Alle Publikationen

  • Erweiterte Suche öffnen

Treffer: 2
  • <<
  • 1

  • 2011

  • Andrighetto, Giulia; Villatoro, Daniel (2011): Beyond the Carrot and Stick Approach to Enforcement: An Agent-Based Model. In: European Conference on Cognitive Science

    Abstract: As specified by Axelrod in his seminal work An Evolutionary Approach to Norms (Axelrod, 1986), punishment is a key mechanism to achieve the necessary social control and to enforce social norms in a self-regulated society. In this paper, we distinguish between two enforcing mechanisms, punishment and sanction, focusing on the specific ways in which they favour the emergence and maintenance of cooperation. In particular, by punishment we refer to a practice that works only by imposing a cost, while by sanction we indicate a practice that also signals the existence of a norm and that its violation is not condoned. To achieve this, we have developed a normative agent able both to punish and sanction offenders and to be affected by these enforcing mechanisms itself.The results obtained through agent-based simulation show that sanction is more effective and makes the population more resilient to sudden changes than mere punishment.

    Keywords: Agent-Based Modelling and Simulation, Axelrod, normative agent, normative Agenten, Normen, norms, Punishment, Realtechnik, sanctions, Sanktion, Selbstorganisation, self-organization, Semira, simulation, Spieltheorie, strafen

  • 2006

  • Reichenbach, Juliane; Bartneck, Christoph; Carpenter, Julie (2006) : Well done, Robot! The importance of praise and presence in human-robot collaboration: The 15th IEEE International Symposium on Robot and Human Interactive Communication, 2006: RO-MAN 2006 ; 6-8 Sept. 2006, University of Hertfordshire, Hatfield, United Kingdom ; proceedings: RO-MAN 2006: The 15th IEEE International Symposium on Robot and Human Interactive Communication: Hatfield: 9/6/2006 - 9/8/2006. IEEE International Symposium on Robot and Human Interactive Communication; IEEE Ro-Man: Piscataway, NJ: IEEE, S. 86-90

    Abstract: This study reports on an experiment in which participants had to collaborate with either another human or a robot (partner). The robot would either be present in the room or only be represented on the participants' computer screen (presence). Furthermore, the participants' partner would either make 20 % errors or 40 % errors (error rate). We automatically measured the praising and punishing behavior of the participants towards their partners and also asked the participant to estimate their own behavior. The participants unconsciously praised Aibo more than the human partner, but punished it just as much. Robots that adapt to the users' behavior should therefore pay extra attention to the users' praises, compared to their punishments

    Keywords: AIBO, Lob, Mensch und Maschine, Menschen loben Roboter mehr als andere Menschen, Mensch-Technik-Relationen (MTR), praise, Punishment, Respekt, Sanktion, Verhalten
  • <<
  • 1