Automated negotiation for e-commerce decision making: A goal deliberated agent architecture for multi-strategy selection

• We model goal deliberated agent architecture for strategy selection.
• We present a multi-strategy selection algorithm, which is a new concession model.
• We reveal empirical knowledge for negotiating agent’s initial settings of strategy.
• We reveal empirical knowledge for negotiating agent’s reservation price and deadline.
• The research benefits the future development of human-computer negotiation system.

Automated negotiation plays an important role in dynamic trading in e-commerce. Its research largely focuses on negotiation protocol and strategy design. There is a paucity of further scientific investigation and a pressing need on the implementation of multi-strategy selection, which is crucially useful in human–computer negotiation to achieve better online negotiation outcomes. The lack of such studies has decelerated the process of applying automated negotiation to real world problems. To address the critical issue, this paper develops a multi-strategy negotiating agent system. More specifically, we formally define the agent's conceptual model, and design its abstract software architecture. Grounded on the integration of the time-dependent and behavior-dependent tactics, we also develop a multi-strategy selection theoretical model and algorithm. To demonstrate the effectiveness of this model algorithm, we implement a prototype and conduct numerous experiments. The experimental analysis not only confirms our model's effectiveness but also reveals some insights into future work about human–computer negotiation systems, which will be widely used in the future B2C e-commerce.