networks (GAN) is proposed. The model consists of two modules: a generative adversarial inference engine and a sampler. The sampler uses a policy-based bidirectional breadth-first search method to find the demonstration path, and the agent uses the reward considering the information of the neighborhood entities as the initial reward function. After sufficient adversarial training between the agent and the discriminator, the policy-based agent can find evidence paths that match the demonstration distribution and synthesize these evidence paths to make predictions. Experiments show that the model achieves better results in both fact prediction and link prediction tasks.

Citation Download Citation

Zhicai Gao, Xiaoze Gong, and Yongli Wang "Reinforcement learning multi-hop reasoning method with GAN network", Proc. SPIE 12610, Third International Conference on Artificial Intelligence and Computer Engineering (ICAICE 2022), 126105J (28 April 2023); https://doi.org/10.1117/12.2671176

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KEYWORDS

Machine learning

Adversarial training

Education and training

Semantics

Statistical modeling

Data modeling

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