TY - JOUR
T1 - ANEGMA
T2 - an automated negotiation model for e-markets
AU - Bagga, Pallavi
AU - Paoletti, Nicola
AU - Alrayes, Bedour
AU - Stathis, Kostas
PY - 2021/6/7
Y1 - 2021/6/7
N2 - 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.
AB - 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.
U2 - 10.1007/s10458-021-09513-x
DO - 10.1007/s10458-021-09513-x
M3 - Article
SN - 1387-2532
VL - 35
JO - Autonomous Agents and Multi-Agent Systems
JF - Autonomous Agents and Multi-Agent Systems
M1 - 27
ER -