Learning to manage risks in non-cooperative dialogues

Ioannis Efstathiou, Oliver Lemon

Research output: Chapter in Book/Report/Conference proceedingConference contribution


We investigate statistical dialogue agents which learn to perform non-cooperative dialogue moves in order to complete their own objectives in a stochastic trading game. We show that, when given the ability to perform both cooperative and non-cooperative dialogue moves, such an agent can learn to bluff and to lie so as to win games more often - against a variety of adversaries, and under various conditions such as risking penalties for being caught in deception. Here we present new results showing how learned non-cooperative dialogue strategies change depending on a) how severe the penalty is for being caught being non-cooperative, and b) how risky the non-cooperative behaviour is (i.e. the probability of being caught). For example, we show that a non-cooperative dialogue agent can learn to win an additional 4.5% of games against a strong rule-based adversary, even when there is an additional 10% chance of being caught (exposed) every time it attempts a non-cooperative (manipulative) move, when the penalty for being caught is that the adversary will no longer trade.
Original languageEnglish
Title of host publicationSemDial 2014 Proceedings
EditorsVerena Rieser, Philippe Muller
Number of pages3
Publication statusPublished - 1 Sept 2014
Event18th Workshop on the Semantics and Pragmatics of Dialogue - Heriot Watt University, Edinburgh, United Kingdom
Duration: 1 Sept 20143 Sept 2014

Publication series

NameProceedings (SemDial)
ISSN (Print)2308-2275


Workshop18th Workshop on the Semantics and Pragmatics of Dialogue
Abbreviated titleSemDial 2014
Country/TerritoryUnited Kingdom


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