AI Agent Preference Narrowing for Multi-User Outcome Selection
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Large language models are inefficient in communication tasks where direct compliance with an input is not optimal, failing to effectively consider preferences of multiple users.
Innovation Solution
A system of machine-learned communication agents iteratively narrows a set of candidate outcome values by exchanging and scoring preferences between users, ensuring a final outcome respects all parties' preferences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If large language models directly comply with input requests, then response speed is improved, but the ability to respect multiple users' preferences deteriorates
Solution Approach 1:
The system dynamically adjusts the interaction process between communication agents based on the complexity of preferences and issues. The iterative narrowing process adapts the number of rounds and depth of evaluation according to the specific task requirements, allowing the system to be both fast for simple tasks and thorough for complex multi-preference scenarios.
Solution Approach 2:
Communication agents serve as intermediaries between users and the outcome selection process. These agents represent user preferences and negotiate with other agents to find mutually acceptable outcomes, resolving the contradiction by introducing a mediating layer that balances speed and preference respect.
2Reliability
If iterative narrowing process is implemented to respect multiple preferences, then outcome quality is improved, but computational time and cost increase
Solution Approach 1:
The system performs partial iterative narrowing by limiting the number of rounds and using stopping criteria to prevent excessive computation. When a satisfactory outcome is found or the maximum rounds are reached, the process terminates, balancing outcome quality with computational efficiency.
Solution Approach 2:
The system changes parameters such as the number of narrowing rounds, the depth of preference evaluation, and the strictness of outcome criteria based on the complexity of the task. This allows the system to adjust computational resources dynamically, spending more time on complex multi-preference issues and less on simpler tasks.
3Device complexity
If traditional large language models are used for communication tasks, then implementation simplicity is maintained, but effectiveness in multi-preference scenarios deteriorates
Solution Approach 1:
The communication agent framework is designed to be universal, handling both simple direct-compliance tasks and complex multi-preference negotiation tasks through the same iterative narrowing mechanism. This multi-functionality allows the system to maintain implementation simplicity while improving effectiveness across diverse scenarios.
Data Source
AI summary
Provided is a system that enables machine-learned communication agents to provide automatic and iterative narrowing of possible outcome values for a particular item or parameter relative to multiple users in an effective manner that both respects the preferences of the users and is guaranteed to reach a definitive result. Communication agents can be or include machine-learned models that can act on behalf of a particular user to perform a variety of tasks associated with communication. In some examples, communication agents can receive requests from the user to perform a communication task, perform the task (e.g., usually communicating with another computing system), and provide the task result to the user. In other examples, the users can request that the agent interacts with another computer-based system to achieve a particular goal. For example, a communication agent can interface with a website to access information or access a service.