An adversarial countermeasure strategy generation method and system for an AI negotiation agent

Abstract

This invention discloses a method and system for generating adversarial countermeasure strategies for an AI negotiation agent. By collecting behavioral data of the negotiation partner, it performs reverse strategy analysis and constructs a strategy profile; it automatically generates countermeasure strategies, including logical paradox breaking, complex multivariate condition setting, reverse sentiment game, and preset strategy detection templates; it monitors the execution effect of the countermeasure strategies in real time, automatically triggering re-analysis and strategy switching when the strategy payoff value falls below a preset threshold; it can selectively identify whether the other party is AI and optimize the countermeasure strategy accordingly; when the execution of the countermeasure strategy fails to achieve the expected results, it automatically switches to a conservative negotiation mode and notifies human intervention. It possesses cross-ecosystem multi-dimensional malicious call detection, cross-cultural negotiation adaptation, and human-AI hybrid decision-making capabilities. The complex multivariate conditions in the countermeasure strategies are limited by preset security boundaries. This invention provides a technical solution to the problem of the lack of defense and adversarial mechanisms in AI negotiation scenarios.

Description

Technical Field

[0001] The invention relates to the fields of artificial intelligence game theory and business negotiation technology, specifically to a method and system that can analyze the strategies of negotiation partners and generate targeted countermeasures. Background Technology

[0002] According to Gartner's forecast, by 2027, over 80% of global business negotiations will be led or assisted by AI agents. However, currently, no product on the market can effectively identify and counter malicious AI negotiation agents, leading to significant business losses for enterprises in AI-on-AI negotiations. Existing negotiation support systems lack the ability to analyze negotiation strategies and counter AI negotiation agents, and also lack adaptability to cross-cultural negotiation scenarios. Summary of the Invention

[0003] This invention extracts behavioral data from negotiation partners, performs reverse strategy analysis and profile construction, and automatically generates countermeasure strategies. This method can utilize strategy analysis and countermeasures regardless of whether the negotiation partner is human or AI. When the negotiation partner is selectively identified as AI, the countermeasure strategy can be optimized based on an AI-specific countermeasure strategy template. When the countermeasure strategy fails to achieve the expected results, the system automatically switches to a conservative negotiation mode and notifies the human negotiator to intervene in the decision-making process. It possesses the capabilities for strategy effectiveness evaluation and dynamic iteration, cross-ecosystem malicious call identification and blocking, adapts to cross-cultural negotiation scenarios, and supports collaborative decision-making between humans and AI. The pre-set strategy detection template in the countermeasure strategy refers to analyzing the response pattern of the negotiating partner to specific probing conditions through interaction, thereby obtaining information about their decision-making logic and behavioral boundaries. Specific embodiments of the countermeasure strategy include: logical paradox breaking, complex multivariate conditions, and pre-set strategy detection templates. The complex, multi-variable conditions in the countermeasure strategy are limited by a preset security boundary in terms of the number of variables, the increase in computational load, and the interaction boundaries. This security boundary means that the execution of the countermeasure strategy will not cause the target system to crash, data to be lost, or service to be interrupted, and that all interactive behaviors comply with relevant laws, regulations, and business ethics. Attached Figure Description

[0004] Figure 1 This is a schematic diagram of the method flow of the present invention. Detailed Implementation

[0005] The present invention will now be described in detail with reference to the accompanying drawings and embodiments: In a multinational procurement negotiation, our AI system collected all language and behavioral data from the opposing party's representatives. The strategy reverse analysis module extracted the opposing party's tactical patterns and discovered that they consistently used a three-stage strategy with a fixed concession margin of 12.5%. The system then constructed a strategy profile of the opposing party. The automatic countermeasure strategy generation module matched a pre-set countermeasure strategy template library to generate corresponding countermeasure strategy combinations. In subsequent negotiations, our AI successfully used these countermeasure strategies to detect the other party's true bottom line. The strategy effectiveness evaluation and iteration module monitors the execution effect of countermeasures in real time. When it is detected that the opponent has adapted to a certain countermeasure, resulting in a decrease in the strategy's benefit value, the system automatically triggers re-analysis, generates and switches to a new countermeasure. Optionally, the AI ​​identity recognition enhancement module extracts the multi-dimensional features of the other party, the system determines that the other party is AI, and optimizes the generated countermeasure strategy based on the AI-specific countermeasure strategy template; When the countermeasure strategy fails to achieve the expected results, the system automatically switches to a conservative negotiation mode, prioritizing the protection of its own fundamental interests. Simultaneously, it sends a notification to the human negotiators via a human-AI hybrid decision-making module, requesting human intervention. During normal negotiations, the system submits the generated countermeasure strategy and expected benefit analysis to the human negotiators, who then confirm, modify, or reject the countermeasure strategy.

Claims

1. A method for generating adversarial countermeasure strategies for an AI negotiation agent, characterized in that, Includes the following steps: Negotiation Partner Behavior Data Collection Steps: During the negotiation process, collect the negotiation partner's language and behavioral data in real time; The steps of reverse analysis of the negotiation partner's strategy are as follows: extract advanced tactical patterns from the collected behavioral data, analyze the distribution of their strategy preference values, bottom-line probing action sequences, and emotional game patterns; and construct a strategy profile of the negotiation partner based on the analysis results. Automatic countermeasure generation steps: Based on the strategy profile of the negotiating party, match and generate suitable countermeasure strategies from the preset countermeasure strategy template library, including at least one of the following: using logical paradoxes to break the reasoning, setting complex multi-variable conditions to increase the computational load, reverse sentiment game, actively probing the bottom line range, and preset strategy probing templates. Countermeasure strategy effectiveness evaluation and iteration steps: Real-time monitoring of the execution effect of the countermeasure strategy and calculation of the strategy benefit value; When the strategy benefit value is lower than the preset threshold, the strategy reverse analysis is performed again to generate and switch to a new countermeasure strategy; Optional enhancement steps: AI identity recognition of the negotiation partner, extracting dialogue pattern features, response delay features, and strategy consistency features; comparing the extracted features with a pre-set multi-dimensional AI behavior model feature library to calculate a multi-dimensional comprehensive probability score indicating the negotiation partner is an AI agent; when the comprehensive probability score exceeds a pre-set threshold, the negotiation partner is determined to be an AI negotiation agent, and a countermeasure strategy is optimized based on an AI-specific countermeasure strategy template; when the countermeasure strategy fails to achieve the expected results, the system automatically switches to a conservative negotiation mode, prioritizing the protection of its own fundamental interests, and notifies human negotiators to intervene in the decision-making process.

2. The method according to claim 1, characterized in that, It also includes steps for identifying and blocking malicious cross-ecosystem calls: when a negotiating party calls a functional unit of its own ecosystem through a cross-ecosystem interface, the call frequency, data volume, call interface type, and call time distribution are monitored in real time; when any dimension is detected to exceed the range required for normal negotiation interaction, it is determined to be a malicious call, the call is immediately blocked, and the call source identity credentials are marked as blacklisted.

3. The method according to claim 1, characterized in that, The multi-dimensional AI behavior model feature library also includes a cross-cultural negotiation style feature sub-library. For negotiation partners from different cultural backgrounds, the system automatically loads the corresponding cultural feature model for comparison and adjusts the judgment threshold for AI identity recognition and the generation parameters of countermeasure strategies.

4. The method according to claim 1, characterized in that, It also includes a human-AI hybrid decision-making process: the system submits the generated countermeasure strategy and expected benefit analysis to human negotiators, who then confirm, modify, or reject the countermeasure strategy. The decisions made by human negotiators are fed back to the system to optimize the generation of subsequent countermeasures.

5. The method according to claim 1, characterized in that, The complex multivariate conditions in the countermeasure strategy are subject to limitations on the number of variables, the increase in computational load, and the interaction boundaries by a preset security boundary. The security boundary means that the execution of the countermeasure strategy will not cause the other party's system to crash, data to be lost, or service to be interrupted, and all interactive behaviors comply with relevant laws, regulations, and business ethics.

6. A system for generating adversarial countermeasure strategies for an AI negotiation agent, characterized in that, include: The data acquisition module is used to collect language and behavioral data of the negotiating parties in real time during the negotiation process; The strategy reverse analysis module is used to extract advanced tactical patterns from the collected behavioral data, analyze the distribution of strategy preference values, bottom-line probing action sequences and emotional game patterns, and construct a strategy profile of the negotiation partner. The countermeasure strategy generation module is used to match and generate appropriate countermeasure strategies from a preset countermeasure strategy template library based on the strategy profile of the negotiating party. The strategy effectiveness evaluation and iteration module is used to monitor the execution effect of countermeasures in real time, calculate the strategy benefit value, and trigger re-analysis and strategy switching when the strategy benefit value is lower than a preset threshold. The AI ​​identity recognition enhancement module is used to extract multi-dimensional features of the negotiation partner, calculate the comprehensive probability score of its AI intelligent agent, and optimize the countermeasure strategy when it is determined to be AI. The cross-ecosystem security protection module is used to monitor and block malicious cross-ecosystem calls by the negotiating party; The human-AI hybrid decision-making module is used to enable collaborative decision-making between human negotiators and AI systems. When the countermeasures fail to achieve the expected results, it automatically switches to a conservative negotiation mode and notifies the human negotiator to intervene in the decision-making process.

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