Deceptive AI

Project overview

An etching of an old automatic chess playing machine

Can computers deceive people? It is clear that computers can be used as tools for people to deceive each other (eg, fake news, phishing, etc), but is it possible for a specially designed AI agent to engage in strategic deception? In other words, can a machine devise and enact deeply deceptive strategies against humans by reasoning about their perceptions, beliefs and intentions? In what kind of human–machine encounters might this be possible? What would be the nature of the machine’s computational and cognitive architecture? How do people understand the possibilities of such machine deception and how do they react to it?

We are a team of computer scientists, psychologists, and magicians who are collaborating to explore these questions. Our methodology is to formalise the techniques of deception used by stage conjurors (for example see Kuhn, Olson & Raz, 2016) such that they can be built into the thinking processes of software agents, and to test the deceptive powers of these agents when playing computer games against humans (See Smith, Dignum & Sonenberg, 2016). The project sheds light on what it means for a computer to intentionally deceive people, and provides insights into the capabilities of software agents to deploy advanced ‘theory-of-mind’ reasoning in human-machine encounters.

Project team

Wally Smith, Faculty of Engineering & Information Technology, The University of Melbourne

Liz Sonenberg, Faculty of Engineering & Information Technology, The University of Melbourne

Michael Kirley, Faculty of Engineering & Information Technology, The University of Melbourne

Frank Dignum, Department of Computing Science, Umeå University, Sweden

Gustav Kuhn, Department of Psychology, Goldsmiths, University of London

Peta Masters, Faculty of Engineering & Information Technology, The University of Melbourne

Publications

Masters, P. & Vered, M., (2021, August). What’s the context? Implicit and Explicit Assumptions in Model-Based Goal Recognition. In Proceedings of the 30th International Joint Conferences on Artificial Intelligence (to appear). [View abstract]

Smith, W. (2021). Deceptive Strategies in the Miniature Illusions of Close-Up Magic. In Rein, K. (Ed.), Productive Deceptions: Illusions Between Entertainment and Experiment (to appear).

Liu, Z., Yang, Y., Miller, T., & Masters, P. (2021, May). Deceptive Reinforcement Learning for Privacy-Preserving Planning. In Proceedings of the 20th International Conference on Autonomous Agents and MultiAgent Systems (pp. 818–826). [View abstract]

Masters, P., Kirley, M., & Smith, W. (2021, May). Extended Goal Recognition: A Planning-Based Model for Strategic Deception. In Proceedings of the 20th International Conference on Autonomous Agents and MultiAgent Systems (pp 871–879). [View abstract]

Masters, P., & Sardina, S. (2021). Expecting the unexpected: Goal recognition for rational and irrational agents. Artificial Intelligence, 297, 103490. [View abstract]

Smith, W., Dignum, F. & Sonenberg, L. (2016). The construction of impossibility: a logic-based analysis of conjuring tricks. Frontiers in psychology, 7, 748. [View abstract]

Project information

Funding source ARC Grant DP180101215 ‘A Computational Theory of Strategic Deception’
Project time frame 2018–2020

Contact details