Applied Intelligence

, Volume 39, Issue 4, pp 749–760 | Cite as

Utilizing theory of mind for action selection applied in the domain of fighter pilot training



When developing intelligent agents, approaches that allow the anticipation of other agents is of utmost importance. For humans, this has also been shown to be crucial to establish good interactions. In this paper, a design for an agent that is equipped with theory of mind based reasoning capabilities is presented. The approach moves beyond the state of the art from several angles: first, it allows for the expression of certainties with respect to the predicted states of the other agents. Second, it allows the prediction during a substantial number of time steps in the future, thereby utilizing the theory of mind model multiple times. The approach has been applied to the domain of fighter pilots whereby intelligent opponents are developed to facilitate dedicated training for F16 fighter pilots.


Theory of mind Agent systems Action selection Fighter pilot training 


  1. 1.
    Barringer H, Fisher M, Gabbay D, Owens R, Reynolds M (1996) The imperative future: principles of executable temporal logic. Wiley, New York Google Scholar
  2. 2.
    Baron-Cohen S (1995) Mindblindness. MIT Press, Cambridge Google Scholar
  3. 3.
    Bosse T, Jonker CM, van der Meij L, Sharpanskykh A, Treur J (2009) Specification and verification of dynamics in agent models. Int J Coop Inf Syst 18:167–193 CrossRefGoogle Scholar
  4. 4.
    Bosse T, Jonker CM, van der Meij L, Treur J (2007) A language and environment for analysis of dynamics by simulation. Int J Artif Intell Tools 16:435–464 CrossRefGoogle Scholar
  5. 5.
    Castelfranchi C (1998) Modelling social action for AI agents. Artif Intell 103:157–182 CrossRefMATHGoogle Scholar
  6. 6.
    Harbers M, Bosch KVD, Meyer JJ (2009) Modeling agent with a theory of mind. In: Baeza-Yates R, Lang J, Mitra S, Parsons S, Pasi G (eds) Proceedings of the 2009 IEEE/WIC/ACM international joint conference on web intelligence and agent technology. IEEE Computer Society Press, Los Alamitos, pp 217–224 CrossRefGoogle Scholar
  7. 7.
    Hoogendoorn M, Soumokil J (2010) Evaluation of virtual agents attributed with theory of mind in a real time action game. In: van der Hoek W, Kaminka GA, Lesperance Y, Luck M, Send S (eds) Proceedings of the ninth international conference on autonomous agents and multiagent systems, AAMAS 2010, pp 59–66 Google Scholar
  8. 8.
    Laird J (2001) It knows what you’re going to do: adding anticipation to a quakebot. In: Andre E, Sen S, Frasson C, Muller JP (eds) Proceedings of the 5th international joint conference on autonomous agents. ACM Press, New York, pp 385–392 CrossRefGoogle Scholar
  9. 9.
    Parker LE (1992) Adaptive action selection for cooperative agent teams. In: Meyer J-A, Roitblat H, Wilson S (eds) Proceedings of the 2nd international conference on simulation of adaptive behavior. MIT Press, Cambridge, pp 442–450 Google Scholar
  10. 10.
    Veloso M, Stone P, Bowling M (1999) Anticipation as a key for collaboration in a team of agents: a case study in robotic soccer. In: Proceedings of SPIE sensor fusion and decentralized control in robotic systems II, vol 3839 Google Scholar
  11. 11.
    Pynadath DV, Marsella SC (2005) PsychSim: modeling theory of mind with decision theoretic agents. In: Kaelbling LP, Saffiotti A (eds) Proceedings of IJCAI 2005. Professional Book Center, Denver, pp 1181–1186 Google Scholar
  12. 12.
    Bosse T, Memon ZA, Treur J (2007) A two-level BDI-agent model for theory of mind and its use in social manipulation. In: Olivier P, Kray C (eds) Proceedings of the artificial and ambient intelligence conference, AISB’07, mindful environments track. AISB Publ., London, pp 335–342 Google Scholar
  13. 13.
    Laird JE, Newell A, Rosenbloom PA (1987) SOAR: an architecture for general intelligence. Artif Intell 33:1–64 CrossRefGoogle Scholar
  14. 14.
    Sindlar MP, Dastani MM, Meyer JJ (2009) BDI-based development of virtual characters with a theory of mind. In: Ruttkay Z, Kipp M, Nijholt A, Vilhjalmsson HH (eds) Proceedings of IVA 2009. LNAI, vol 5773. Springer, Berlin, pp 34–41 Google Scholar
  15. 15.
    Ingrand FF, Georgeff MP, Rao AS (1992) An architecture for real-time reasoning and system control. IEEE Intell Syst Appl 7(6):34–44 Google Scholar
  16. 16.
    Al GCM, Hoogendoorn M (2011) Moving target search using theory of mind. In: Boissier O et al (eds) Proceedings of the 11th IEEE/WIC/ACM international conference on intelligent agent technology, IAT’11. IEEE Computer Society Press, Los Alamitos Google Scholar
  17. 17.
    Forgy CL (1982) Rete: a fast algorithm for the many pattern/many object pattern match problem. Artif Intell 19:17–37 CrossRefGoogle Scholar
  18. 18.
    Both F, Hoogendoorn M, van der Mee A, Treur J, de Vos M (2012) An intelligent agent model with awareness of workflow progress. J Appl Intell 36:498–510 CrossRefGoogle Scholar
  19. 19.
    Malatesta L, Raouzaiou A, Karpouzis K, Kollias S (2009) Towards modeling embodied conversational agent character profiles using appraisal theory predictions in expression synthesis. J Appl Intell 30:58–64 CrossRefGoogle Scholar
  20. 20.
    Lai R, Menq K, Lin W, Yu TJ (2010) Learning opponent’s beliefs via fuzzy constraint-directed approach to make effective agent negotiation. J Appl Intell 33:232–246 CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  1. 1.Department of Computer ScienceVU University AmsterdamAmsterdamThe Netherlands
  2. 2.Training, Simulation, and Operator PerformanceNational Aerospace LaboratoryAmsterdamThe Netherlands

Personalised recommendations