REINFORCED FEEDBACK LEARNING SYSTEM FOR COLLABORATIVE AI NETWORKS

Abstract

ABSTRACT Reinforced Feedback Learning System for Collaborative AI Networks The present disclosure introduces reinforced feedback learning system for collaborative AI networks 100 which optimizes multi-agent collaboration using AI agents 102 equipped with reinforcement learning algorithms. It leverages a feedback mechanism 104 to capture performance metrics and share insights, with knowledge repository 106. Real-time synchronization are managed by the coordination module 108, while contextual adaptation engine 110 ensures agents adjust strategies dynamically. The other components are multi-modal communication protocol 112, hierarchical reinforcement learning framework 114, adaptive role assignment mechanism 116, collaborative performance metrics system 118, inter-agent reward sharing mechanism 120, integrative conflict resolution framework 122, distributed learning capability 124, self-assessment mechanism 126, collaborative simulation environment 128, real-time performance monitoring dashboard 130, transfer learning capability 132, collaborative risk assessment tools 134 and behavioural adaptation metrics 136. Reference Fig 1

Specification

Description:Reinforced Feedback Learning System for Collaborative AI Networks
TECHNICAL FIELD
[0001] The present innovation relates to field of collaborative artificial intelligence and machine learning systems, focusing on reinforcement learning and feedback mechanisms to enhance multi-agent interactions and decision-making.

BACKGROUND

[0002] Artificial Intelligence (AI) has significantly advanced in recent years, enabling applications in various sectors, including robotics, autonomous vehicles, smart cities, and healthcare. As AI systems become more interconnected, collaborative AI networks have emerged, allowing multiple agents to work together toward shared objectives. However, these networks face several challenges, such as synchronization issues, inefficient knowledge sharing, and the inability to adapt dynamically to changing environments. Existing systems often rely on isolated reinforcement learning (RL) methods or traditional collaborative frameworks that are limited in their scalability and adaptability. These approaches typically focus on individual agents learning through trial and error, with minimal mechanisms for real-time collaboration and feedback-driven learning.

[0003] Users have access to single-agent RL frameworks, federated learning systems, and decentralized AI networks to address collaborative needs. However, these systems come with notable drawbacks, such as slow learning processes, communication overhead, lack of coordinated action, and suboptimal decision-making. Additionally, many existing frameworks lack robust feedback mechanisms to facilitate real-time updates, making it challenging for agents to efficiently learn from each other's experiences in dynamic environments.


[0004] The reinforced feedback learning system for collaborative AI networks differentiates itself by integrating reinforcement learning with continuous feedback loops and dynamic role adaptation. Unlike conventional systems, this invention facilitates seamless communication among agents, enabling them to evaluate each other's actions, share insights, and modify their learning strategies in real-time. The system's novel features include a contextual adaptation engine, hierarchical reinforcement learning framework, and multi-modal communication protocol that enhance both individual and collective learning. By incorporating a shared knowledge repository and adaptive coordination module, the invention ensures scalability and flexibility, allowing the network to grow without compromising performance.

OBJECTS OF THE INVENTION

[0005] The primary object of the invention is to enhance multi-agent collaboration by utilizing reinforced feedback loops for more effective learning.

[0006] Another object of the invention is to improve decision-making processes through adaptive role assignment and real-time coordination among AI agents.

[0007] Another object of the invention is to facilitate efficient knowledge sharing by providing a shared repository for performance data and learned strategies.

[0008] Another object of the invention is to enable seamless scalability by allowing the integration of additional agents without compromising network performance.

[0009] Another object of the invention is to accelerate learning by incorporating dynamic feedback mechanisms that guide agents based on real-time experiences.

[00010] Another object of the invention is to promote adaptive behavior in changing environments through a contextual adaptation engine.

[00011] Another object of the invention is to optimize communication efficiency with a multi-modal communication protocol supporting diverse channels and signals.

[00012] Another object of the invention is to reduce conflict among agents by providing an integrative conflict resolution framework for collaborative decision-making.

[00013] Another object of the invention is to improve learning resilience through distributed learning capabilities across different environments or locations.

[00014] Another object of the invention is to enable faster problem-solving and adaptability by leveraging transfer learning, allowing agents to apply knowledge from one context to another


SUMMARY OF THE INVENTION

[00015] In accordance with the different aspects of the present invention, reinforced feedback learning system for collaborative AI networks is presented. It is an advanced framework that enhances multi-agent collaboration through reinforcement learning, dynamic feedback, and adaptive coordination. It enables agents to share knowledge, optimize decision-making, and continuously learn from real-time experiences. The system supports scalability, distributed learning, and conflict resolution, making it suitable for complex environments. Its innovative design promotes efficient communication through a multi-modal protocol and ensures agents adapt seamlessly to changing conditions.

[00016] Additional aspects, advantages, features and objects of the present disclosure would be made apparent from the drawings and the detailed description of the illustrative embodiments constructed in conjunction with the appended claims that follow.

[00017] It will be appreciated that features of the present disclosure are susceptible to being combined in various combinations without departing from the scope of the present disclosure as defined by the appended claims.

BRIEF DESCRIPTION OF DRAWINGS
[00018] The summary above, as well as the following detailed description of illustrative embodiments, is better understood when read in conjunction with the appended drawings. For the purpose of illustrating the present disclosure, exemplary constructions of the disclosure are shown in the drawings. However, the present disclosure is not limited to specific methods and instrumentalities disclosed herein. Moreover, those in the art will understand that the drawings are not to scale. Wherever possible, like elements have been indicated by identical numbers.

[00019] Embodiments of the present disclosure will now be described, by way of example only, with reference to the following diagrams wherein:

[00020] FIG. 1 is component wise drawing for reinforced feedback learning system for collaborative AI network.

[00021] FIG 2 is working methodology of reinforced feedback learning system for collaborative AI network.




DETAILED DESCRIPTION

[00022] The following detailed description illustrates embodiments of the present disclosure and ways in which they can be implemented. Although some modes of carrying out the present disclosure have been disclosed, those skilled in the art would recognise that other embodiments for carrying out or practising the present disclosure are also possible.

[00023] The description set forth below in connection with the appended drawings is intended as a description of certain embodiments of reinforced feedback learning system for collaborative AI network and is not intended to represent the only forms that may be developed or utilised. The description sets forth the various structures and/or functions in connection with the illustrated embodiments; however, it is to be understood that the disclosed embodiments are merely exemplary of the disclosure that may be embodied in various and alternative forms. The figures are not necessarily to scale; some features may be exaggerated or minimised to show details of particular components. Therefore, specific structural and functional details disclosed herein are not to be interpreted as limiting, but merely as a representative basis for teaching one skilled in the art to variously employ the present invention.

[00024] While the disclosure is susceptible to various modifications and alternative forms, specific embodiment thereof has been shown by way of example in the drawings and will be described in detail below. It should be understood, however, that it is not intended to limit the disclosure to the particular forms disclosed, but on the contrary, the disclosure is to cover all modifications, equivalents, and alternatives falling within the scope of the disclosure.

[00025] The terms "comprises", "comprising", "include(s)", or any other variations thereof, are intended to cover a non-exclusive inclusion, such that a setup, or system that comprises a list of components or steps does not include only those components or steps but may include other components or steps not expressly listed or inherent to such setup or system. In other words, one or more elements in a system or apparatus preceded by "comprises... a" does not, without more constraints, preclude the existence of other elements or additional elements in the system or apparatus.

[00026] In the following detailed description of the embodiments of the disclosure, reference is made to the accompanying drawings and which are shown by way of illustration-specific embodiments in which the disclosure may be practiced. These embodiments are described in sufficient detail to enable those skilled in the art to practice the disclosure, and it is to be understood that other embodiments may be utilized and that changes may be made without departing from the scope of the present disclosure. The following description is, therefore, not to be taken in a limiting sense.

[00027] The present disclosure will be described herein below with reference to the accompanying drawings. In the following description, well-known functions or constructions are not described in detail since they would obscure the description with unnecessary detail.

[00028] Referring to Fig. 1, reinforced feedback learning system for collaborative AI network 100 is disclosed, in accordance with one embodiment of the present invention. It comprises of AI agents 102, feedback mechanism 104, knowledge repository 106, coordination module 108, contextual adaptation engine 110, multi-modal communication protocol 112, hierarchical reinforcement learning framework 114, adaptive role assignment mechanism 116, collaborative performance metrics system 118, inter-agent reward sharing mechanism 120, integrative conflict resolution framework 122, distributed learning capability 124, self-assessment mechanism 126, collaborative simulation environment 128, real-time performance monitoring dashboard 130, transfer learning capability 132, collaborative risk assessment tools 134 and behavioural adaptation metrics 136.

[00029] Referring to Fig. 1, the present disclosure provides details of reinforced feedback learning system for collaborative AI network 100. It is a framework designed to enhance multi-agent collaboration using advanced reinforcement learning, feedback loops, and adaptive coordination. It enables AI agents 102 to learn from shared experiences, optimize decision-making, and adapt to dynamic environments. In one of the embodiments, the reinforced feedback learning system for collaborative AI network 100 may be provided with following key components such as feedback mechanism 104, knowledge repository 106, and coordination module 108, facilitating efficient information exchange and synchronization. The system incorporates contextual adaptation engine 110 and hierarchical reinforcement learning framework 114 to enable seamless scalability and role assignment. It also features multi-modal communication protocol 112 for flexible interaction and collaborative performance metrics system 118 to monitor efficiency. Additional components such as transfer learning capability 132 and collaborative risk assessment tools 134 enhance learning adaptability and decision-making.

[00030] Referring to Fig. 1, reinforced feedback learning system for collaborative AI networks 100 is provided with AI agents 102, which use reinforcement learning algorithms to perform specific tasks, adapt to changing environments, and learn from their experiences. These agents continuously interact with the feedback mechanism 104 to refine their strategies and collaborate effectively. The performance of these agents is further enhanced through real-time coordination managed by the coordination module 108.

[00031] Referring to Fig. 1, reinforced feedback learning system for collaborative AI networks 100 is provided with feedback mechanism 104, which captures performance metrics, action outcomes, and insights shared among agents 102. It plays a critical role in facilitating communication between agents, ensuring timely feedback to enhance decision-making. The feedback mechanism 104 works closely with the knowledge repository 106, storing and retrieving collaborative insights to guide learning.

[00032] Referring to Fig. 1, reinforced feedback learning system for collaborative AI networks 100 is provided with knowledge repository 106, which stores shared strategies, performance data, and learning insights collected from agents 102. It acts as a collective memory, ensuring agents can access and utilize past experiences. The repository interacts dynamically with the coordination module 108 to ensure all agents are aligned in their actions.

[00033] Referring to Fig. 1, reinforced feedback learning system for collaborative AI networks 100 is provided with coordination module 108, which manages communication and synchronization among agents 102, avoiding conflicts during collaboration. It assigns roles to agents dynamically, leveraging the adaptive role assignment mechanism 116 for optimized task distribution. The coordination module 108 ensures seamless execution by continuously monitoring feedback via the feedback mechanism 104.

[00034] Referring to Fig. 1, reinforced feedback learning system for collaborative AI networks 100 is provided with contextual adaptation engine 110, which enables agents 102 to adjust their strategies and behaviors in response to environmental changes. It works in tandem with the hierarchical reinforcement learning framework 114 to provide multi-level adaptability, ensuring agents remain effective in complex, dynamic environments. The contextual adaptation engine 110 also integrates insights from the knowledge repository 106 to enhance adaptability.

[00035] Referring to Fig. 1, reinforced feedback learning system for collaborative AI networks 100 is provided with multi-modal communication protocol 112, facilitating diverse interaction channels such as text, visual, and auditory signals among agents 102. This component ensures seamless information flow, even in heterogeneous environments. The protocol 112 works closely with the feedback mechanism 104 to deliver timely information across the network.

[00036] Referring to Fig. 1, reinforced feedback learning system for collaborative AI networks 100 is provided with hierarchical reinforcement learning framework 114, which allows agents 102 to operate at various abstraction levels, facilitating both high-level strategy development and low-level action execution. The framework interacts with the coordination module 108 to align actions and ensures smooth role transition via the adaptive role assignment mechanism 116.

[00037] Referring to Fig. 1, reinforced feedback learning system for collaborative AI networks 100 is provided with adaptive role assignment mechanism 116, which dynamically assigns and reassigns roles among agents 102 based on performance and environmental feedback. It ensures optimized collaboration by working in sync with the coordination module 108. The adaptive role assignment mechanism 116 also integrates data from the collaborative performance metrics system 118 to enhance efficiency.

[00038] Referring to Fig. 1, reinforced feedback learning system for collaborative AI networks 100 is provided with collaborative performance metrics system 118, which evaluates the efficiency and effectiveness of interactions among agents 102. It provides actionable insights that guide the coordination module 108 in managing agent roles. The system also influences learning rates through the feedback mechanism 104.

[00039] Referring to Fig. 1, reinforced feedback learning system for collaborative AI networks 100 is provided with inter-agent reward sharing mechanism 120, which distributes rewards among agents 102 based on their contributions to collaborative efforts. This mechanism fosters cooperation and aligns agent behaviors with shared goals. The reward sharing mechanism 120 relies on data from the feedback mechanism 104 to determine fair distribution.

[00040] Referring to Fig. 1, reinforced feedback learning system for collaborative AI networks 100is provided with integrative conflict resolution framework 122, which helps agents 102 navigate disagreements or conflicting strategies through negotiation and compromise. It works closely with the coordination module 108 to ensure smooth interactions and aligns with the collaborative performance metrics system 118 to maintain efficiency.

[00041] Referring to Fig. 1, reinforced feedback learning system for collaborative AI networks 100 is provided with distributed learning capability 124, which enables agents 102 to collaborate across different geographic locations or computing environments. This capability ensures resilience by reducing single points of failure and works with the knowledge repository 106 to maintain learning consistency.

[00042] Referring to Fig. 1, reinforced feedback learning system for collaborative AI networks 100 is provided with self-assessment mechanism 126, allowing agents 102 to evaluate their performance and identify areas for improvement. It integrates with the feedback mechanism 104 to facilitate continuous improvement and adapts learning strategies based on results.

[00043] Referring to Fig. 1, reinforced feedback learning system for collaborative AI networks 100 is provided with collaborative simulation environment 128, offering agents 102 a risk-free setting to test strategies and interactions before real-world deployment. Insights from simulations are stored in the knowledge repository 106 to guide future decisions and adaptations.

[00044] Referring to Fig. 1, reinforced feedback learning system for collaborative AI networks 100 is provided with real-time performance monitoring dashboard 130, which provides stakeholders with visibility into agent interactions, feedback dynamics, and learning progress. It integrates with the coordination module 108 to facilitate informed decision-making.

[00045] Referring to Fig. 1, reinforced feedback learning system for collaborative AI networks 100 is provided with transfer learning capability 132, allowing agents 102 to apply knowledge from one task or environment to another. It accelerates the learning process and promotes adaptability, especially when integrated with the contextual adaptation engine 110.

[00046] Referring to Fig. 1, reinforced feedback learning system for collaborative AI networks 100 is provided with collaborative risk assessment tools 134, which enable agents 102 to evaluate risks collectively and take preventive actions. These tools work closely with the coordination module 108 to align decisions with safety and efficiency goals.

[00047] Referring to Fig. 1, reinforced feedback learning system for collaborative AI networks 100 is provided with behavioural adaptation metrics 136, which measure the effectiveness of agent adaptations over time. These metrics help agents 102 refine their strategies and guide the self-assessment mechanism 126 for continuous learning.

[00048] Referring to Fig 2, there is illustrated method 200 for reinforced feedback learning system for collaborative AI networks 100. The method comprises:

At step 202, method 200 includes initializing AI agents 102 with reinforcement learning algorithms and assigning roles based on their tasks;

At step 204, method 200 includes each AI agent 102 exploring the environment by taking actions and generating data on the outcomes;

At step 206, method 200 includes AI agents 102 exchanging feedback through the feedback mechanism 104, providing evaluations of actions taken;

At step 208, method 200 includes AI agents 102 accessing the knowledge repository 106 to retrieve insights and strategies from their peers for collaborative learning;

At step 210, method 200 includes the coordination module 108 managing communication between AI agents 102 and synchronizing their actions for seamless collaboration;

At step 212, method 200 includes contextual adaptation engine 110 adjusting agent strategies based on environmental changes and real-time conditions;

At step 214, method 200 includes hierarchical reinforcement learning framework 114 guiding agents to operate at multiple abstraction levels for complex task execution;

At step 216, method 200 includes adaptive role assignment mechanism 116 dynamically adjusting agent roles based on performance metrics and feedback;

At step 218, method 200 includes collaborative performance metrics system 118 evaluating the efficiency of agent interactions and guiding further learning;

At step 220, method 200 includes inter-agent reward sharing mechanism 120 distributing rewards to AI agents 102 based on their contributions to collaborative efforts;

At step 222, method 200 includes integrative conflict resolution framework 122 resolving disagreements among AI agents 102 to ensure smooth collaboration;

At step 224, method 200 includes distributed learning capability 124 enabling AI agents 102 to learn across different geographic locations or environments;

At step 226, method 200 includes self-assessment mechanism 126 allowing AI agents 102 to monitor their performance and identify areas for improvement;

At step 228, method 200 includes AI agents 102 testing their strategies within the collaborative simulation environment 128 before applying them to real-world tasks;

At step 230, method 200 includes stakeholders monitoring real-time progress using the real-time performance monitoring dashboard 130 to track agent interactions and learning outcomes;

At step 232, method 200 includes transfer learning capability 132 allowing AI agents 102 to apply knowledge gained from one task or environment to another;

At step 234, method 200 includes collaborative risk assessment tools 134 helping AI agents 102 collectively evaluate and mitigate risks associated with actions and decisions;

At step 236, method 200 includes behavioral adaptation metrics 136 measuring how effectively AI agents 102 have adjusted their strategies over time, ensuring continuous learning and improvement


[00049] In the description of the present invention, it is also to be noted that, unless otherwise explicitly specified or limited, the terms "fixed" "attached" "disposed," "mounted," and "connected" are to be construed broadly, and may for example be fixedly connected, detachably connected, or integrally connected, either mechanically or electrically. They may be connected directly or indirectly through intervening media, or they may be interconnected between two elements. The specific meaning of the above terms in the present invention can be understood in specific cases to those skilled in the art.

[00050] Modifications to embodiments of the present disclosure described in the foregoing are possible without departing from the scope of the present disclosure as defined by the accompanying claims. Expressions such as "including", "comprising", "incorporating", "have", "is" used to describe and claim the present disclosure are intended to be construed in a non- exclusive manner, namely allowing for items, components or elements not explicitly described also to be present. Reference to the singular is also to be construed to relate to the plural where appropriate.

[00051] Although embodiments have been described with reference to a number of illustrative embodiments thereof, it should be understood that numerous other modifications and embodiments can be devised by those skilled in the art that will fall within the spirit and scope of the principles of this disclosure. More particularly, various variations and modifications are possible in the component parts and/or arrangements of the subject combination arrangement within the scope of the present disclosure, the drawings and the appended claims. In addition to variations and modifications in the component parts and/or arrangements, alternative uses will also be apparent to those skilled in the art.
, Claims:WE CLAIM:
1. A reinforced feedback learning system for collaborative AI networks 100 comprising of
AI agents 102 to perform tasks, adapt behavior, and learn through reinforcement learning algorithms;
feedback mechanism 104 to capture and exchange performance metrics and action outcomes among agents;
knowledge repository 106 to store shared strategies, performance data, and collaborative insights for continuous learning;
coordination module 108 to manage communication, synchronization, and task distribution among agents;
contextual adaptation engine 110 to adjust agent strategies based on real-time environmental conditions;
multi-modal communication protocol 112 to enable information exchange through diverse channels like text, visual, and auditory signals;
hierarchical reinforcement learning framework 114 to allow agents to operate at multiple abstraction levels for complex task execution;
adaptive role assignment mechanism 116 to dynamically assign and adjust roles of agents based on performance feedback;
collaborative performance metrics system 118 to evaluate the efficiency of agent interactions and guide further learning;
inter-agent reward sharing mechanism 120 to distribute rewards among agents based on collaborative contributions;
integrative conflict resolution framework 122 to resolve disagreements and conflicting strategies among agents;
distributed learning capability 124 to enable agents to collaborate and learn across geographic locations or environments;
self-assessment mechanism 126 to allow agents to monitor performance and identify areas for improvement;
collaborative simulation environment 128 to provide a risk-free setting for agents to test strategies and interactions;
real-time performance monitoring dashboard 130 to track agent interactions, feedback, and learning progress for stakeholders;
transfer learning capability 132 to allow agents to apply knowledge from one task or environment to another;
collaborative risk assessment tools 134 to help agents evaluate and mitigate risks associated with decisions and actions and
behavioural adaptation metrics 136 to measure how effectively agents adjust strategies over time for continuous improvement.

2. The reinforced feedback learning system for collaborative AI networks 100 as claimed in claim 1, wherein AI agents 102 are configured to perform tasks, learn through reinforcement algorithms, and adapt behavior based on collaborative feedback, enabling dynamic problem-solving and decision-making in complex environments.

3. The reinforced feedback learning system for collaborative AI networks 100 as claimed in claim 1, wherein feedback mechanism 104 is configured to capture performance metrics, share insights, and facilitate continuous learning by enabling real-time communication between agents for improved collaboration and efficiency.

4. The reinforced feedback learning system for collaborative AI networks 100 as claimed in claim 1, wherein knowledge repository 106 is configured to store shared strategies, learning insights, and performance data, ensuring continuous access to accumulated knowledge to optimize agent actions and decision-making processes.

5. The reinforced feedback learning system for collaborative AI networks 100 as claimed in claim 1, wherein coordination module 108 is configured to synchronize agent actions, dynamically assign roles, and manage task distribution based on real-time conditions, ensuring smooth collaboration and preventing conflicts.

6. The reinforced feedback learning system for collaborative AI networks 100 as claimed in claim 1, wherein contextual adaptation engine 110 is configured to adjust agent behavior and strategies based on environmental dynamics and task requirements, enhancing adaptability to changing scenarios.

7. The reinforced feedback learning system for collaborative AI networks 100 as claimed in claim 1, wherein multi-modal communication protocol 112 is configured to facilitate seamless information exchange across multiple channels, including text, visual, and auditory signals, ensuring effective interaction among agents.

8. The reinforced feedback learning system for collaborative AI networks 100 as claimed in claim 1, wherein adaptive role assignment mechanism 116 is configured to dynamically adjust agent roles and responsibilities based on performance feedback and environmental conditions, ensuring optimal task distribution and resource utilization.

9. The reinforced feedback learning system for collaborative AI networks 100 as claimed in claim 1, wherein collaborative performance metrics system 118 is configured to evaluate the efficiency and effectiveness of agent interactions, guiding further learning processes and enhancing collaborative outcomes through continuous feedback

10. The reinforced feedback learning system for collaborative AI networks 100 as claimed in claim 1, wherein method comprises of
initializing AI agents 102 with reinforcement learning algorithms and assigning roles based on their tasks;
each AI agent 102 exploring the environment by taking actions and generating data on the outcomes;
AI agents 102 exchanging feedback through the feedback mechanism 104, providing evaluations of actions taken;
AI agents 102 accessing the knowledge repository 106 to retrieve insights and strategies from their peers for collaborative learning;
coordination module 108 managing communication between AI agents 102 and synchronizing their actions for seamless collaboration;
contextual adaptation engine 110 adjusting agent strategies based on environmental changes and real-time conditions;
hierarchical reinforcement learning framework 114 guiding agents to operate at multiple abstraction levels for complex task execution;
adaptive role assignment mechanism 116 dynamically adjusting agent roles based on performance metrics and feedback;
collaborative performance metrics system 118 evaluating the efficiency of agent interactions and guiding further learning;
inter-agent reward sharing mechanism 120 distributing rewards to AI agents 102 based on their contributions to collaborative efforts;
integrative conflict resolution framework 122 resolving disagreements among AI agents 102 to ensure smooth collaboration;
distributed learning capability 124 enabling AI agents 102 to learn across different geographic locations or environments;
self-assessment mechanism 126 allowing AI agents 102 to monitor their performance and identify areas for improvement;
AI agents 102 testing their strategies within the collaborative simulation environment 128 before applying them to real-world tasks;
stakeholders monitoring real-time progress using the real-time performance monitoring dashboard 130 to track agent interactions and learning outcomes;
transfer learning capability 132 allowing AI agents 102 to apply knowledge gained from one task or environment to another;
collaborative risk assessment tools 134 helping ai agents 102 collectively evaluate and mitigate risks associated with actions and decisions;
behavioural adaptation metrics 136 measuring how effectively AI agents 102 have adjusted their strategies over time, ensuring continuous learning and improvement

Documents