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A Brain-Inspired Theory of Mind Spiking Neural Network for Reducing Safety Risks of Other Agents

文献类型：期刊论文

作者Zhao, Zhuoya4,5; Lu, Enmeng5; Zhao, Feifei5; Zeng, Yi1,2,3,4,5; Zhao, Yuxuan5
刊名FRONTIERS IN NEUROSCIENCE
出版日期2022-04-14
卷号16页码:13
关键词brain-inspired model safety risks SNNs R-STDP theory of mind
DOI10.3389/fnins.2022.753900
通讯作者Zeng, Yi(yi.zeng@ia.ac.cn)
英文摘要Artificial Intelligence (AI) systems are increasingly applied to complex tasks that involve interaction with multiple agents. Such interaction-based systems can lead to safety risks. Due to limited perception and prior knowledge, agents acting in the real world may unconsciously hold false beliefs and strategies about their environment, leading to safety risks in their future decisions. For humans, we can usually rely on the high-level theory of mind (ToM) capability to perceive the mental states of others, identify risk-inducing errors, and offer our timely help to keep others away from dangerous situations. Inspired by the biological information processing mechanism of ToM, we propose a brain-inspired theory of mind spiking neural network (ToM-SNN) model to enable agents to perceive such risk-inducing errors inside others' mental states and make decisions to help others when necessary. The ToM-SNN model incorporates the multiple brain areas coordination mechanisms and biologically realistic spiking neural networks (SNNs) trained with Reward-modulated Spike-Timing-Dependent Plasticity (R-STDP). To verify the effectiveness of the ToM-SNN model, we conducted various experiments in the gridworld environments with random agents' starting positions and random blocking walls. Experimental results demonstrate that the agent with the ToM-SNN model selects rescue behavior to help others avoid safety risks based on self-experience and prior knowledge. To the best of our knowledge, this study provides a new perspective to explore how agents help others avoid potential risks based on bio-inspired ToM mechanisms and may contribute more inspiration toward better research on safety risks.
WOS关键词SELF-PERSPECTIVE INHIBITION ; SYNAPTIC PLASTICITY ; MODEL
WOS研究方向Neurosciences & Neurology
语种英语
WOS记录号WOS:000795460500001
出版者FRONTIERS MEDIA SA
源URL[http://ir.ia.ac.cn/handle/173211/49440]  
专题类脑智能研究中心_类脑认知计算
通讯作者Zeng, Yi
作者单位1.Chinese Acad Sci, Ctr Excellence Brain Sci & Intelligence Technol, Shanghai, Peoples R China
2.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing, Peoples R China
3.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing, Peoples R China
4.Univ Chinese Acad Sci, Sch Future Technol, Beijing, Peoples R China
5.Chinese Acad Sci, Inst Automat, Res Ctr Brain Inspired Intelligence, Beijing, Peoples R China
推荐引用方式
GB/T 7714		 Zhao, Zhuoya,Lu, Enmeng,Zhao, Feifei,et al. A Brain-Inspired Theory of Mind Spiking Neural Network for Reducing Safety Risks of Other Agents[J]. FRONTIERS IN NEUROSCIENCE,2022,16:13.
APA Zhao, Zhuoya,Lu, Enmeng,Zhao, Feifei,Zeng, Yi,&Zhao, Yuxuan.(2022).A Brain-Inspired Theory of Mind Spiking Neural Network for Reducing Safety Risks of Other Agents.FRONTIERS IN NEUROSCIENCE,16,13.
MLA Zhao, Zhuoya,et al."A Brain-Inspired Theory of Mind Spiking Neural Network for Reducing Safety Risks of Other Agents".FRONTIERS IN NEUROSCIENCE 16(2022):13.

入库方式： OAI收割

来源：自动化研究所

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