Negotiation Agent Opponent Modeling for Non-Stationary Bargaining
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Solution Overview
Problem
Existing automated negotiation agents struggle to accurately predict and adapt to the behavior of opponents in non-stationary environments, leading to suboptimal negotiation outcomes due to oversimplifying assumptions and lack of real-world adaptability, especially when dealing with human decision-makers whose behavior is influenced by cognitive, social, and cultural factors.
Innovation Solution
An adaptive negotiation agent that utilizes machine learning and cognitive computing to learn from opponent behavior, employing domain-independent online and offline modeling, and deep neural networks to predict opponent strategies, reducing state and action spaces while maintaining computational tractability and accuracy, and incorporating deep reinforcement learning to handle non-stationary environments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If opponent modeling is used to predict opponent behavior, then negotiation outcome quality is improved, but computational cost increases
Solution Approach 1:
The opponent model is segmented into multiple independent components: behavioral traits model, strategy model, and preference model. Each component processes specific aspects of opponent behavior independently, reducing the computational complexity of the overall model while maintaining prediction accuracy.
Solution Approach 2:
The system performs preliminary action by pre-processing opponent behavior data during the negotiation process to extract behavioral traits and patterns. This preliminary analysis enables faster prediction of opponent responses without requiring intensive computation during critical decision-making moments.
2Measurement precision
If extensive opponent behavior data is collected and analyzed, then prediction accuracy is improved, but negotiation time increases
Solution Approach 1:
The system applies partial action by selectively analyzing only the most relevant behavioral traits and negotiation patterns from the collected data, rather than processing all available information. This approach maintains prediction accuracy while significantly reducing the time required for analysis during negotiation.
Solution Approach 2:
The system implements feedback mechanisms that continuously refine predictions based on actual opponent responses. As the negotiation progresses, the model learns from observed behaviors and adjusts predictions, reducing the need for extensive preliminary data analysis and enabling faster decision-making in later stages.
Data Source
AI summary
Software based intelligent agents performing automated negotiations for the generation of offers leading to settlements that maximize the utility. A set of opponent's models, which best represents the opponent's profile, behavior, and cognitive orientation, is paired to a set of hypothetical agents. The hypothetical agent and the associated opponent model bargains against each other to generate a hypothetical sequence of proposed agent's offers and predicted opponent's counter offers. An ensemble unifies the proposed and predicted sequences based on the current and past performance measured according to the accuracy of the opponent's behavior prediction and agent's utility maximization. The opponent's adopted tactic is dynamically learned during the negotiation to update the opponent's model. The initial belief about the probability distribution of the opponent's preferences and internal states is set according to the historical negotiation's data. The belief is revised according to observed outcomes of the current negotiation.