550 results about "Multi-agent system" patented technology

The invention belongs to the field of natural language processing, and provides a task complexity driven graph semantic multi-agent collaborative decision-making method and system.The task complexity driven graph semantic multi-agent collaborative decision-making method comprises the steps that a task text is obtained and subjected to semantic coding to obtain a task semantic vector, evaluation is conducted based on the task semantic vector to obtain a complexity vector, and a task complexity score of the complexity vector is calculated; the task semantic vector and the complexity vector are fused to obtain a task representation vector, an agent capability relation graph is constructed, the participation probability of each agent node is obtained according to the task representation vector and the agent capability relation graph, and a dynamic agent combination scheme is formed; and performing task decomposition according to the agent combination scheme, constructing a sub-task dependency graph, scheduling the execution sequence of the sub-tasks through topological sorting, realizing cooperative execution of the agents, and generating a task result. According to the method, precise matching and efficient cooperation of the agent combination are realized, and the capability of processing complex tasks and the resource utilization efficiency of the multi-agent system are remarkably improved.

The present disclosure provides a system for secure compute using artificial intelligence agents. The system includes a hardware execution environment with one or more computerized processors and electronic storage media. The processors are configured to establish a secure agent comprising a secure state management module, an encrypted state transition management module, a threshold cryptography implementation, and a digital signature verification module. The system also includes a plurality of atomic agents, each configured to perform a composite task and comprising a task-specific execution module, a state management interface, and a communication module. An orchestrator agent coordinates secure execution of the atomic agents and includes a task distribution module, a result aggregation module, a workflow management module, and a security policy enforcement module. The orchestrator agent integrates encrypted outputs from the atomic agents to perform composite tasks while maintaining data integrity and security through the secure state management module.

The present disclosure provides a multi-agent system-based information processing method and a multi-agent system. The multi-agent system comprises multiple agents. The method is applied to a target agent, and the target agent is any agent among the multiple agents. The method comprises: determining information to be processed corresponding to a target agent; on the basis of an information processing model, when said information does not match a function corresponding to the target agent, determining an agent to which a function matching said information corresponds; and sending said information to the matching agent for processing. In the present disclosure, information to be processed can be quickly and accurately transferred between agents by utilizing the analytical capability of an information processing model, and is processed by a suitable agent, thereby effectively improving the processing effect of tasks.

The invention discloses a multi-agent cooperation method based on a large language model, and relates to the field of agent cooperation control, and the method comprises the steps: constructing a multi-agent system; establishing a multi-agent coordination resource allocation model; a task allocation problem in the resource allocation model is converted into a multi-objective optimization problem, and optimization objectives comprise the minimum task completion time and the optimal resource utilization rate; and solving a multi-objective optimization problem by using an improved particle swarm optimization (PSO) to obtain an optimal cooperation scheme among the master control agent, the task creation agent, the intention arrangement agent and the at least one professional agent. The improved particle swarm optimization (PSO) evaluates the uncertainty of each candidate solution through multiple times of random sampling, and when the uncertainty exceeds a threshold value, cross validation is performed through a multi-agent voting mechanism. For uncertainty accumulation of a multi-agent system under a complex cognitive task, the efficiency and stability of collaborative decision and task scheduling of the multi-agent system are improved.

The invention discloses a hierarchical multi-agent robot cluster control method and system, and aims to solve the problems of partial observability and environment non-stability of a multi-agent system in a complex environment. According to the method, a three-layer layered reinforcement learning architecture is constructed, a high-layer strategy is responsible for global task decomposition and role allocation, a middle-layer strategy converts tactical intention into a cooperative behavior mode, and a low-layer strategy executes accurate motion control; a graph neural network is adopted for cluster modeling, global graph representation and local neighborhood features are extracted in parallel through graph convolution and an attention mechanism, and hierarchical decision making is supported; a centralized graph enhancement evaluation network is designed to be combined with an MAPPO algorithm for collaborative optimization, and dynamic adversarial training is introduced to improve strategy robustness. According to the method, effective decoupling of global planning and local control is realized, and the cluster cooperation efficiency, the strategy interpretability and the adaptive capacity in a dynamic environment are improved.

The invention discloses a data-based predefined time heterogeneous multi-agent formation collision avoidance method. The method comprises the following steps: establishing a nonlinear heterogeneous multi-agent system; designing a bimodal artificial potential field function to perform dynamic obstacle avoidance and prevent regional escape; establishing a self-adaptive robust controller used for generating an obstacle avoidance safety motion trail of the root leader; designing a self-adaptive formation zooming mechanism of the leader, and constructing a predefined time affine observer of the follower based on the self-adaptive formation zooming mechanism; designing a unified obstacle function; constructing a virtual control law for processing tracking errors based on the unified obstacle function; designing a neural network estimator; designing controllers of the leader and the follower according to the virtual control law; and forming a collision avoidance decision of the heterogeneous multi-agent formation based on a self-adaptive robust controller, a predefined time affine observer, a neural network estimator and controllers of the leader and the follower. According to the method, safe, efficient and robust cooperative control of the formation in a complex environment is realized, and the safety and task execution efficiency of the heterogeneous multi-agent formation in a limited and unknown environment are remarkably improved.

The invention discloses a visual algorithm self-training method based on multi-agent collaborative optimization, and the method comprises the following steps: constructing a multi-agent system architecture which comprises a user interaction layer, an intelligent scheduling layer, an A2A protocol communication layer and a professional agent cluster layer; the user interaction layer analyzes a user task intention and generates an execution plan; the scheduling agent calls the professional agent to complete data processing, model construction, training, testing and deployment; a task process is coordinated through a standardized communication mechanism, and task execution is supported by combining an MCP tool set, a knowledge base module and a memory system; and when the task fails, automatically executing rescheduling operation, and finally outputting a self-training result. According to the method, the development efficiency, the self-adaptability and the intelligent level are remarkably improved, and the method is suitable for computer vision tasks such as industrial detection, intelligent security and protection and automatic driving.

The invention relates to the field of multi-agent system control, and particularly provides a second-order nonlinear multi-agent system consistency control method and system, and the method comprises the steps: building a second-order nonlinear multi-agent dynamic system model with a plurality of same agents; establishing a leader following finite time consistency sufficient condition; establishing a distributed sliding mode surface based on a second-order nonlinear multi-agent dynamic system model; obtaining a control protocol by combining a preset dynamic event triggering condition, a preset adaptive law and a distributed sliding mode surface, and introducing the control protocol into a distributed time triggering controller to obtain a novel controller; and verifying the novel controller by utilizing a condition that the leader follows the finite time consistency is sufficient, obtaining a stable expected trajectory of the multi-agent, and completing the control of the consistency of the second-order nonlinear multi-agent system. According to the invention, trajectory tracking and consistency control of the leader following intelligent agent system are realized, and external disturbance can be effectively inhibited.

The invention provides a distributed formation control method driven by a recursive balance network under a communication attack, and relates to the technical field of cooperative control of a multi-agent system, and the method comprises the steps: constructing a distributed multi-agent system model, and constructing an RBN controller heterogeneous communication network architecture and a DoS attack model; the RBN controller design is optimized, the distributed RBN controller architecture is realized, the stability of the RBN controller is analyzed based on the shrinkage mapping theory, and the exponential convergence of the system state difference is proved by constructing a Lyapunov function; training the optimized RBN controller by adopting a difficulty priority confrontation training strategy, designing a multi-objective loss function, and dynamically adjusting the training weight of each difficulty level; and simulating the distributed multi-agent system optimized by the above steps, testing the robustness of the distributed multi-agent system under incremental DoS attack intensity, and performing comparative analysis of heterogeneous and isomorphic communication network configuration and comprehensive performance comparison of an RBN controller and a traditional MPC method.

Provided are a multi-agent construction method and apparatus, a task processing method and apparatus, a computing device, a storage medium, a computer program product, and a chip. The multi-agent construction method comprises: splitting a task into a plurality of sub-tasks; determining, from an agent pool, agents respectively matching the plurality of sub-tasks, wherein the agent pool comprises a plurality of agents, and the agents matching the sub-tasks can process the sub-tasks by calling at least one of tools and other agents which are configured by the agents; and constructing a multi-agent system on the basis of the agents respectively matching the plurality of sub-tasks, wherein the multi-agent system is used for processing the task.

The invention relates to the technical field of medical information, in particular to a method and a system for processing medical record documents by utilizing artificial intelligence, and particularly relates to a method and a system which can receive and process multi-modal input information including images, videos and voices, can work cooperatively through a multi-agent system and can process the medical record documents. The invention discloses a system for automatically generating, controlling quality and safely inputting medical records in combination with a medical knowledge base and an implementation method thereof. The invention relates to an automatic medical record writing system based on multi-modal input and multi-agent driving. The automatic medical record writing system comprises a multi-modal input module, a multi-agent processing platform and a safety intranet input module. According to the method, establishment of the data channel between the medical intranet and the external AI system is proposed for the first time, the problem of medical intranet isolation is solved, industrial pain points are solved in a breakthrough mode, and a new medical AI landing path is developed.

The invention relates to the technical field of intelligent unmanned systems, in particular to a task allocation method of a sea area cross-medium unmanned system based on a graph attention network and deep reinforcement learning, and the method comprises the following steps: S1, constructing a dynamic graph and calculating task priority; s2, screening and selecting schedulable tasks; s3, intelligent agent distribution and state updating; s4, dynamic reordering and cycle control are carried out; according to the method, a space-time coupling dynamic graph structure is constructed, task attributes and agent states are coded into node features, a four-layer graph attention network is designed to extract task priority distribution, and a self-adaptive scheduling decision is realized in combination with a near-end strategy optimization algorithm; a task dependence verification and resource availability check mechanism is established, and approximate optimal agent allocation is realized; a multi-dimensional reward function is included, training stability is guaranteed in combination with an experience playback mechanism and a gradient clipping technology, and task allocation efficiency of a multi-agent system in a complex environment is remarkably improved.

The invention belongs to the technical field of computers, and discloses a resource elastic scaling method and system based on the average consistency of a multi-agent system, and the method comprises the steps: obtaining the state information of a current server, and estimating user input based on the state information and a scaling decision at the last moment; an event trigger is designed, and if the event trigger based on the edge is triggered, the server sends own state information to the neighbor and receives the state information of the neighbor; if the event trigger based on the node is triggered, the server records the state of the load observer of the server and the information of the self-adaptive variable; and forming a scaling decision according to the state information of the server at the last triggering moment, the state information of the neighbor server and the state information of the load observer, executing the scaling decision and updating the state information of the server. According to the method, on the premise that global information of communication topology is not used, the security resources of the server cluster under DSS attack are elastically expanded and contracted, and the server cannot be misguided by tampered information.

The invention relates to a multi-agent system distributed consistency optimization method considering an unbalanced directed communication graph, and belongs to the field of multi-agent system coordination control. The method comprises the following steps: S1, establishing a first-order dynamic model of a multi-agent system; s2, constructing an unbalanced directed communication topology network of the multi-agent system, and describing a communication relationship between agents through an adjacent matrix; s3, defining a global optimization target to minimize the sum of local cost functions of all agents; s4, constructing a distance-based adaptive precise penalty function, and converting a set constraint optimization problem into a set-free constraint optimization problem; s5, designing an adaptive coupling gain to solve a distributed optimization problem, and designing an auxiliary variable to estimate a left eigenvector corresponding to a zero eigenvalue of the Laplacian matrix; and then updating the state variable and the auxiliary variable of the intelligent agent according to the estimated value, and solving the problem of consistency optimization of the multi-intelligent-agent system of which the cost function cannot be differentiated.

The invention belongs to the technical field of smart power grids, and discloses a distributed photovoltaic cluster-based smart power grid optimal scheduling method and system, and the method comprises the steps: fusing meteorological and power grid data through a cross-modal sensing network, combining a meta-learning framework to quickly adapt to a new power station, and narrowing a fluctuation interval through a prediction-correction dual-channel mechanism; according to the method, implicit association between weather and a power grid is mined, prediction robustness of low-probability events is improved through extreme scene intensive training, output prediction precision is remarkably improved, and the problem that traditional prediction lags behind actual fluctuation is effectively relieved; a dynamic role multi-agent system is adopted, and a credit scoring mechanism of block chain evidence storage and an improved auction algorithm are combined, so that distributed efficient decision making is realized; the intelligent agent dynamically switches roles according to the load state, the credit condition is linked with the scheduling priority, the problem that the topology adaptability of centralized decision making is poor is solved, and a trusted collaborative environment is constructed through transaction records which cannot be tampered.

The invention provides a thinking map-based sustainable learning multi-agent reasoning method and system. A reasoning task is cooperatively completed by transferring a task planning agent, a step execution agent, a task solving agent, an reflection agent, a historical experience library and a knowledge library; through a query matching algorithm and a continuous learning mechanism of a historical experience library, the reasoning efficiency and adaptability of the system are improved; the thinking map is combined with the knowledge base, so that the reasoning ability of the system in different tasks and scenes is enhanced; the intelligent agent system mounted with the knowledge base cooperates with an external calling mechanism, so that the knowledge retrieval and generation capability of the system is improved; and continuously optimizing a task execution result through evaluation of the reflection agent and a user feedback mechanism. According to the method, a thinking map is introduced to optimize a task decomposition process, a multi-stage knowledge base is constructed by utilizing historical experience data to realize continuous learning, and the deployment cost is reduced by calling a special small model or an external tool, so that the efficiency and the flexibility of a multi-agent system are remarkably improved.

The present invention relates to an autonomous driving test method based on multi-agent swarm adversarial, a device and a medium. The method includes: deducing a conflict topological relationship graph between a tested autonomous vehicle and an agent; deducing a feasible planning space of the tested autonomous vehicle according to the conflict topological relationship graph; establishing a multi-agent swarm adversarial model based on a potential game under the feasible planning space according to a correlation between an individual reward of the agent and a swarm adversarial test effect of a multi-agent system, and solving and obtaining an optimal adversarial strategy of the multi-agent system against the tested autonomous vehicle, where in the multi-agent swarm adversarial model, an adversarial intensity is introduced, and the adversarial intensity is adaptively adjusted according to an actual response of the tested autonomous vehicle; and repeatedly executing the S1-S3 until an adversarial task is completed.

The invention relates to the technical field of multi-agent cooperative control, and discloses a multi-agent autonomous decision-making method based on deep reinforcement learning. The method comprises the steps of synchronously detecting an initial collaborative state of a cluster, performing joint situation assessment, and judging a collaborative operation mode according to a quantitative situation. And analyzing the capability of each agent and the real-time task load, and constructing a distributed task knowledge graph. And utilizing the atlas to drive a deep reinforcement learning network, coupling computing resource allocation and a task path, and generating a preliminary behavior strategy of each agent. And performing cluster-level conflict detection and iterative negotiation adjustment, and finally issuing an executable action instruction sequence. According to the method, integrated optimization of resource allocation and action paths is realized, the situation understanding and negotiation mechanism is enhanced through the knowledge graph to guarantee the collaborative consistency, and the collaborative decision-making efficiency and task execution robustness of a multi-agent system in a dynamic environment are improved.

The invention provides a multi-agent-based task cooperative execution method and device, and the method comprises the steps: carrying out the calculation based on the task type of a target task and the current system state of a multi-agent system, and obtaining an index weight; determining a task allocation scheme of each subtask based on the index weight and the agent bidding information of each subtask; and determining an execution strategy of each sub-task based on the task attribute of each sub-task, the task allocation scheme and the environment perception data of the multi-agent system. According to the method and device provided by the invention, the index weight is calculated according to the task type of the target task and / or the current system state of the multi-agent system; the task allocation scheme of each subtask is determined based on the index weight and the bidding information of each subtask corresponding to the plurality of agents, so that dynamic task allocation adapting to environment change is realized, the task allocation rationality is improved, and the task execution efficiency and task completion quality of the system are improved.

The invention discloses a multi-agent-based context knowledge internalization, sharing and multiplexing system and method. The system comprises a multi-source knowledge capture module, a situation information extraction and structuring module, a knowledge internalization and incremental learning module, a situation awareness knowledge reuse and recommendation module and a knowledge quality evaluation and maintenance module, wherein each module is composed of a special intelligent agent or a functional unit and works cooperatively; according to the method, interaction, cooperation and process data and expert input of a task execution full cycle of a multi-agent system are captured through a multi-source module, preprocessing is carried out through a situation extraction module, a situation is associated to generate structured knowledge, and then the structured knowledge is fused into a dynamic knowledge system through an internalization module to achieve incremental learning. In a new task, matching knowledge is retrieved and recommended through the context awareness module, and finally the knowledge quality is periodically maintained by the evaluation module to ensure accuracy and timeliness; according to the method, the autonomous learning ability, the knowledge reuse efficiency and the intelligent level of solving complex problems of the multi-agent system are remarkably improved.

The invention discloses a heterogeneous agent distributed multi-alliance game control method, device and equipment and a medium, and relates to the field of multi-agent system cooperative control and game theory cross application, and the method comprises the steps: dividing agents in a heterogeneous unmanned cluster system into a plurality of alliances; establishing a high-order heterogeneous linear state dynamical model for the intelligent agent in each alliance; constructing an internal and external double-layer alliance communication topological graph according to an actual communication condition; and on the basis of the model and the topological graph, through a distributed Nash equilibrium search algorithm containing a search layer and an output adjustment layer, performing iterative optimization on an agent decision state to obtain a multi-alliance game strategy, and controlling the agent according to the strategy. According to the method, the cooperative control problem of the heterogeneous agents in a multi-alliance game scene can be effectively solved, the Nash equilibrium point is quickly searched, the decision state is adjusted to be optimal, and the cooperative control performance and task execution efficiency of a multi-agent system are improved.

The embodiment of the invention provides a context information management method and device based on hierarchical memory, equipment and a medium, and the method comprises the steps: analyzing a task request to generate a sub-task node sequence containing a target description and acceptance function, and arranging a plurality of agents to cooperatively execute a task, and after the audit is passed, a complete execution context is stored in a long-term memory, and a structured abstract is generated and stored in a short-term memory, so that the problem that the context management mechanism of the existing multi-agent system is single is effectively solved, stable maintenance of cross-session cognitive continuity is realized, and the efficiency of the multi-agent system is improved. The method improves the execution reliability of a complex long-process task, optimizes the information storage and retrieval efficiency through hierarchical memory, reduces the risk of semantic deviation accumulation and amplification, clarifies the task target and acceptance standard of each stage, enhances the multi-agent cooperation consistency, and improves the efficiency of task execution. And the execution efficiency and the task completion quality of the intelligent agent system in a complex application scene are comprehensively improved.

The invention provides a multi-agent proposition method and system based on collaborative reinforcement learning. The method comprises the following steps: constructing a multi-agent proposition framework comprising a test question generation agent and a quality inspection agent based on a large language model; constructing training data on the basis of a target subject test question set, respectively supervising and adjusting large language models on which a test question generation agent and a quality inspection agent are based, and training a reward model for evaluating the output quality of each agent for the agent; and performing multi-agent collaborative reinforcement learning training on the test question generation agent and the quality inspection agent by using the reward model to obtain a trained agent model weight. And loading the trained agent model weight into a multi-agent collaborative proposition framework, and receiving proposition requirements to generate final test questions and reference answers. A multi-agent cooperative reinforcement learning mechanism is introduced into a multi-agent system, so that the agents can form a stable and efficient cooperation strategy in a real proposition task interaction environment.

The invention provides a dialogue chain multi-dimensional semantic enhancement method based on an MCP agent negotiation and voting mechanism, and relates to the field of artificial intelligence, and the method comprises the steps: outputting a plurality of candidate dialogue chains based on a large language model; performing multi-dimensional semantic enhancement on the plurality of candidate dialogue chains to obtain a plurality of enhanced dialogue chains; outputting a corresponding dialogue chain score for each enhanced dialogue chain through each preset agent; screening the plurality of dialogue chain scores, and obtaining a target consistency score of which the score value is greater than a preset ranking in the plurality of dialogue chain scores; determining a target conversation chain corresponding to each target consistency score in the plurality of enhanced conversation chains; and calculating a ticket selection number corresponding to each target dialogue chain, and outputting the target dialogue chain corresponding to the highest ticket selection number as an optimal dialogue chain. The problems that an existing multi-agent system mostly drives agents to execute tasks through a single control center, an independent expression and consensus decision-making mechanism of individual agents is lacked, and multi-round dialogue efficiency is low are solved.

The invention belongs to the technical field of agent safety control, and relates to a distributed cooperative safety control method applied to a multi-agent system, and the method comprises the steps: S1, building a kinematic model of an omnidirectional mobile agent, and generating a nominal speed instruction according to a preset track; s2, determining position information of each agent and broadcasting the position information to other agents; s3, determining a candidate zero-crossing obstacle function; s4, generating a security constraint by using the candidate zero-crossing obstacle function; s5, determining a loss function based on the nominal speed, and solving a control quantity which enables the loss function to be minimum on the premise of meeting security constraints as a security control instruction; and S6, controlling the intelligent body to move according to the safety control instruction. The method has the beneficial effects that in a large-scale agent cluster, even if global information and a central node are lacked, collision among the agents can be effectively avoided, meanwhile, falling into a local optimal solution is avoided, and it is ensured that the agents are safely navigated to a specified target point.

The invention discloses a multi-agent-based space moving target cooperative monitoring resource scheduling method, which establishes a problem scheduling mathematical model aiming at a space moving target tracking-oriented satellite-ground resource integrated scheduling problem, designs a satellite-ground cooperative multi-agent framework based on a multi-agent system, and provides a multi-agent-based space moving target cooperative monitoring resource scheduling method. The invention provides a satellite-ground cooperative scheduling strategy for space moving target tracking.

A query can be received from a user. The query can be sent to a plurality of automated agents to process the query. Results and associated confidence scores can be received from the plurality of automated agents. At least some of the results and associated confidence scores can be probed, based at least on a reason given for a result having the highest associated confidence score among the received results and associated confidence scores, to select an automated agent from the plurality of automated agents for answering the query. Information can be stored, where the information can include at least the results and associated confidence scores and a selected automated agent for answering the query, where at least one of the plurality of automated agents learns from the stored information to update its confidence score in answering the query.

The present invention provides a collaborative scheduling method for a multi-agent system, comprising the following steps: S1: Modeling a real-world problem into a problem scenario of a multi-agent system by means of graphical modeling and constructing a decision model; S2: Optimizing the weight values of each decision index in the decision model to obtain an optimized decision model; S3: Applying the optimized decision model to the problem scenario for collaborative scheduling simulation and recording the intermediate process of the collaborative scheduling simulation; S4: Generating a scheduling Gantt chart from the recorded intermediate process of the simulated scheduling as the scheduling chart of the multi-agent system. The present invention provides a collaborative scheduling method for a multi-agent system, which can make a more feasible and efficient decision by comprehensively considering various factors during the process of collaborative scheduling simulation, and solves the problem that in some multi-agent collaborative control scenarios at present, multiple agents may wait for each other at different stations, resulting in the inability to perform collaboration.

The invention discloses an agent contribution degree evaluation method and device of a multi-agent system, a storage medium, electronic equipment and a computer program product. The evaluation method comprises the steps that according to received environment state information, a graph neural network model corresponding to the multi-agent system is determined, and the environment state information at least comprises agent state information, task requirements and environment information; training an action network and a graph neural network model according to the environment state information; determining action information of nodes of the graph neural network model through the trained action network according to the environment state information; and determining the contribution degree of the intelligent agent through the trained graph neural network model according to the environment state information and the action information.

The invention relates to the technical field of computers, and discloses a fragmentation block chain federal learning method based on a large language model multi-agent, and the method comprises the steps: S1, initializing a client; s2, dynamic fragmentation scheduling and distribution; s3, generating and uploading local knowledge; s4, intelligent agent collaborative routing and knowledge acquisition; s5, knowledge fusion and model updating; and S6, repeatedly executing the steps S3 to S5 until the model converges or reaches a preset number of iterations. According to the invention, under a decentralized and fragmented federated learning architecture, the complex reasoning ability of a large language model and the autonomous cooperation mechanism of three multi-agent systems, namely a fragmented scheduling agent, a fragmented knowledge state agent and a global knowledge routing agent, are deeply fused; according to the mechanism, dynamic optimization of a bottom layer fragment structure and intelligent routing of high-value knowledge are achieved in an intelligent mode, and therefore the overall efficiency and model performance of a system under the condition of heterogeneous data and heterogeneous equipment are remarkably improved.