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Inference of Other's Minds with Limited Information in Evolutionary Robotics

INTERNATIONAL JOURNAL OF SOCIAL ROBOTICS 2021年第4期13卷 661-676页

作者： Kim, Kyung-Joong Cho, Sung-Bae GIST Sch Integrated Technol Gwangju South Korea Yonsei Univ Dept Comp Sci Seoul South Korea

Theory of mind (ToM) is the ability to understand others' mental states (e.g., intentions). Studies on human ToM show that the way we understand others' mental states is very efficient, in the sense that observing only some portion of others' behaviors can lead to successful performance. Recently, ToM has gained interest in robotics to build robots that can engage in complex social interactions. Although it has been shown that robots can infer others' internal states, there has been limited focus on the data utilization of ToM mechanisms in robots. Here we show that robots can infer others' intentions based on limited information by selectively and flexibly using behavioral cues similar to humans. To test such data utilization, we impaired certain parts of an actor robot's behavioral information given to the observer, and compared the observer's performance under each impairment condition. We found that although the observer's performance was not perfect compared to when all information was available, it could infer the actor's mind to a degree if the goal-relevant information was intact. These results demonstrate that, similar to humans, robots can learn to infer others' mental states with limited information.

关键词： Evolutionary robotics estimation-exploration algorithm Neural network Physics-based simulation Information loss

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Inference of other's internal neural models from active observation

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BIOSYSTEMS 2015年 128卷 37-47页

作者： Kim, Kyung-Joong Cho, Sung-Bae Sejong Univ Dept Comp Sci & Engn Seoul South Korea Yonsei Univ Dept Comp Sci Seoul 120749 South Korea

Recently, there have been several attempts to replicate theory of mind, which explains how humans infer the mental states of other people using multiple sensory input, with artificial systems. One example of this is a robot that observes the behavior of other artificial systems and infers their internal models, mapping sensory inputs to the actuator's control signals. In this paper, we present the internal model as an artificial neural network, similar to biological systems. During inference, an observer can use an active incremental learning algorithm to guess an actor's internal neural model. This could significantly reduce the effort needed to guess other people's internal models. We apply an algorithm to the actor observer robot scenarios with/without prior knowledge of the internal models. To validate our approach, we use a physics-based simulator with virtual robots. A series of experiments reveal that the observer robot can construct an "other's self-model", validating the possibility that a neural-based approach can be used as a platform for learning cognitive functions. (C) 2015 Elsevier Ireland Ltd. All rights reserved.

关键词： Theory of mind Robot Physics-based simulation Active learning Neural network Evolutionary computation estimation-exploration algorithm

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Self Identification and Control of Four-leg Robot Based on biological evolutionary mechanisms

Self Identification and Control of Four-leg Robot Based on b...

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5th IEEE Conference on Industrial Electronics and Applications

作者： Liang, Jianhong Xue, Chenghao Beihang Univ Inst Robot Beijing Peoples R China

ISBN: (纸本)9781424450466

This paper provides a method of self identification for robot model, and applies it to four-leg robot's self identification of legs' length. The majority of robots are based on some certain models, which once damaged, may lead to the task fail of robots. The algorithm provided in this paper enables the robot to identify its model, and then the model is applied to create its behavior. This method can keep the robot from total incapacitation when certain damage occurred in the robot.

关键词： self identification estimation-exploration algorithm Four-leg robot

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Towards a simple Robotic Theory of Mind 09

Towards a simple Robotic Theory of Mind

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Performance metrics for intelligent systems workshop

作者： Kyung-Joong Kim Hod Lipson Cornell University Ithaca NY and Sejong University Seoul Korea Cornell University Ithaca NY

ISBN: (纸本)9781605587479

Theory of mind (ToM) is a cognitive function in which an agent can infer another agent's internal state and intention based on their behaviors. Can robots realize ToM like humans? There are many issues to be tackled to address this challenging problem, such as the representation, discovery and exploitation of an actor's self models. In this paper we study how robots can represent other's self with artificial neural networks and an evolutionary learning mechanism. This framework was tested with simulated and physical robots and a novel prey-predator scenario was introduced to measure the performance of ToM learning. Experimental results showed that the proposed ToM approach can recover other's self models successfully.

关键词： robotics evolutionary computation estimation-exploration algorithm theory of mind neural network robot test

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