  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.
