MULTI-AGENT GOAL-SEEKING SYSTEMS AND METHODS

Abstract

ABSTRACT MULTI-AGENT GOAL-SEEKING SYSTEMS AND METHODS The present disclosure introduces a multi-agent goal-seeking systems and methods 100 designed for dynamic, autonomous collaboration across varied environments. It comprises of agent units 102 with decision-making algorithms, utilizing a hybrid goal-seeking mechanism 104 that combines reactive and proactive strategies for goal achievement. Adaptive communication protocols 106 facilitate real-time data exchange, while decentralized coordination and conflict resolution algorithms 108 allow agents to autonomously negotiate tasks. Additional components are hierarchical agent structure 110, self-healing and fault-tolerance module 112, Context-aware resource management 114, agent-to-human collaboration interface 116, cloud-based knowledge management system 118, an energy management and harvesting system 120, real-time environmental sensing and feedback loop 122, multi-objective optimization module 124, simulation and scenario analysis capabilities 126, behavioral consistency and trust metrics 128, real-time performance monitoring and analytics 130, multi-agent path planning with cooperative navigation 132, and dynamic decision-making algorithms 134. Reference Fig 1

Specification

Description: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 multi-agent goal-seeking systems and methods 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, multi-agent goal-seeking systems and methods 100 is disclosed in accordance with one embodiment of the present invention. It comprises of agent units 102, hybrid goal-seeking mechanism 104, adaptive communication protocols 106, decentralized coordination and conflict resolution algorithms 108, scalable hierarchical agent structure 110, self-healing and fault-tolerance module 112, context-aware resource management 114, agent-to-human collaboration interface 116, cloud-based knowledge management system 118, energy management and harvesting system 120, real-time environmental sensing and feedback loop 122, multi-objective optimization module 124, simulation and scenario analysis capabilities 126, behavioral consistency and trust metrics 128, real-time performance monitoring and analytics 130, multi-agent path planning with cooperative navigation 132 and dynamic decision-making algorithms 134.

[00029] Referring to Fig. 1, the present disclosure provides details of multi-agent goal-seeking system 100 designed to optimize data storage, retrieval, and resource allocation dynamically. This system leverages machine learning models 106 and ai-driven memory management software 104 to predict memory needs, allocate resources in real-time, and improve data access efficiency. In one embodiment, the ai-enhanced memory system may include key components such as memory hardware 102, memory management unit 108, and predictive data access and caching module 110, facilitating low-latency data retrieval and efficient memory use. The system also incorporates energy management module 112 and thermal management module 122 to reduce power consumption and control hardware temperature. Additional components such as self-healing system 134 and error detection, fault tolerance, and redundancy module 128 enhance system reliability and longevity.

[00030] Referring to Fig. 1, the present disclosure provides details of a multi-agent goal-seeking system 100 designed to enable autonomous collaboration across diverse environments. It leverages advanced decision-making, decentralized coordination, and adaptive communication protocols to enhance the efficiency and scalability of agent interactions. In one embodiment, the multi-agent goal-seeking system 100 may include key components such as agent units 102, hybrid goal-seeking mechanism 104, and adaptive communication protocols 106 to facilitate real-time collaboration and resource optimization. The system also integrates decentralized coordination algorithms 108 and scalable hierarchical agent structure 110 for seamless scalability and task management. Additional components such as real-time environmental sensing and feedback loop 122 and multi-agent path planning with cooperative navigation 132 enable continuous adaptability and safe navigation. Further, components like cloud-based knowledge management system 118 and dynamic decision-making algorithms 134 enhance collective intelligence and situational responsiveness.

[00031] Referring to Fig.1, the multi-agent goal-seeking system 100 is provided with agent units 102, which are autonomous entities designed to execute tasks independently or collaboratively within the system. Each agent unit 102 is equipped with decision-making algorithms and computational power, allowing it to operate seamlessly in various environments. These agents interact closely with the hybrid goal-seeking mechanism 104 to pursue both individual and collective objectives. Additionally, the agents 102 rely on adaptive communication protocols 106 for real-time data exchange, enabling efficient coordination in dynamic scenarios. Together, agent units 102 serve as the building blocks of the system, driving autonomous operations across a wide range of applications.

[00032] Referring to Fig.1, the multi-agent goal-seeking system 100 is provided with a hybrid goal-seeking mechanism 104, which allows agents 102 to achieve both reactive and proactive goals. This mechanism incorporates reinforcement learning and genetic algorithms, enabling agents 102 to adapt and optimize their behaviors based on real-time feedback. The hybrid goal-seeking mechanism 104 works in tandem with the multi-objective optimization module 124 to balance multiple, often competing, objectives. By providing agents 102 with strategic flexibility, this component ensures that they can effectively respond to changing environments while working toward long-term goals.

[00033] Referring to Fig.1, the multi-agent goal-seeking system 100 is provided with adaptive communication protocols 106, which facilitate efficient and secure information exchange among agents 102. These protocols support various communication modes, including peer-to-peer messaging, broadcast, and shared knowledge bases, adapting based on situational demands. Adaptive communication protocols 106 are essential for maintaining coordination and collaboration among decentralized agents 102 in real time. This component works closely with the decentralized coordination and conflict resolution algorithms 108, ensuring that communication is streamlined and that agents 102 can resolve conflicts effectively.

[00034] Referring to Fig.1, the multi-agent goal-seeking system 100 is provided with decentralized coordination and conflict resolution algorithms 108, which enable agents 102 to independently coordinate their tasks and resolve conflicts. By employing localized negotiation and resource-sharing strategies, this component allows agents 102 to work together without the need for a central controller. These algorithms work synergistically with the scalable hierarchical agent structure 110 to manage coordination in large-scale deployments. This approach improves system resilience and scalability, as agents 102 autonomously adapt to dynamic operational conditions.

[00035] Referring to Fig.1, the multi-agent goal-seeking system 100 is provided with a scalable hierarchical agent structure 110, allowing the system to expand efficiently as the number of agents 102 increases. In this hierarchical setup, higher-level agents oversee clusters of lower-level agents 102, managing tasks and ensuring smooth operation. This structure works closely with the decentralized coordination algorithms 108 to maintain effective task delegation and collaboration across all levels. By supporting large-scale operations, the hierarchical agent structure 110 enables the system to handle complex, distributed tasks across extensive networks.

[00036] Referring to Fig.1, the multi-agent goal-seeking system 100 is provided with a self-healing and fault-tolerance module 112, designed to autonomously detect and address failures within the network. This module enables agents 102 to reallocate tasks or reconfigure themselves when disruptions occur, maintaining system integrity without human intervention. The self-healing module 112 interacts with the real-time environmental sensing and feedback loop 122 to monitor performance and adjust as necessary. This robust fault-tolerance capability enhances the system's reliability, ensuring uninterrupted operations even in unpredictable conditions.
[00037] Referring to Fig.1, the multi-agent goal-seeking system 100 is provided with context-aware resource management 114, allowing agents 102 to adjust their resource usage, task priorities, and coordination strategies based on the current environment. This component evaluates system-wide and local conditions, optimizing efficiency under varying demands. Context-aware resource management 114 works in conjunction with the multi-objective optimization module 124 to balance resource allocation effectively. By adapting dynamically, this component supports sustainable operation and optimal resource utilization across the system.

[00038] Referring to Fig.1, the multi-agent goal-seeking system 100 is provided with an agent-to-human collaboration interface 116, which facilitates seamless interaction between human operators and agents 102. This interface allows users to set high-level goals, receive real-time updates, and provide critical inputs, particularly during complex tasks. The agent-to-human collaboration interface 116 integrates with the feedback loop 122 to deliver timely insights, keeping human stakeholders informed and in control. This component enhances transparency and adaptability, ensuring that agents 102 can incorporate human guidance when necessary.

[00039] Referring to Fig.1, the multi-agent goal-seeking system 100 is provided with a cloud-based knowledge management system 118, a shared repository that stores strategies, experiences, and environmental data for agent 102 access. Acting as a collective intelligence hub, this component allows agents 102 to leverage historical data to enhance decision-making and collaborative problem-solving. The cloud-based knowledge management system 118 works closely with collaborative learning across agents, ensuring that all agents 102 benefit from accumulated knowledge, thereby improving efficiency and adaptability.

[00040] Referring to Fig.1, the multi-agent goal-seeking system 100 is provided with an energy management and harvesting system 120, enabling physical agents 102 to optimize their power consumption during operation. This component includes energy-aware algorithms and renewable energy sources, like solar panels, to extend operational sustainability. The energy management system 120 interacts with context-aware resource management 114 to adjust energy usage based on demand. This feature is especially crucial in resource-constrained environments, promoting prolonged and efficient operation for physical agents 102.

[00041] Referring to Fig.1, the multi-agent goal-seeking system 100 is provided with a real-time environmental sensing and feedback loop 122 to continuously monitor surroundings and enable adaptive responses from agents 102. This feedback loop collects data on environmental changes, informing agents 102 to adjust actions accordingly. The real-time feedback loop 122 integrates with the hybrid goal-seeking mechanism 104 to refine goal-seeking strategies, ensuring agents 102 remain effective even in unpredictable environments.

[00042] Referring to Fig.1, the multi-agent goal-seeking system 100 is provided with a multi-objective optimization module 124, enabling agents 102 to pursue several goals simultaneously while managing trade-offs between competing objectives. This module dynamically prioritizes goals based on real-time conditions, facilitating efficient decision-making for agents 102. Working closely with the context-aware resource management 114, the multi-objective optimization module 124 ensures that agents 102 adapt their actions to maximize system-wide performance.

[00043] Referring to Fig.1, the multi-agent goal-seeking system 100 is provided with simulation and scenario analysis capabilities 126, which allow agents 102 to evaluate different strategies in simulated environments before real-world application. This component helps agents 102 minimize risks and optimize decision-making by testing potential outcomes. The simulation capabilities 126 collaborate with the hybrid goal-seeking mechanism 104, providing agents 102 with insights into the most effective approaches for complex tasks.

[00044] Referring to Fig.1, the multi-agent goal-seeking system 100 is provided with behavioral consistency and trust metrics 128, which track agents' 102 actions to establish reliability and support consistent collaboration. By assessing historical performance, this component enables agents 102 to make trust-based decisions in collaborative tasks. Behavioral consistency 128 works in alignment with the multi-agent path planning with cooperative navigation 132, ensuring that agents 102 operate predictably and safely within shared environments.

[00045] Referring to Fig.1, the multi-agent goal-seeking system 100 is provided with real-time performance monitoring and analytics 130, which continuously track agent 102 activities, resource usage, and goal achievement rates. This component provides performance insights, allowing agents 102 to refine strategies and adapt to changing conditions. Real-time performance analytics 130 integrates with the feedback loop 122 to enable continuous improvement, ensuring that agents 102 optimize their actions based on up-to-date metrics.

[00046] Referring to Fig.1, the multi-agent goal-seeking system 100 is provided with multi-agent path planning with cooperative navigation 132, which enables agents 102 to navigate shared spaces safely and efficiently. This component allows agents 102 to communicate planned paths and adjust trajectories based on others' movements, reducing collision risks. Multi-agent path planning 132 collaborates with the decentralized coordination algorithms 108 to maintain effective coordination even in congested environments.

[00047] Referring to Fig.1, the multi-agent goal-seeking system 100 is provided with dynamic decision-making algorithms 134, which enable agents 102 to select contextually appropriate strategies based on situational factors. These algorithms consider environment, mission priorities, and agent 102 capabilities, optimizing response times and outcomes. The dynamic decision-making algorithms 134 work in conjunction with the context-aware resource management 114 to ensure that agents 102 make informed decisions tailored to real-time conditions.

[00048] Referring to Fig 2, there is illustrated method 200 for multi-agent goal-seeking system 100. The method comprises:
At step 202, method 200 includes agents 102 initializing and identifying individual or collective goals using the hybrid goal-seeking mechanism 104;
At step 204, method 200 includes agents 102 establishing communication links through adaptive communication protocols 106 to coordinate actions and share real-time data;
At step 206, method 200 includes agents 102 autonomously coordinating their tasks and resolving conflicts using decentralized coordination and conflict resolution algorithms 108, thereby ensuring smooth collaboration without centralized control;
At step 208, method 200 includes the hierarchical agent structure 110 organizing agents 102 into clusters, where higher-level agents manage lower-level agents for scalable and efficient task distribution;
At step 210, method 200 includes the self-healing and fault-tolerance module 112 monitoring agents 102 for any failures or disruptions and autonomously reassigning tasks as needed to maintain continuous operations;
At step 212, method 200 includes agents 102 dynamically adjusting resource use, task prioritization, and energy management through context-aware resource management 114 based on real-time environmental conditions;
At step 214, method 200 includes agents 102 interacting with human operators via the agent-to-human collaboration interface 116, allowing users to set goals, receive updates, and make adjustments as required;
At step 216, method 200 includes agents 102 accessing shared experiences and strategic data from the cloud-based knowledge management system 118 to optimize decision-making and enhance problem-solving capabilities;
At step 218, method 200 includes the energy management and harvesting system 120 monitoring and optimizing power consumption of physical agents 102 and utilizing renewable energy sources to extend operational life;
At step 220, method 200 includes agents 102 gathering and analyzing environmental data through the real-time environmental sensing and feedback loop 122, adjusting actions to align with current conditions;
At step 222, method 200 includes agents 102 balancing multiple objectives such as efficiency and task completion speed using the multi-objective optimization module 124 to manage trade-offs in complex scenarios;
At step 224, method 200 includes agents 102 evaluating potential strategies in simulated environments using simulation and scenario analysis capabilities 126 to select the most effective approaches for task execution;
At step 226, method 200 includes agents 102 building behavioral consistency and trust metrics 128 by tracking historical actions, which supports reliable cooperation among agents;
At step 228, method 200 includes agents 102 monitoring their activities, resource utilization, and goal achievements through real-time performance monitoring and analytics 130 to refine strategies and improve overall performance;
At step 230, method 200 includes agents 102 using multi-agent path planning with cooperative navigation 132 to navigate shared spaces safely, adjusting their paths to avoid collisions and optimize movement;
At step 232, method 200 includes agents 102 employing dynamic decision-making algorithms 134 to choose strategies based on situational factors, ensuring optimal responses tailored to real-time conditions.
[00049] The multi-agent goal-seeking system can be applied in different embodiments across various industries to enhance efficiency, adaptability, and autonomous operations. In one of the embodiments in autonomous transportation, the system enables vehicles to act as agents within a larger network, communicating with each other and infrastructure to optimize traffic flow, avoid collisions, and reduce congestion.

[00050] In another embodiment in smart manufacturing and Industry 4.0, the system allows robots and machinery to coordinate production tasks, dynamically allocate resources, and improve automation by responding to real-time demands, maximizing productivity and minimizing downtime.

[00051] In yet another embodiment in distributed energy management in smart grids, agents representing different energy sources and consumers can autonomously manage energy distribution, ensuring balanced supply and demand across the network.

[00052] In yet another embodiment, system also finds applications in security and surveillance, where drones or camera-equipped agents collaborate to monitor large areas, detect threats, and coordinate responses, offering comprehensive and resilient security solutions. Additionally, in urban planning and resource management, agents help optimize resource allocation, manage waste, and monitor environmental conditions, contributing to sustainable and efficient city infrastructures.

[00053] 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.

[00054] 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.

[00055] 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 multi-agent goal-seeking system 100 comprising of
agent units 102 to act as autonomous entities for independent and collaborative task execution;
hybrid goal-seeking mechanism 104 to enable both reactive and proactive goal achievement;
adaptive communication protocols 106 to facilitate efficient, secure information exchange among agents;
decentralized coordination and conflict resolution algorithms 108 to support autonomous task coordination and conflict resolution;
scalable hierarchical agent structure 110 to manage agents in clusters for effective task distribution;
self-healing and fault-tolerance module 112 to detect and address system disruptions autonomously;
context-aware resource management 114 to dynamically adjust resources based on real-time environmental conditions;
agent-to-human collaboration interface 116 to allow human operators to set goals and receive updates;
cloud-based knowledge management system 118 to store and share collective intelligence among agents;
energy management and harvesting system 120 to optimize power usage and extend agent operation with renewable sources;
real-time environmental sensing and feedback loop 122 to monitor surroundings and adjust actions accordingly;
multi-objective optimization module 124 to balance competing goals and manage trade-offs;
simulation and scenario analysis capabilities 126 to evaluate strategies in a controlled environment;
behavioral consistency and trust metrics 128 to build reliability for cooperation among agents;
real-time performance monitoring and analytics 130 to track activities and refine strategies;
multi-agent path planning with cooperative navigation 132 to ensure safe and efficient movement in shared spaces; and
dynamic decision-making algorithms 134 to select strategies based on real-time situational factors.
2. The multi-agent goal-seeking system and methods 100 as claimed in claim 1, wherein agent units 102 are configured as autonomous entities capable of executing independent and collaborative tasks, utilizing decision-making algorithms to dynamically adapt to varying operational conditions, thereby enabling robust multi-agent interactions across diverse environments.

3. The multi-agent goal-seeking system and methods 100 as claimed in claim 1, wherein hybrid goal-seeking mechanism 104 is configured to provide both reactive and proactive goal-achievement strategies by integrating reinforcement learning and optimization algorithms, allowing agents 102 to respond dynamically while advancing long-term objectives.

4. The multi-agent goal-seeking system and methods 100 as claimed in claim 1, wherein adaptive communication protocols 106 are configured to facilitate multi-mode, secure information exchange among agents 102, enabling seamless real-time coordination with minimal communication overhead in changing scenarios.

5. The multi-agent goal-seeking system and methods 100 as claimed in claim 1, wherein decentralized coordination and conflict resolution algorithms 108 are configured to allow agents 102 to autonomously negotiate task assignments and resolve resource conflicts without centralized control, enhancing system scalability and operational resilience.

6. The multi-agent goal-seeking system and methods 100 as claimed in claim 1, wherein self-healing and fault-tolerance module 112 is configured to autonomously detect system disruptions, reassign tasks, and maintain continuous operation, ensuring robustness in the presence of agent failures or environmental anomalies.

7. The multi-agent goal-seeking system and methods 100 as claimed in claim 1, wherein cloud-based knowledge management system 118 is configured to store and share collective intelligence, including historical performance data and learning insights, to optimize decision-making and enhance collaborative problem-solving among agents 102.

8. The multi-agent goal-seeking system and methods 100 as claimed in claim 1, wherein real-time environmental sensing and feedback loop 122 is configured to continuously monitor external conditions, enabling agents 102 to adjust actions dynamically to maintain alignment with environmental changes and task requirements.

9. The multi-agent goal-seeking system and methods 100 as claimed in claim 1, wherein multi-objective optimization module 124 is configured to manage competing goals, allowing agents 102 to balance priorities and make real-time trade-offs to achieve optimal performance in complex, multi-faceted operational settings.

10. The multi-agent goal-seeking system and methods 100 as claimed in claim 1, wherein method comprises of
agents 102 initializing and identifying individual or collective goals using the hybrid goal-seeking mechanism 104;
agents 102 establishing communication links through adaptive communication protocols 106 to coordinate actions and share real-time data;
agents 102 autonomously coordinating their tasks and resolving conflicts using decentralized coordination and conflict resolution algorithms 108, thereby ensuring smooth collaboration without centralized control;
the hierarchical agent structure 110 organizing agents 102 into clusters, where higher-level agents manage lower-level agents for scalable and efficient task distribution;
the self-healing and fault-tolerance module 112 monitoring agents 102 for any failures or disruptions and autonomously reassigning tasks as needed to maintain continuous operations;
agents 102 dynamically adjusting resource use, task prioritization, and energy management through context-aware resource management 114 based on real-time environmental conditions;
agents 102 interacting with human operators via the agent-to-human collaboration interface 116, allowing users to set goals, receive updates, and make adjustments as required;
agents 102 accessing shared experiences and strategic data from the cloud-based knowledge management system 118 to optimize decision-making and enhance problem-solving capabilities;
the energy management and harvesting system 120 monitoring and optimizing power consumption of physical agents 102 and utilizing renewable energy sources to extend operational life;
agents 102 gathering and analyzing environmental data through the real-time environmental sensing and feedback loop 122, adjusting actions to align with current conditions;
agents 102 balancing multiple objectives such as efficiency and task completion speed using the multi-objective optimization module 124 to manage trade-offs in complex scenarios;
agents 102 evaluating potential strategies in simulated environments using simulation and scenario analysis capabilities 126 to select the most effective approaches for task execution;
agents 102 building behavioral consistency and trust metrics 128 by tracking historical actions, which supports reliable cooperation among agents;
agents 102 monitoring their activities, resource utilization, and goal achievements through real-time performance monitoring and analytics 130 to refine strategies and improve overall performance;
agents 102 using multi-agent path planning with cooperative navigation 132 to navigate shared spaces safely, adjusting their paths to avoid collisions and optimize movement;
agents 102 employing dynamic decision-making algorithms 134 to choose strategies based on situational factors, ensuring optimal responses tailored to real-time conditions.

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