Be Selfish, But Wisely: Investigating the Impact of Agent Personality in
  Mixed-Motive Human-Agent Interactions
Kushal Chawla Ian Wu Yu Rong Gale M. Lucas Jonathan Gratch
A natural way to design a negotiation dialogue system is via self-play RL:
train an agent that learns to maximize its performance by interacting with a
simulated user that has been designed to imitate human-human dialogue data.
Although this procedure has been adopted in prior work, we find that it results
in a fundamentally flawed system that fails to learn the value of compromise in
a negotiation, which can often lead to no agreements (i.e., the partner walking
away without a deal), ultimately hurting the model's overall performance. We
investigate this observation in the context of the DealOrNoDeal task, a
multi-issue negotiation over books, hats, and balls. Grounded in negotiation
theory from Economics, we modify the training procedure in two novel ways to
design agents with diverse personalities and analyze their performance with
human partners. We find that although both techniques show promise, a selfish
agent, which maximizes its own performance while also avoiding walkaways,
performs superior to other variants by implicitly learning to generate value
for both itself and the negotiation partner. We discuss the implications of our
findings for what it means to be a successful negotiation dialogue system and
how these systems should be designed in the future.