Stackelberg Punishment and Bully-Proofing Autonomous Vehicles
| Autor: | Michael Gillett, Joshua D. Fishman, Michael L. Littman, Zoe Papakipos, Matt Cooper, Aansh Shah, Jerome Ramos, Jun Ki Lee, Jacob Beck, Aaron Zhang |
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| Rok vydání: | 2019 |
| Předmět: |
050101 languages & linguistics
Social robot Punishment (psychology) Computer science Efficient algorithm 05 social sciences 02 engineering and technology Best response 0202 electrical engineering electronic engineering information engineering Stackelberg competition Repeated game 020201 artificial intelligence & image processing 0501 psychology and cognitive sciences Algorithmic game theory Mathematical economics Game theory |
| Zdroj: | Social Robotics ISBN: 9783030358877 ICSR |
| DOI: | 10.1007/978-3-030-35888-4_34 |
| Popis: | Mutually beneficial behavior in repeated games can be enforced via the threat of punishment, as enshrined in game theory’s well-known “folk theorem.” There is a cost, however, to a player for generating these disincentives. In this work, we seek to minimize this cost by computing a “Stackelberg punishment,” in which the player selects a behavior that sufficiently punishes the other player while maximizing its own score under the assumption that the other player will adopt a best response. This idea generalizes the concept of a Stackelberg equilibrium. Known efficient algorithms for computing a Stackelberg equilibrium can be adapted to efficiently produce a Stackelberg punishment. We demonstrate an application of this idea in an experiment involving a virtual autonomous vehicle and human participants. We find that a self-driving car with a Stackelberg punishment policy discourages human drivers from bullying in a driving scenario requiring social negotiation. |
| Databáze: | OpenAIRE |
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