A Data-Driven Method for Recognizing Automated Negotiation Strategies

TL;DR

This paper introduces a novel data-driven approach using deep learning to recognize negotiation strategies of opponents across domains, enhancing understanding beyond preference modeling.

Contribution

It presents a new method combining data generation, feature engineering, and RNN-based recognition to identify complex negotiation tactics in various scenarios.

Findings

Effective recognition of negotiation strategies demonstrated across four scenarios.

Outperforms simple heuristics in identifying opponent tactics.

Provides a domain-independent framework for strategy recognition.

Abstract

Understanding an opponent agent helps in negotiating with it. Existing works on understanding opponents focus on preference modeling (or estimating the opponent's utility function). An important but largely unexplored direction is recognizing an opponent's negotiation strategy, which captures the opponent's tactics, e.g., to be tough at the beginning but to concede toward the deadline. Recognizing complex, state-of-the-art, negotiation strategies is extremely challenging, and simple heuristics may not be adequate for this purpose. We propose a novel data-driven approach for recognizing an opponent's s negotiation strategy. Our approach includes a data generation method for an agent to generate domain-independent sequences by negotiating with a variety of opponents across domains, a feature engineering method for representing negotiation data as time series with time-step features and overall features, and a hybrid (recurrent neural network-based) deep learning method for recognizing an opponent's strategy from the time series of bids. We perform extensive experiments, spanning four problem scenarios, to demonstrate the effectiveness of our approach.