Autonomous Agent Negotiation via Utility Thresholds
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Solution Overview
Problem
Existing automatic negotiation methods between multiple agents often require centralized control, which is not always feasible, and struggle to achieve optimal outcomes in distributed management scenarios.
Innovation Solution
A negotiation system and method that includes execution planning and determination mechanisms within each agent to calculate and evaluate the utility of offers and desired execution states using policy and utility functions, allowing agents to autonomously decide on accepting or proposing plans based on predetermined threshold values.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If centralized control is used to perform overall optimization in automatic negotiation, then the optimization result is improved, but the system complexity and control requirements increase
Solution Approach 1:
The patent divides the negotiation system into multiple independent agents, each capable of autonomous decision-making. Instead of one centralized controller optimizing all negotiations, each agent segments the optimization task and performs it locally using its own policy and utility functions, thereby reducing overall system complexity while maintaining optimization capability.
Solution Approach 2:
Each agent in the patent is equipped with its own execution planning means and determination means, allowing it to autonomously evaluate offers, calculate utilities, and make decisions without external control. This self-service mechanism enables distributed optimization where each agent serves its own optimization needs independently.
2Reliability
If centralized control is implemented for automatic negotiation, then overall optimization is achieved, but the ease of operation and system feasibility decrease
Solution Approach 1:
The patent segments the negotiation system into autonomous agents that operate independently. This segmentation eliminates the need for complex centralized control infrastructure, making the system more feasible for real-world deployment where centralized control may be impractical or impossible to implement.
Solution Approach 2:
Each agent autonomously performs evaluation and decision-making using its own embedded policy and utility functions. This self-service capability removes the operational burden of centralized control, significantly improving ease of operation while maintaining optimization performance through distributed intelligence.
3Ease of operation
If distributed management is used for automatic negotiation, then the ease of operation and system feasibility improve, but the ability to achieve optimal outcomes deteriorates
Solution Approach 1:
Each agent in the patent incorporates feedback mechanisms through its determination means, which evaluates offers against its utility function and compares calculated values with threshold values. This feedback loop enables each distributed agent to learn from negotiation outcomes and adjust its strategy, allowing the distributed system to achieve optimal or near-optimal results without centralized control.
Solution Approach 2:
The patent empowers each agent with self-service capabilities including execution planning, utility calculation, and threshold-based decision-making. This enables distributed agents to independently achieve optimal outcomes for their own objectives while contributing to overall system optimization, resolving the contradiction between distributed operation and optimization quality.
Data Source
AI summary
An execution planning means 81 calculates, with an offer from another agent as a constraint condition, a first value which is a value of an optimal execution plan up to achievement of an objective planned based on a state transition by an action taken according to a policy of an own agent. A determination means 82 determines, with the first value as an argument, whether or not a value calculated by a utility function, which is a function defining a utility of an execution plan of the own agent when the offer from the other agent is accepted, is greater than a predetermined threshold value. The determination means 82 determines to accept the offer from the other agent when the value is greater than the threshold value, and determines to reject the offer from the other agent when the value is equal to or less than the threshold value.