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Cooperating with machines

Author

Listed:
  • Jacob Crandall

    (Computer Science Department, Brigham Young University, 3361 TMCB, Provo, UT 84602, USA)

  • Mayada Oudah

    (Khalifa University for Science Technology [Abou Dabi])

  • Fatimah Ishowo-Oloko Tennom

    (UVA Digital Himalaya Project, University of Virginia, Charlottesville, VA 22904, USA)

  • Fatimah Ishowo-Oloko
  • Sherief Abdallah
  • Jean-François Bonnefon

    (CLLE-ERSS - Cognition, Langues, Langage, Ergonomie - EPHE - École Pratique des Hautes Études - PSL - Université Paris Sciences et Lettres - UT2J - Université Toulouse - Jean Jaurès - UT - Université de Toulouse - UBM - Université Bordeaux Montaigne - CNRS - Centre National de la Recherche Scientifique, TSM - Toulouse School of Management Research - UT Capitole - Université Toulouse Capitole - UT - Université de Toulouse - CNRS - Centre National de la Recherche Scientifique - TSM - Toulouse School of Management - UT Capitole - Université Toulouse Capitole - UT - Université de Toulouse)

  • Manuel Cebrian

    (Optimisation Research Group - NICTA - National ICT Australia [Sydney] - University of Melbourne)

  • Azim Shariff
  • Michael Goodrich
  • Iyad Rahwan

    (Massachusetts - MIT - Massachusetts Institute of Technology)

Abstract

Since Alan Turing envisioned artificial intelligence, technical progress has often been measured by the ability to defeat humans in zero-sum encounters (e.g., Chess, Poker, or Go). Less attention has been given to scenarios in which human–machine cooperation is beneficial but non-trivial, such as scenarios in which human and machine preferences are neither fully aligned nor fully in conflict. Cooperation does not require sheer computational power, but instead is facilitated by intuition, cultural norms, emotions, signals, and pre-evolved dispositions. Here, we develop an algorithm that combines a state-of-the-art reinforcement-learning algorithm with mechanisms for signaling. We show that this algorithm can cooperate with people and other algorithms at levels that rival human cooperation in a variety of two-player repeated stochastic games. These results indicate that general human–machine cooperation is achievable using a non-trivial, but ultimately simple, set of algorithmic mechanisms.
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Suggested Citation

  • Jacob Crandall & Mayada Oudah & Fatimah Ishowo-Oloko Tennom & Fatimah Ishowo-Oloko & Sherief Abdallah & Jean-François Bonnefon & Manuel Cebrian & Azim Shariff & Michael Goodrich & Iyad Rahwan, 2018. "Cooperating with machines," Post-Print hal-01897802, HAL.
    • Handle: RePEc:hal:journl:hal-01897802
    DOI: 10.1038/s41467-017-02597-8
    as

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    Other versions of this item:

    • Jacob W. Crandall & Mayada Oudah & Tennom & Fatimah Ishowo-Oloko & Sherief Abdallah & Jean-François Bonnefon & Manuel Cebrian & Azim Shariff & Michael A. Goodrich & Iyad Rahwan, 2018. "Cooperating with machines," Nature Communications, Nature, vol. 9(1), pages 1-12, December.
    • Abdallah, Sherief & Bonnefon, Jean-François & Cebrian, Manuel & Crandall, Jacob W. & Ishowo-Oloko, Fatimah & Oudah, Mayada & Rahwan, Iyad & Shariff, Azim & Tennom,, 2017. "Cooperating with Machines," IAST Working Papers 17-68, Institute for Advanced Study in Toulouse (IAST).
    • Abdallah, Sherief & Bonnefon, Jean-François & Cebrian, Manuel & Crandall, Jacob W. & Ishowo-Oloko, Fatimah & Oudah, Mayada & Rahwan, Iyad & Shariff, Azim & Tennom,, 2017. "Cooperating with Machines," TSE Working Papers 17-806, Toulouse School of Economics (TSE).

    References listed on IDEAS

    as
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