A Data-Driven Method for Recognizing Automated Negotiation Strategies
This work addresses a largely unexplored direction in automated negotiations, which is important for improving negotiation outcomes with opponent agents, though it appears incremental as it builds on existing methods with a novel application.
The paper tackles the problem of recognizing an opponent's negotiation strategy in automated negotiations, proposing a data-driven approach that includes data generation, feature engineering, and a hybrid deep learning method, and demonstrates its effectiveness through experiments across four problem scenarios.
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.