Task routing for prediction tasks

Haoqi Zhang, Eric Horvitz, Yiling Chen, David C. Parkes

Research output: Chapter in Book/Report/Conference proceedingConference contribution

16 Scopus citations

Abstract

We describe methods for routing a prediction task on a network where each participant can contribute information and route the task onwards. Routing scoring rules bring truthful contribution of information about the task and optimal routing of the task into a Perfect Bayesian Equilibrium under common knowledge about the competencies of agents. Relaxing the common knowledge assumption, we address the challenge of routing in situations where each agent's knowledge about other agents is limited to a local neighborhood. A family of local routing rules isolate in equilibrium routing decisions that depend only on this local knowledge, and are the only routing scoring rules with this property. Simulation results show that local routing rules can promote effective task routing.

Original languageEnglish (US)
Title of host publication11th International Conference on Autonomous Agents and Multiagent Systems 2012, AAMAS 2012
Subtitle of host publicationInnovative Applications Track
PublisherInternational Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS)
Pages824-831
Number of pages8
Volume2
StatePublished - Jan 1 2012
Event11th International Conference on Autonomous Agents and Multiagent Systems 2012: Innovative Applications Track, AAMAS 2012 - Valencia, Spain
Duration: Jun 4 2012Jun 8 2012

Other

Other11th International Conference on Autonomous Agents and Multiagent Systems 2012: Innovative Applications Track, AAMAS 2012
CountrySpain
CityValencia
Period6/4/126/8/12

Keywords

  • Scoring rules
  • Social networks
  • Task routing

ASJC Scopus subject areas

  • Artificial Intelligence

Cite this

Zhang, H., Horvitz, E., Chen, Y., & Parkes, D. C. (2012). Task routing for prediction tasks. In 11th International Conference on Autonomous Agents and Multiagent Systems 2012, AAMAS 2012: Innovative Applications Track (Vol. 2, pp. 824-831). International Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS).