207 results about "Autonomous agent" patented technology

Systems and methods disclosed herein perform privacy-preserving evaluations of artificial intelligence (AI) agents. A first AI agent associated with a first entity obtains a machine-readable data structure defining one or more operative boundaries for a second AI agent associated with a second entity. The system generates a unique fixed reference value representing the machine-readable data structure by applying a first transformation operation set, and transmits the unique fixed reference value to a multi-agent storage to store the value. The system receives, via the multi-agent storage, a verification artifact from the second AI agent that indicates an observed value based on internal operational data of the second AI agent corresponding to the operative boundaries. The first AI agent determines a verification status of the verification artifact by comparing the unique fixed reference value with the observed value, and autonomously generates a verification record including a representation of the verification status.

Systems, methods, and devices that relate to monitoring and managing autonomous agents are disclosed. In one example aspect, the method includes receiving activity data from autonomous agents in an operational environment, deploying static and dynamic observing agents to monitor expected behavior and deviations, detecting a deviation by an autonomous agent, determining the cause through analysis, performing a mitigative action based on the cause, and executing a preventative action to block similar future deviations. The method may also involve configuring observing agents with different observation modalities, periodically modifying observation parameters unpredictably, facilitating direct communication between observing agents, resolving conflicts in observations, and updating observation policies. Mitigative actions can include disabling credentials, rerouting communications, and logging actions. Preventative measures may involve updating behavioral policies and adjusting agent parameters to disincentivize problematic behaviors.

Systems, methods, and computer-readable media for autonomous agents are disclosed. The autonomous agent can include one or more processing circuits configured to receive or identify a dynamic data structure comprising one or more functions or one or more frameworks for performing a plurality of cyber resilience operations. The processing circuits can register the autonomous agent with a decentralized network, centralized network, or data source (DNCNDS). The processing circuits can receive or identify, from at least one computing system external or internal to the DNCNDS, cyber resilience data. The processing circuits can perform, in real-time, the at least one cyber resilience operation of the plurality of cyber resilience operations based at least on selecting at least one function of the one or more functions of the dynamic data structure based at least on the cyber resilience data.

An agent management platform for providing a runtime environment for the execution of agents can be used to manage process deployments. The agents can be configured to perform specific tasks on software applications within defined objectives. Using an obtained execution request, the agent management platform can associate agents with one or more nodes by comparing node constraints against agent constraints. The nodes can include computers equipped with containers that provide isolated runtime environments for agent execution. The agent management platform can instantiate agents in corresponding containers on the computers of associated nodes, and / or execute the instantiated agents to perform corresponding tasks.

The invention discloses an intelligent traffic control system and method based on multi-agent near-end strategy optimization, and belongs to the field of intelligent traffic, Internet of Vehicles and deep reinforcement learning. The method comprises the following steps: firstly, constructing a fog-cloud collaborative three-layer architecture, and realizing real-time monitoring and dynamic regulation and control of traffic flow through cloud global decision and local sensing collaboration of a road side unit (RSU); secondly, designing indexes of'road section overlap ratio 'and'road section time overlap ratio', and solving the problem of secondary congestion caused by rerouting; then, a multi-agent near-end strategy optimization (MAPPO) algorithm is adopted, so that the traffic signal lamp is used as an autonomous agent to dynamically adjust the phase, and the limitation of single-point control is broken; and finally, through integrated optimization of rerouting and adaptive signal control, an original multi-objective optimization problem is converted into a layered multi-agent reinforcement learning problem. According to the invention, vehicle driving time and system energy consumption can be effectively reduced, road traffic efficiency is improved, and active avoidance and dynamic alleviation of urban traffic congestion are realized.

A system for context-sensitive orchestration of autonomous agents in cloud platforms, consisting of: a hardware-based orchestration device configured for integration into a distributed cloud infrastructure; a context inference engine within the orchestration device, wherein the context inference engine is configured to receive and aggregate real-time telemetry data from a variety of distributed nodes, including at least one system-level parameter, at least one application-level parameter, and at least one environment parameter; a semantic inference module within the context inference engine, configured to generate a context-related state representation by correlating the parameters using a knowledge graph-based model of interdependencies; an optimization unit for machine learning within the orchestration device, which is communicatively connected to the context inference engine and is configured to predict resource requirements and operational states using reinforcement learning models trained on historical and real-time data streams; a policy-driven orchestration controller configured to translate the contextual state representation into actionable orchestration decisions by applying dynamic orchestration policies stored in a domain-specific policy repository; and a distributed agent interaction bus configured to delegate orchestration decisions to a variety of autonomous agents deployed on the cloud platform.

Systems and methods for resolving code vulnerabilities through collaborative agents which may include accessing a code base of an identified vulnerability; configuring a plurality of autonomous agents, each comprising a predefined agent role associated with application security remediation process; executing a directed workflow of the plurality of agents, wherein the workflow is a conditional sequence of agent-driven processing steps for generating a proposed resolution to the identified vulnerability; and outputting a candidate resolution for the vulnerability based on results produced by the workflow.

A system for real-time creditworthiness assessment of small and medium-sized enterprises (SMEs) based on agent-based artificial intelligence (AI), the system includes: a large number of autonomous agents connected to each other via secure communication protocols; a data ingestion processing unit configured to capture and preprocess heterogeneous data streams from structured and unstructured sources, including APIs for financial transactions, tax databases, e-invoicing registers, inventory management systems, social sentiment feeds, and IoT-enabled devices, with preprocessing including schema harmonization, anomaly detection, and encryption-based integrity preservation; an ensemble of scoring control units configured to generate dynamic credit scores, wherein the ensemble includes machine learning models, including gradient-enhanced decision trees, recurrent neural networks, and graph neural networks, the graph neural networks being configured to assess relational dependencies between SMEs, suppliers, and customer nodes in a business ecosystem; an audit controller configured to generate regulatory-compliant justification paths for each credit score; a feedback processing unit configured to update the evaluation control unit ensemble using reinforcement learning signals derived from observed repayment behavior, payment default trends, and fraud detection markers; and a user interface configured to display timestamped credit histories, confidence intervals and explanatory justifications, with the entire system running on a containerized cloud-native infrastructure with blockchain-based ledgers for version control of each point update.

Disclosed is decentralized transaction system comprising computing nodes to implement platform, computing node(s) is configured to: receive, from first user device (FUD), first request for first trade (FT), first smart contract (FSC) to lock funds for FT; receive, from second user device, second request for second trade (ST), second smart contract (SSC) to lock trade subject (TS) for ST; send, to agent database (AD), first notification, for first autonomous agent (FAA) associated with first user (FU); second notification, for second autonomous agent (SAA) associated with second user (SU); create communication interface, FAA configured to send first set of trade parameters (FSOTP), first digital signature (FDS) to SAA, facilitate agent-based trade match, SAA configured to perform trade match, send FDS, FSOTP and second set of trade parameters (SSOTP) to SSC; employ processing interface for enabling order book-based trade verification, SSC to execute transfer of funds and trade subject to SU and FU, respectively.

The present disclosure provides systems and methods performed in a central system. An initial entity model comprising a plurality of artefacts is formed. A runtime engine determines a plurality of available agent engagements based on the initial entity model. Responsive to an input selecting one of the available agent engagements, a notification is sent to an external autonomous agent. An indication of an action to be performed is received from the external autonomous agent. Responsive thereto, a message is sent to an external service indicating the action, thereby causing the action to be performed. The entity model is updated with a new artefact corresponding to the performed action, and a further plurality of agent actions is determined based on the updated entity model.

A symbolic neuroadaptive control system is disclosed for real-time arbitration, consent, and ethical modulation of artificial intelligence agents operating in wearable computing environments. The system integrates multimodal biometric telemetry—including high-resolution EEG signals—with a symbolic kernel that performs logic-driven arbitration over cognitive, emotional, and ethical states. Using Coq-verified invariants and zero-knowledge biometric consent tokens, the system constructs a deterministic symbolic execution graph, gating AI outputs based on internal user states such as trauma, stress, or intentionality. Unlike conventional black-box BCI models, the invention routes EEG-inferred affective-symbolic tokens through a formal ethics layer that enforces real-time interrupt control, utility bounding, and trust verification. The kernel enables AGI systems to defer or modify behavior based on user-state alignment, granting sovereign agency over all downstream actions. This neuro-symbolic architecture redefines the interface between human cognition and intelligent machines, enabling emotionally conscious, morally verifiable, and symbolically transparent AI governance in dynamic, high-stakes contexts.The present invention relates to artificial intelligence and neurotechnology, specifically to a real-time, neuro-symbolic operating system kernel that converts electroencephalography (EEG) signals into structured symbolic data for use in emotional cognition, ethical prioritization, autonomous agent dispatch, and real-time telecommunications routing. The invention bridges brain-computer interface (BCI) inputs with symbolic AI architectures to enable ethically aligned machine response during cognitively or emotionally intense events.

Systems and methods disclosed herein perform privacy-preserving evaluations of artificial intelligence (AI) agents. The system identifies an auditing AI agent from a set of auditing AI agents for assessing target AI agent sets. The system obtains a data structure that defines operative boundaries for a target AI agent set and generates a reference value by applying a first transformation operation set on the data structure. The system transmits the reference value to a multi-agent storage and receives, via the multi-agent storage, a verification artifact from the target AI agent set that indicates an observed value generated by applying a second transformation operation set on an artifact set generated by the target AI agent set. The system determines, via the auditing AI agent, a verification status and responsive to a particular artifact failing to satisfy one or more assessment metrics, generates an action set to modify the target AI agent set.

Systems, methods, and devices for monitoring and controlling communications between autonomous agents are disclosed. The system monitors real-time communications between autonomous agents, intercepting and recording each communication. Communications are translated to a standardized language and processed through communication protocol filters that evaluate compliance with predefined operational policies. The system parses each translated communication to identify policy violations. When violations are detected, the system modifies communications to ensure compliance with operational policies. All communications and modifications are recorded via distributed ledger technology for audit and accountability purposes. This approach enables comprehensive oversight of autonomous agent interactions while maintaining tamper-proof records of all monitoring and control activities.

An autonomous communication system includes a language model, a retrieval module, a caching mechanism, an autonomous agent, and a human interface for managing interactions and responses. A method and computer-readable medium for managing communication also include these components for processing, enhancing, summarizing, managing interactions, and allowing human intervention.

A hybrid, redundant, fail-safe architecture provides a unified fail-operational framework for autonomous agents operating across physical and virtual domains. The system employs a multi-modal data source suite, an adaptive hybrid data fusion module, and an intelligent decision-making module. A novel closed-loop interaction enables a health monitoring module that detects an incipient fault in a data source by monitoring ancillary performance metrics. Upon detection, the module generates a fault signature, including a quantitative prognostic estimate of a future failure time, and transmits it to an adaptive data fusion module. The fusion module proactively reconfigures its state estimation algorithm by decreasing reliance on the degrading data source in proportion to the prognostic estimate. This preemptive compensation ensures the system maintains a high-integrity environmental model and achieves true fail-operational continuity. The architecture is applicable to numerous embodiments providing a universal solution for proactive fault management and system resilience.

A system and devices for automating and computerizing audits, tracking, and error prevention associated with container packing events and, in particular, multi-container packing events at a storage facility, yard, warehouse, or the like. In some cases, the systems and devices may be configured to monitor and detect errors or issues with packing items and send alerts and control signals to agents, such as autonomous agents, to correct the detected errors.

Systems and methods disclosed herein perform privacy-preserving evaluations of artificial intelligence (AI) agents. A first AI agent associated with a first entity obtains a machine-readable data structure defining one or more operative boundaries for a second AI agent associated with a second entity. The system generates a unique fixed reference value representing the machine-readable data structure by applying a first transformation operation set, and transmits the unique fixed reference value to a multi-agent storage to store the value. The system receives, via the multi-agent storage, a verification artifact from the second AI agent that indicates an observed value based on internal operational data of the second AI agent corresponding to the operative boundaries. The first AI agent determines a verification status of the verification artifact by comparing the unique fixed reference value with the observed value, and autonomously generates a verification record including a representation of the verification status.

A system and method includes an autonomous agent having a communication interface that enables the autonomous agent to communicate with a plurality of infrastructure sensing devices; a plurality of distinct health monitors that monitor distinct operational aspects of the autonomous agent; an autonomous state machine that computes a plurality of allowed operating states of the autonomous agent based on inputs from the plurality of distinct health monitors; a plurality of distinct autonomous controllers that generate a plurality of distinct autonomous control instructions; and an arbiter of autonomous control instructions that: collects, as a first input, the plurality of autonomous control instructions generated by each of the plurality of distinct autonomous controllers; collects, as a second input, data relating to the plurality of allowed operating state of the autonomous agent; and selectively enables only a subset of the autonomous control instructions to pass to driving components of the autonomous agent.

The invention discloses a multi-robot cooperative measurement method and system based on crowd intelligence emergence, and aims to solve the problems of single-point fault, poor environmental adaptability and insufficient expandability in the existing centralized and pre-planned multi-robot cooperative measurement method. According to the method, a shared digital environment cooperatively and jointly maintained by all robots through decentration is constructed, a decentration task decision-making mechanism based on digital pheromones is introduced, each measurement robot is used as an autonomous agent, and the concentration of the digital pheromones dynamically determined by the quality of measurement data in the shared digital environment is sensed, so that the concentration of the digital pheromones in the shared digital environment is determined. Measurement target decisions are made independently. According to the method, a robot cluster for multi-robot cooperative measurement can spontaneously emerge a globally optimal cooperative measurement behavior without a central control node, so that the robustness, the adaptability and the expandability of a measurement system are remarkably improved, and efficient and reliable three-dimensional measurement of a large component is realized.

Embodiments of the disclosure provide a machine learning framework to control an autonomous agent in a dynamic environment. A system according to the disclosure includes a sensor coupled to the autonomous agent to receive a set of inputs. At least one actuator causes the autonomous agent to perform an action. A controller causes the actuator to perform an action based on the inputs and an operative policy. The controller determines whether the operative policy is terminated, based on the set of inputs. Upon terminating the operative policy, the controller evaluates several candidate policies and selects one of the candidate policies as a new operative policy.

The invention relates to an autonomous agent-oriented large language model element cognitive correction self-updating reward method, which comprises the following steps of: firstly, automatically generating an initial reward function by utilizing a large language model (LLM) according to high-level task description by using the AURA; then, meta-cognition on calculation is realized through a double-loop architecture: a rapid internal loop carries out collaborative evolution of strategies and rewards through a layered attention reward synthesizer; a slower outer cycle serves as a meta-cognitive layer, and a multi-dimensional diagnosis vector composed of strategy dynamics, value-authenticity alignment and reward architecture adaptability is continuously monitored so as to recognize anomalies in the learning process. The closed-loop feedback mechanism endows the autonomous agent with the capability of carrying out introspection and self-correction on the award function of the autonomous agent, and normal form transformation from static target specification to dynamic target correction is formed. And the robustness, the sample efficiency and the final performance of the intelligent agent in the face of sparse rewards, protocol vulnerabilities and environment uncertainty are enhanced.

The subject matter described herein discloses apparatus, systems, techniques, and articles for an MEIA (Modular, Extensible, Interoperable Autonomy) architecture that enables a vehicle or autonomous agent with minimal human guidance to perform solo and collaborative missions with other vehicles. The MEIA architecture includes four layers—Awareness, Strategy, Tactics, and Execution. The Execution Layer bookends a single loop through the layers by observing and acting. The Awareness Layer orients, and the Strategy Layer and Tactics Layer split the responsibility of deciding.

Techniques for latent belief space planning include: during execution of an autonomous agent configured to control operation of a physical mechanism, obtaining a current observation of a physical environment; based at least on the current observation of the physical environment, generating a trajectory tree that represents possible trajectories in a belief space, wherein nodes of the trajectory tree represent values of a continuous observation, a continuous state, and a continuous control, each node being associated with one of multiple timesteps along the plurality of possible trajectories, and wherein branches from inner nodes to child nodes correspond to possible outcomes and observations of a multi-modal latent state; determining a current value of the continuous control associated with a current node; and applying the current value of the continuous control to operation of the physical mechanism.

A system and method for performing autonomous testing of software applications is provided. The system is comprised of an autonomous testing agent; a generalized representation of components, actions, rules, and flows that can be dynamically mapped to application-specific instances; a work queue for items to be tested; a dynamically-constructed model of the application under test; and a log of testing actions and results. The autonomous agent navigates the application under test, discovers interface components and actions, places components and actions in the work queue and / or adds them to the model, executes actions against interface components, applies rules to verify that expected behaviors were exhibited, and logs testing actions and results. The system provides an objective measure of testing by aggregating testing actions and results relative to the application model.

A computer-implemented, machine learning method for guided programming of autonomous agents using a meta agent includes generating a first task that includes a start tag, script agents involved in the first task, and a timer associated with the first task or the script agents. A prompt is generated for each script agent of the script agents associated with the first task that includes the timer, wherein each script agent is configured to generate a script that corresponds to the prompt and includes an end tag for executing a second task associated with the prompt. An action is determined based on the script agents returning a response to the prompt or upon expiration of the timer. The method has applications including, but not limited to, use cases in medical Al / healthcare, cybersecurity, city planning, safer cities, industry 4.0 and material acquisition for optimization of actions or to support decision-making.

A computing services environment may include application servers providing computing services including access to a database system, a unified metadata framework including autonomous agent definitions referencing action definitions defining a plurality of actions capable of being performed within the computing services environment, an agent service configured to instantiate an autonomous agent instance based on an autonomous agent definition, and an orchestration layer configured to determine an orchestration plan based on novel planning text generated by a generative language model. The orchestration plan may include a subset of the plurality of actions identified in the novel planning text. The computing services environment may execute the subset of the plurality of actions within the computing services environment.

A method for risk-aware policy assessment for an autonomous vehicle can include: collecting information associated with an environment of an ego vehicle; determining a set of policy proposals; determining and assessing a set of risks encounterable (e.g., potentially encountered in the future) by the ego vehicle; selecting a policy based on the set of risks, operating the ego vehicle based on the assessed risks, and / or any other suitable elements. Additionally or alternatively, the method can include any or all of: performing a set of simulations, analyzing the simulation results, determining a set of discount profiles, discounting a set of risks, and / or any other processes. The method can be performed with a system as described below and / or any other suitable system.

A method of training an autonomous agent is provided, the method comprising: providing videogame data generated by a human playing a videogame as input data to a training network for training an autonomous agent for playing the videogame; generating videogame data of the trained autonomous agent playing the videogame; providing the videogame data of the trained autonomous agent playing the videogame data to a discriminator of a generative adversarial network ‘GAN’, the discriminator of the GAN being trained to distinguish videogame data of a human playing the videogame and videogame data of an autonomous agent playing the videogame; generating a classification, by the discriminator of the GAN, of the output videogame data of the trained autonomous agent playing the videogame as human or agent; and updating at least one of the training network and the discriminator of the GAN based on the classification generated by the discriminator of the GAN.

A computing services environment may include application servers providing computing services including access to a database system, a unified metadata framework including autonomous agent definitions referencing action definitions defining a plurality of actions capable of being performed within the computing services environment, an agent service configured to instantiate an autonomous agent instance based on an autonomous agent definition, and an orchestration layer configured to determine an orchestration plan based on novel planning text generated by a generative language model. The orchestration plan may include a subset of the plurality of actions identified in the novel planning text. The computing services environment may execute the subset of the plurality of actions within the computing services environment.