An AI-supported negotiation and discussion system with adaptive learning and strategy optimization

Abstract

An AI-powered negotiation and discussion system (100) with adaptive learning and strategy optimization, comprising: an input processing module configured to receive and preprocess user input, including natural language text or speech; a natural language understanding module configured to extract intent, sentiment, and contextual entities from the preprocessed input; an argument search module configured to identify and structure arguments, claims, evidence, and counterarguments; an adversary modeling module configured to analyze incoming data and create dynamic behavioral profiles of negotiating partners; a strategy optimization engine configured to select and refine negotiation strategies based on adaptive learning algorithms; a dialogue management module configured to generate and manage context-dependent responses; a feedback and learning module configured to update the system based on the results of negotiation or discussion sessions; and to an explanation module configured to provide human-readable justifications for the decisions made by the system.

Description

The present invention relates to the field of artificial intelligence and human-computer interaction, more particularly to AI-based communication systems.Negotiations and debatts are basic components of human interaction, decision making, conflict resolution, and policy formulation. Traditionally, these processes rely heavily on human intuition, experience and communication capability. However, due to the subjective character of human behavior, they are often prone to distortions, emotional variations, and inconsistencies.With the increasing complexity of global interactions-whether in business agreements, legal arbitration methods, political diplomatia or online discursors-the need for intelligent systems that can support or automate aspects of negotiations and debatts is increasing. Current digital tools are limited to static decision trees, simple chatbs, or rule-based systems that lack the ability to dynamically learn, adapt strategies, and understand nuanced human communication in real-time.Recent advances in artificial intelligence, particularly machine learning, natural language processing (NLP), and reinforcement learning, provide new ways to build smart systems that simulate human thinking, understand context, and adapt strategies to achieve negotiation goals. However, existing systems often lack integration of emotional intelligence, contextual learning, and strategy optimization in real-time.Therefore, there is a need for an AI-based negotiation and discussion system that not only understands and generates human-like dialogs, but also continuously learns from interactions, adjusts its strategies based on opposing behavior, and optimizes results using intelligent negotiation and discussion frames. The present invention addresses this need by providing a robust and adaptable AI system capable of supporting or guiding negotiations and discounts in a wide range of environments.To solve this problem, the present invention provides an AI-based negotiation and discussion system with adaptive learning and strategy optimization.The system automatically and in real time adjusts the configurations of the data pipelines to variations in workload.The system utilizes adaptive learning algorithms to continually improve its negotiation and debattoe strategies based on historical interactions, the opponent's behavior, the results, and the evolving context.The system is capable of simulating and participating in human-like negotiations and discounts while considering the context.The system utilizes machine learning algorithms to adjust and refine the negotiation strategies based on historical data and user feedback.The system dynamically and in real time adjusts the policy to the emotional, logical or ethical pose of the opponent.The system uses natural language processing (NLP) and argument mining to effectively construct, score, and contend arguments.The system allows human users to train, monitor, and cooperate with AI negotiation partners in both competitive and cooperative environments.The system may be incorporated into existing decision support systems, chat platforms, or virtual assistants.In one embodiment, the system utilizes adaptive learning and dynamic strategy optimization to enable effective human-like negotiation and reasoning. The system utilizes advances in machine learning, natural language processing, and real-time behavioral analysis to provide smart and responsive virtual agents that can participate, support, or analyze negotiations and debatts in a variety of areas. The system includes an AI engine configured to analyze contextual information, extract arguments, score counter arguments, and generate convincing responses in both cooperative and opposing environments. The system continuously adjusts its negotiation strategies by incorporating feedback from previous interactions through supervised and augmented learning techniques. The system also has a clearable AI (XAI) module that provides transparent reasoning for decisions and strategies adopted during negotiation sessions. This enhances the trust of users and allows integration into legal, business, paedagogical and diploid applications where responsibility and interpretability are critical.The invention is explained again below with reference to the figure. The following shows: FIG. 1 : an AI-based negotiation and discussion system with adaptive learning and strategy optimization.FIG. 1 illustrates an AI-based negotiation and discussion system with adaptive learning and strategy optimization. The system includes a modular architecture that facilitates adaptive negotiations and debatching by AI-controlled components. In one embodiment, the system includes, but is not limited to, the following main modules, an input processing module configured to receive and process user input including natural language text, voice commands, or structured data, and convert such input to a format suitable for semantic analysis, possibly including a voice-to-text engine, tokenizer, and voice parser; a natural language understanding module configured to analyze user inputs for intent, mood, named entities and key sets, and comprising subcomponents such as named entity recognition units, syntactic and semantic parsers, and context disambiguation logic, wherein the output is a structured representation of the user's negotiation or argument statements; an argument mining and evaluation module operable to extract assertions, evidences, assumptions, and counter arguments from the processed inputs and map their logical relationships, such as support, attack, or contradiction, using a predefined ontology and / or machine learning models; a module for modeling the opponent configured to create dynamic profiles of negotiation participants based on behavioral characteristics including mood, rhetorian style, tempo, consent, and strategy patterns and use probabilistic or deep learning models to predict negotiation behavior; a strategy optimization engine configured to dynamically select and refine negotiation strategies using methods such as reinforcement learning, multi-armed bandites, or policy optimization to maximize a defined reward function based on session goals and historical data; a dialog management and generation module configured to generate context aware, convincing, and coherent dialog responses using templates, generative models, or hybrid approaches, thereby adapting tonefall, style, and perchability to user profiles and context; a feedback and learning module operable to update internal system models based on negotiation results, using online or offline learning techniques including reinforcement learning and continuous model training; an reconnaissibility and verification module that generates transparent, comprehensible reasoning of the system decisions including argument structures, strategy selections, and adaptation logic, in a human readable format for verification or compliance purposes; a user interface module providing interaction means such as dashboards, call agents or command line interfaces that allow users to configure negotiation objectives, review arguments, and track progress; and a communication and integration layer that supports secure, scalable interoperability with external systems via standardized communication protocols.List of reference characters100 System

Claims

An AI-based negotiation and discussion system (100) with adaptive learning and strategy optimization, comprising: an input processing module configured to receive and pre-process user inputs including natural language text or speech; a natural language understanding module configured to extract intent, mood, and contextual entities from the pre-processed input; an argument search module configured to identify and structure arguments, assertions, evidence, and antiarguments; an opponent modeling module configured to analyze the incoming data and generate dynamic behavior profiles of the negotiation partners; a strategy optimization engine configured to select and refine negotiation strategies based on adaptive learning algorithms; a dialog management module configured to generate and manage contextual responses; a feedback and learning module configured to update the system based on results of negotiation or discussion sessions; and an explanation module configured to provide human readable reasons for decisions made by the system.The system (100) of claim 1, wherein the input processing module includes a speech-to-text converter for processing speech-based inputs.The system (100) of claim 1, wherein the strategy optimization engine uses reinforcement learning to adapt negotiation strategies based on the interaction history.The system of (100) claim 1, wherein the anger modeling module uses mood analysis and rhetorial pattern recognition to infer the behavior of the opponent.The system (100) of claim 1, wherein the argument mining module applies machine learning models to extract support and attack relationships between arguments.The system (100) of claim 1, wherein the dialog management module uses a generative language model to construct convincing responses.The system of (100) claim 1, wherein the declarability module logs decision processes and outputs them in a structured reporting format.

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