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Complex Systems of Mindful Entities: On Intention Recognition and Commitment

  • Luís Moniz PereiraEmail author
  • The Anh Han
  • Francisco C. Santos
Conference paper
First Online:
Part of the Studies in Applied Philosophy, Epistemology and Rational Ethics book series (SAPERE, volume 8)

Abstract

The mechanisms of emergence and evolution of cooperation in populations of abstract individuals with diverse behavioural strategies in co-presence have been undergoing mathematical study via Evolutionary Game Theory, inspired in part on Evolutionary Psychology. Their systematic study resorts as well to implementation and simulation techniques, thus enabling the study of aforesaid mechanisms under a variety of conditions, parameters, and alternative virtual games. The theoretical and experimental results have continually been surprising, rewarding, and promising. Recently, in our own work we have initiated the introduction, in such groups of individuals, of cognitive abilities inspired on techniques and theories of Artificial Intelligence, namely those pertaining to both Intention Recognition and to Commitment (separately and jointly), encompassing errors in decision-making and communication noise. As a result, both the emergence and stability of cooperation become reinforced comparatively to the absence of such cognitive abilities. This holds separately for Intention Recognition and for Commitment, and even more when they are engaged jointly. The present paper aims to sensitize the reader to these Evolutionary Game Theory based studies and issues, which are accruing in importance for the modelling of minds with machines, with impact on our understanding of the evolution of mutual tolerance and cooperation. In doing so, it also provides a coherent bird’s-eye view of our own varied recent work, whose more technical details and results are spread throughout a number of well recognized publishing venues, and to which we refer the reader for a fuller support of our claims where felt necessary.

Keywords

Bayesian Network Punishment Cost Social Dilemma Payoff Matrix Evolutionary Game Theory 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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Notes

Acknowledgments

This article is in great part a summary and cursory overview of the authors’ detailed joint work mentioned in the publications. TAH acknowledges support from FCT-Portugal (grant SFRH/BD/62373/2009) and the FWO Vlaanderen (Postdoctoral fellowship). FCS acknowledges support from FCT-Portugal (PTDC/FIS/101248/2008, PTDC/MAT/122897/2010 and INESC-ID’s PEst-OE/EEI/LA0021/2011). We thank all three reviewers for their very helpful indications.

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© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  • Luís Moniz Pereira
    • 1
    Email author
  • The Anh Han
    • 1
    • 2
  • Francisco C. Santos
    • 3
  1. 1.Centro de Inteligência Artificial (CENTRIA), Departamento de Informática, Faculdade de Ciências e TecnologiaUniversidade Nova de LisboaCaparicaPortugal
  2. 2.AI-labVrije Universiteit BrusselBrusselsBelgium
  3. 3.INESC-ID, Instituto Superior Técnico and ATP-group, Instituto para a Investigação Interdisciplinar Universidade Técnica de LisboaPorto Salvo Portugal

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