Giving advice to people in path selection problems

Amos Azaria, Zinovi Rabinovich, Sarit Kraus, Claudia V. Goldman, Omer Tsimhoni

Research output: Contribution to conferencePaperpeer-review

19 Scopus citations

Abstract

We present a novel computational method for advice-generation in path selection problems which are difficult for people to solve. The advisor agent's interests may conflict with the interests of the people who receive the advice. Such optimization settings arise in many human-computer applications in which agents and people are self-interested but also share certain goals, such as automatic route-selection systems that also reason about environmental costs. This paper presents an agent that clusters people into one of several types, based on how their path selection behavior adheres to the paths presented to them by the agent who does not necessarily suggest their most preferred paths. It predicts the likelihood that people will deviate from these suggested paths and uses a decision theoretic approach to suggest paths to people which will maximize the agent's expected benefit, given the people's deviations. This technique was evaluated empirically in an extensive study involving hundreds of human subjects solving the path selection problem in mazes. Results showed that the agent was able to outperform alternative methods that solely considered the benefit to the agent or the person, or did not provide any advice.

Original languageEnglish
Pages512-519
Number of pages8
StatePublished - 2012
Externally publishedYes
Event11th International Conference on Autonomous Agents and Multiagent Systems 2012: Innovative Applications Track, AAMAS 2012 - Valencia, Spain
Duration: 4 Jun 20128 Jun 2012

Conference

Conference11th International Conference on Autonomous Agents and Multiagent Systems 2012: Innovative Applications Track, AAMAS 2012
Country/TerritorySpain
CityValencia
Period4/06/128/06/12

Keywords

  • Computer applications; Multi agent systems
  • Decision theoretic approach; Environmental costs; Human subjects; Path selection; Path selection problem
  • Autonomous agents

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