Multiagent Evaluation Mechanisms

Tal Alon, Magdalen Dobson, Ariel D. Procaccia, Inbal Talgam-Cohen, Jamie Tucker-Foltz

AAAI 2020 pp. 1774-1781

doi:10.1609/AAAI.V34I02.5543 /aaai/2020/alon2020aaai-multiagent/

Abstract

We consider settings where agents are evaluated based on observed features, and assume they seek to achieve feature values that bring about good evaluations. Our goal is to craft evaluation mechanisms that incentivize the agents to invest effort in desirable actions; a notable application is the design of course grading schemes. Previous work has studied this problem in the case of a single agent. By contrast, we investigate the general, multi-agent model, and provide a complete characterization of its computational complexity.

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Text

Alon et al. "Multiagent Evaluation Mechanisms." AAAI Conference on Artificial Intelligence, 2020. doi:10.1609/AAAI.V34I02.5543

Markdown

[Alon et al. "Multiagent Evaluation Mechanisms." AAAI Conference on Artificial Intelligence, 2020.](https://mlanthology.org/aaai/2020/alon2020aaai-multiagent/) doi:10.1609/AAAI.V34I02.5543

BibTeX

@inproceedings{alon2020aaai-multiagent,
  title     = {{Multiagent Evaluation Mechanisms}},
  author    = {Alon, Tal and Dobson, Magdalen and Procaccia, Ariel D. and Talgam-Cohen, Inbal and Tucker-Foltz, Jamie},
  booktitle = {AAAI Conference on Artificial Intelligence},
  year      = {2020},
  pages     = {1774-1781},
  doi       = {10.1609/AAAI.V34I02.5543},
  url       = {https://mlanthology.org/aaai/2020/alon2020aaai-multiagent/}
}