Evaluating persuasion strategies and deep reinforcement learning methods for negotiation dialogue agents

Keizer, Simon and Guhe, Markus and Cuayahuitl, Heriberto and Efstathiou, Ioannis and Engelbrecht, Klaus-Peter and Dobre, Mihai and Lascarides, Alex and Lemon, Oliver (2017) Evaluating persuasion strategies and deep reinforcement learning methods for negotiation dialogue agents. In: 15th Conference of the European chapter of the Association for Computational Linguistics, 3-7 April 2017, Valencia, Spain.

Documents
eacl-evaluation-negotiation.pdf
[img]
[Download]
[img]
Preview
PDF
eacl-evaluation-negotiation.pdf - Whole Document

696kB
Item Type:Conference or Workshop contribution (Paper)
Item Status:Live Archive

Abstract

In this paper we present a comparative evaluation of various negotiation strategies within an online version of the game “Settlers of Catan”. The comparison is based on human subjects playing games against artificial game-playing
agents (‘bots’) which implement different negotiation dialogue strategies, using a chat dialogue interface to negotiate trades. Our results suggest that a negotiation strategy that uses persuasion, as well as a strategy that is trained from data using Deep Reinforcement Learning, both lead to an improved win rate against humans, compared to previous rule-based and supervised learning baseline dialogue negotiators.

Keywords:Deep Reinforcement Learning, Strategic Conversation
Subjects:G Mathematical and Computer Sciences > G700 Artificial Intelligence
G Mathematical and Computer Sciences > G710 Speech and Natural Language Processing
Divisions:College of Science > School of Computer Science
ID Code:26621
Deposited On:06 Mar 2017 16:00

Repository Staff Only: item control page