This paper introduces an acceptance strategy based on reinforcement learning for automated bilateral negotiation, where negotiating agents bargain on multiple issues in a variety of negotiation scenarios. Several acceptance strategies based on predefined rules have been introduced in the automated negotiation literature. Those rules mostly rely on some heuristics, which take time and/or utility into account. For some negotiation settings, an acceptance strategy solely based on a negotiation deadline might perform well; however, it might fail in another setting. Instead of following predefined acceptance rules, this paper presents an acceptance strategy that aims to learn whether to accept its opponent's offer or make a counter offer by reinforcement signals received after performing an action. In an experimental setup, it is shown that the performance of the proposed approach improves over time.

In a supply chain environment, supply chain entities need to make joint decisions on the transaction of goods under the issues quantity, delivery time and unit price in order to procure/sell goods at right quantities and time while minimizing the transaction costs. This paper presents our negotiating agent designed for Supply Chain Management League (SCML) in the International Automated Negotiation Agents Competition (ANAC). Basically, the proposed approach relies on determining reservation value by taking the changes in the inventory stock into account. We have tested the performance of our bidding strategy in the competition simulation environment and compared it with the performance of the winner strategies in ANAC SCML 2019. Our experimental results showed that the proposed strategy outperformed the winner strategies in overall.