Towards a simple robotic theory of mind

Authors:
Kyung-Joong Kim;Hod Lipson
Affiliations:
Cornell University, Ithaca, NY and Sejong University, Seoul, Korea;Cornell University, Ithaca, NY
Venue:
PerMIS '09 Proceedings of the 9th Workshop on Performance Metrics for Intelligent Systems
Year:
2009

Citing 8
Cited 0

Reading a Robot's Mind: A Model of Utterance Understanding Based on the Theory of Mind Mechanism

Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
Foundations for a theory of mind for a humanoid robot

Foundations for a theory of mind for a humanoid robot
Learning From and About Others: Towards Using Imitation to Bootstrap the Social Understanding of Others by Robots

Artificial Life
Theory of mind (ToM) on robots: a functional neuroimaging study

Proceedings of the 3rd ACM/IEEE international conference on Human robot interaction
Toward Logic-Based Cognitively Robust Synthetic Characters in Digital Environments

Proceedings of the 2008 conference on Artificial General Intelligence 2008: Proceedings of the First AGI Conference
Towards a "theory of mind" in simulated robots

Proceedings of the 11th Annual Conference Companion on Genetic and Evolutionary Computation Conference: Late Breaking Papers
PsychSim: modeling theory of mind with decision-theoretic agents

IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Theory of mind as a theoretical prerequisite to model communication with virtual humans

ZiF'06 Proceedings of the Embodied communication in humans and machines, 2nd ZiF research group international conference on Modeling communication with robots and virtual humans

Quantified Score

Hi-index	0.00

Visualization

Abstract

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.