Abstract
A Theory of Mind (ToM) is a mental representation one agent has of another’s emotion, desires, beliefs, and intentions formed through their interactions which help the agent predict the other’s behaviors. The concept comes from work in cognitive science which addresses questions about the mechanism for inferring motivations behind human behavior. We aim to apply this concept to understand the degree to which we can impart ToM capabilities to artificial agents. While we do not aim to resolve the depths of human emotions, desires, and beliefs, we hope to recreate a proof-of-concept from a recent machine learning application and later scale to more realistic contexts.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Abadi, M., et al.: TensorFlow: large-scale machine learning on heterogeneous systems (2015). https://www.tensorflow.org/. Software available from tensorflow.org
Akula, A.R., et al.: CX-ToM: counterfactual explanations with theory-of-mind for enhancing human trust in image recognition models. iScience 25(1), 103581 (2022). https://doi.org/10.1016/j.isci.2021.103581. https://www.sciencedirect.com/science/article/pii/S2589004221015510
Baker, C., Saxe, R., Tenenbaum, J.: Bayesian theory of mind: modeling joint belief-desire attribution. In: Proceedings of the Annual Meeting of the Cognitive Science Society, vol. 33 (2011)
Baron-Cohen, S., Leslie, A.M., Frith, U.: Does the autistic child have a “theory of mind”? Cognition 21(1), 37–46 (1985). https://doi.org/10.1016/0010-0277(85)90022-8
Chen, B., Vondrick, C., Lipson, H.: Visual behavior modelling for robotic theory of mind. Sci. Rep. 11(1), 1–14 (2021)
Diaconescu, A.O., et al.: Inferring on the intentions of others by hierarchical Bayesian learning. PLOS Comput. Biol. 10(9), 1–19 (2014). https://doi.org/10.1371/journal.pcbi.1003810
Girase, H., et al.: LOKI: long term and key intentions for trajectory prediction. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9803–9812 (2021)
Liu, B., et al.: Spatiotemporal relationship reasoning for pedestrian intent prediction. IEEE Rob. Autom. Lett. 5(2), 3485–3492 (2020)
Ng, A.Y., Russell, S., et al.: Algorithms for inverse reinforcement learning. In: ICML, vol. 1, p. 2 (2000)
Nguyen, T.N., Gonzalez, C.: Cognitive machine theory of mind, Technical report. Carnegie Mellon University (2020)
Nguyen, T.N., Gonzalez, C.: Theory of mind from observation in cognitive models and humans. Top. Cogn. Sci. (2021). https://doi.org/10.1111/tops.12553. https://onlinelibrary.wiley.com/doi/abs/10.1111/tops.12553
Premack, D., Woodruff, G.: Does the chimpanzee have a theory of mind? Behav. Brain Sci. 1(4), 515–526 (1978). https://doi.org/10.1017/S0140525X00076512
Rabinowitz, N., Perbet, F., Song, F., Zhang, C., Eslami, S.A., Botvinick, M.: Machine theory of mind. In: International Conference on Machine Learning, pp. 4218–4227. PMLR (2018)
Raglin, A., Metu, S., Lott, D.: Challenges of simulating uncertainty of information. In: Stephanidis, C., Antona, M., Ntoa, S. (eds.) HCII 2020. CCIS, vol. 1293, pp. 255–261. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-60700-5_33
Raileanu, R., Denton, E., Szlam, A., Fergus, R.: Modeling others using oneself in multi-agent reinforcement learning. In: Krause, A., Dy, J. (eds.) 35th International Conference on Machine Learning, ICML 2018, pp. 6779–6788 (2018)
Ritter, F.E., Tehranchi, F., Oury, J.D.: ACT-R: a cognitive architecture for modeling cognition. WIREs Cognit. Sci. 10(3) (2019). https://doi.org/10.1002/wcs.1488
Sarkadi, S., Panisson, A., Bordini, R., McBurney, P., Parsons, S., Chapman, M.: Modelling deception using theory of mind in multi-agent systems. AI Commun. 32(4), 287–302 (2019). https://doi.org/10.3233/AIC-190615
Scholl, B.J., Tremoulet, P.D.: Perceptual causality and animacy. Trends Cogn. Sci. 4(8), 299–309 (2000). https://doi.org/10.1016/s1364-6613(00)01506-0
Wilensky, U.: Netlogo itself (1999). http://ccl.northwestern.edu/netlogo/
Yoshida, W., Dolan, R.J., Friston, K.J.: Game theory of mind. PLOS Comput. Biol. 4(12), 1–14 (2008). https://doi.org/10.1371/journal.pcbi.1000254
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Kumar, P., Raglin, A., Richardson, J. (2023). General Agent Theory of Mind: Preliminary Investigations and Vision. In: Degen, H., Ntoa, S. (eds) Artificial Intelligence in HCI. HCII 2023. Lecture Notes in Computer Science(), vol 14051. Springer, Cham. https://doi.org/10.1007/978-3-031-35894-4_37
Download citation
DOI: https://doi.org/10.1007/978-3-031-35894-4_37
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-35893-7
Online ISBN: 978-3-031-35894-4
eBook Packages: Computer ScienceComputer Science (R0)