Curiosity-Aware Bargaining

TL;DR

This paper introduces curiosity-aware bargaining protocols that limit information leakage in automated negotiations by extending standard protocols and incorporating a rationality model that considers exchanged information.

Contribution

It proposes three novel bargaining protocol extensions that incorporate curiosity-driven agents and analyze their theoretical properties and experimental performance.

Findings

Protocols effectively limit information leak.

Curiosity-aware agents can negotiate successfully.

Enhanced rationality models improve negotiation robustness.

Abstract

Opponent modeling consists in modeling the strategy or preferences of an agent thanks to the data it provides. In the context of automated negotiation and with machine learning, it can result in an advantage so overwhelming that it may restrain some casual agents to be part of the bargaining process. We qualify as "curious" an agent driven by the desire of negotiating in order to collect information and improve its opponent model. However, neither curiosity-based rational-ity nor curiosity-robust protocol have been studied in automatic negotiation. In this paper, we rely on mechanism design to propose three extensions of the standard bargaining protocol that limit information leak. Those extensions are supported by an enhanced rationality model, that considers the exchanged information. Also, they are theoretically analyzed and experimentally evaluated.