ANEGMA : an automated negotiation model for e-markets. / Bagga, Pallavi; Paoletti, Nicola; Alrayes, Bedour; Stathis, Kostas.

In: Autonomous Agents and Multi-Agent Systems, Vol. 35, 27, 07.06.2021.

Research output: Contribution to journalArticlepeer-review



We present a novel negotiation model that allows an agent to learn how to negotiate during concurrent bilateral negotiations in unknown and dynamic e-markets. The agent uses an actor-critic architecture with model-free reinforcement learning to learn a strategy expressed as a deep neural network. We pre-train the strategy by supervision from synthetic market data, thereby decreasing the exploration time required for learning during negotiation. As a result, we can build automated agents for concurrent negotiations that can adapt to different e-market settings without the need to be pre-programmed. Our experimental evaluation shows that our deep reinforcement learning based agents outperform two existing well-known negotiation strategies in one-to-many concurrent bilateral negotiations for a range of e-market settings.
Original languageEnglish
Article number27
JournalAutonomous Agents and Multi-Agent Systems
Publication statusPublished - 7 Jun 2021
This open access research output is licenced under a Creative Commons Attribution-NonCommercial-NoDerivs 3.0 Unported License.

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