Neural Network Architecture Selection for Dynamic Negotiation
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Solution Overview
Problem
Current AI negotiation systems lack precision in predicting offers and counteroffers due to uncertainties in user preferences and priorities, leading to suboptimal negotiation outcomes, especially when negotiating with parties whose priorities change over time or have different strategies.
Innovation Solution
A neural network-based system that adapts by selecting or switching between trained architectures during negotiations based on past negotiation traces, using protocols to communicate offers and counteroffers, and evaluating performance to ensure accurate predictions and value maximization for the user.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If an AI negotiator is fully trained with user preferences and priorities prior to beginning negotiation, then prediction accuracy of opponent actions is improved, but adaptability to changing priorities and strategies deteriorates
Solution Approach 1:
The system dynamically switches between multiple pre-trained neural network architectures during negotiation based on detected changes in opponent behavior patterns. When the opponent's strategy or priorities change, the system transitions from a static fully-trained model to a dynamic architecture selection mechanism that adapts to new conditions by selecting appropriate pre-trained models or initiating retraining.
Solution Approach 2:
The system changes the parameter of neural network architecture selection based on negotiation context. Instead of using a single fixed architecture, it maintains multiple pre-trained architectures with different characteristics and selects the most appropriate one based on observed opponent behavior, effectively changing the system's structural parameters to match the negotiation situation.
2Adaptability or versatility
If an AI negotiator includes some minor uncertainty regarding user preferences and priorities at the beginning of negotiation, then adaptability to changing strategies is improved, but prediction accuracy of opponent actions deteriorates
Solution Approach 1:
The system performs preliminary training of multiple neural network architectures before negotiation begins, each trained on different aspects of user preferences and opponent behavior patterns. This preliminary preparation allows the system to have pre-computed models ready for rapid deployment, reducing the need for real-time learning while maintaining adaptability through architecture selection.
Solution Approach 2:
The system incorporates feedback mechanisms that monitor negotiation outcomes and opponent responses in real-time. Based on this feedback, it evaluates the performance of different pre-trained architectures and adjusts its selection accordingly, allowing it to learn from actual negotiation dynamics while maintaining the benefits of pre-trained models.
3Loss of information
If the AI negotiator queries the user to reduce uncertainty of preferences, then information accuracy is improved, but negotiation time increases
Solution Approach 1:
Instead of querying the user about all possible preferences and priorities, the system performs partial information gathering by querying only about the most critical parameters that have the greatest impact on negotiation strategy. This selective approach reduces the time cost of user queries while still obtaining sufficient information to make accurate predictions and decisions.
Data Source
AI summary
A system for selecting and using a neural architecture includes a non-transitory computer readable medium configured to store instructions thereon; and a processor connected to the non-transitory computer readable medium. The processor is configured to execute the instructions for receiving negotiation traces, wherein the negotiation traces include offers from previous negotiations with a target negotiating party. The processor is configured to execute the instructions for training the neural architecture using only the received negotiation traces. The processor is configured to execute the instructions for optimizing an offer to the target negotiating party during a negotiation using the trained neural architecture.