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

doi:10.3389/fnins.2022.753900

CORC > 自动化研究所 > 中国科学院自动化研究所 > 类脑智能研究中心 > 类脑认知计算

	A Brain-Inspired Theory of Mind Spiking Neural Network for Reducing Safety Risks of Other Agents
	Zhao, Zhuoya 4,5; Lu, Enmeng 5; Zhao, Feifei 5; Zeng, Yi 1,2,3,4,5; Zhao, Yuxuan 5
刊名	FRONTIERS IN NEUROSCIENCE
	2022-04-14
卷号	16 页码:13
关键词	brain-inspired model safety risks SNNs R-STDP theory of mind
DOI	10.3389/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
出版者	FRONTIERS MEDIA SA
WOS记录号	WOS:000795460500001
内容类型	期刊论文
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.