AI Agent Adversarial Training for Automated User Negotiation
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Solution Overview
Problem
Conventional online systems require users to individually search and decide on products, lacking the ability to negotiate with retailers, making the process time-intensive and limiting to binary purchase decisions, and existing machine-learned models struggle to effectively address diverse negotiation strategies.
Innovation Solution
Adversarial training of AI agents, specifically through adversarial training, where the system AI agents and user AI agents are trained using pre-defined constraints and objectives, enabling them to negotiate on behalf of users and the online system, facilitating efficient and dynamic interaction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If conventional online systems present products to users for individual selection, then users can make purchase decisions, but the process becomes time-intensive and users lack negotiation capability
Solution Approach 1:
The system employs AI agents that autonomously negotiate with retailers on behalf of users, eliminating the need for users to manually search and decide on each product. The AI agents independently engage in multi-turn negotiations, adjust strategies, and secure deals, allowing users to benefit from negotiated prices without investing time in the negotiation process themselves.
2Extent of automation
If machine-learned models are used to act on behalf of retailers, then automation is improved, but training difficulty increases due to diverse negotiation strategies
Solution Approach 1:
The system pre-trains AI agents using adversarial examples that incorporate diverse negotiation strategies and tactics before deployment. By anticipating and preparing for various negotiation scenarios in advance, the system reduces the complexity of real-time training and enables the AI agents to handle diverse negotiation situations effectively without requiring complex ongoing training mechanisms.
Data Source
AI summary
A system artificial intelligence (AI) agent is trained to act on behalf of an online system. The system AI agent comprises a large language model that has been pre-trained using a set of system constraints and a set of system objectives. The system AI agent is trained adversarially using training service requests from a plurality of different user AI agents of different types to determine resolutions to the training service requests. Once trained, the system AI agent may determine resolutions to service requests of users of the online system. In some embodiments, the system agent may determine the resolutions via messaging with user AI agents that represent the users. The online system may further train the system AI agent (and in some embodiments the user AI agents) based in part on the resolutions to the service requests.