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Approaching (super)human intent recognition in stag hunt with the Naïve Utility Calculus generative model

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Abstract

The human ability to utilize social and behavioral cues to infer each other’s intents, infer motivations, and predict future actions is a central process to human social life. This ability represents a facet of human cognition that artificial intelligence has yet to fully mimic and master. Artificial agents with greater social intelligence have wide-ranging applications from enabling the collaboration of human–AI teams to more accurately modelling human behavior in complex systems. Here, we show that the Naïve Utility Calculus generative model is capable of competing with leading models in intent recognition and action prediction when observing stag-hunt, a simple multiplayer game where agents must infer each other’s intentions to maximize rewards. Moreover, we show the model is the first with the capacity to out-compete human observers in intent recognition after the first round of observation. We conclude with a discussion on implications for the Naïve Utility Calculus and of similar generative models in general.

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Acknowledgements

This material is based upon work supported by the Defense Advanced Research Projects Agency (DARPA) under Contract No. HR001120C0036. Any opinions, findings and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of DARPA.

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  1. Human-Centered Artificial Intelligence Research Laboratory, University of Central Florida, 12800 Pegasus Drive, Bldg. 91, Rm. 314, Orlando, FL, 32826-3246, USA

    Lux Miranda & Ozlem Ozmen Garibary

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  1. Lux Miranda
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  2. Ozlem Ozmen Garibary
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Correspondence to Lux Miranda.

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Miranda, L., Garibary, O.O. Approaching (super)human intent recognition in stag hunt with the Naïve Utility Calculus generative model. Comput Math Organ Theory 29, 434–447 (2023). https://doi.org/10.1007/s10588-022-09367-y

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