24 results about "Temporal logic" patented technology

Technology is described for determining a task plan that is usable by a robotic device in a workspace. The method can include converting instructions received for the robotic device into temporal logic (TL) statements and to a non-deterministic Buchi Automaton. A task probabilistic machine learning model can be generated with feasible task plans using the non-deterministic Buchi Automaton. A plurality of task plans can also be created or generated using the task probabilistic machine learning model. A sensor probabilistic machine learning model of the workspace can be constructed using information from sensors of the robotic device. The task plans from the task probabilistic machine learning model can be compared with the sensor probabilistic machine learning model to select the task plan with a high probability of correlation to the workspace.

The invention relates to a data processing technology, can be applied to business system platforms of medical health, financial science and technology and the like, and discloses an insurance policy auditing method, device and equipment and a medium, and the method comprises the steps: obtaining an insurance policy to be audited, carrying out the data extraction of the insurance policy to be audited, and generating structured insurance policy data; matching the structured insurance policy data with an auditing strategy in an auditing model library to obtain a matched auditing strategy; based on the auditing strategy, auditing the structured insurance policy data to generate an auditing result; and generating a corresponding risk level report based on a preset risk level rule and the audit result, and displaying the risk level report. The method can automatically extract the structured data of the insurance policy, thoroughly gets rid of the dependence of artificial experience, is different from single-dimensional forward verification, performs deep logic deduction from multiple angles, combines sequential logic verification, can accurately recognize hidden risks which are difficult to cover by a traditional system, and improves the auditing efficiency.

Various embodiments are directed to verifying outputs of large language models ("LLMs") using logical reasoning. Various embodiments can obtain raw data from a datastore which can be presumed as true. Various embodiments can then factorize the raw data into factorized data points using a large language model ("LLM"). The large language model can then be used to analyze, categorize, and summarize the factorized data points to generate an insight summary. Various embodiments can then validate the insight summary by identifying a chain of logic between the raw data and the insight summary, such that each statement can be proven using logical reasoning (e.g., deductive reasoning, temporal logic, syllogisms, etc.).

The invention relates to the technical field of industrial internet-of-things automatic control, and discloses an actuator internet-of-things multi-protocol adaptation system and method.The method comprises the steps that a protocol module constructs a state machine model for each protocol adapter, cross-protocol state collaboration and abnormal event release are achieved through tense logic rule verification, and a resource module receives abnormal events and sends the abnormal events to a server; the method comprises the following steps: dynamically updating a global resource constraint graph, analyzing an optimal resource reallocation scheme and an atomic task sequence, deconstructing the task sequence into a pi-calculation concurrent process network by applying a reconstruction module, recombining the process network and mapping the recombined process network into a cross-protocol instruction set to drive an executor, and finally, executing the Pi-calculation concurrent process network. Full-link adaptation from protocol layer state consistency guarantee and resource layer dynamic optimization scheduling to application layer process flexible reconstruction is realized, and the reliability and response efficiency of a multi-protocol actuator system in a complex industrial environment are improved.

This invention discloses a temporally enhanced knowledge graph-driven dialogue agent and its implementation method, belonging to the fields of artificial intelligence and natural language processing technology. The dialogue agent includes a dialogue interface, natural language understanding, a temporal knowledge graph, a temporal reasoning engine, dynamic update and failure management, and a natural language generation module. It includes designing the basic representation unit of the knowledge graph as a temporal quintuple; designing a temporal reasoning engine and a dynamic update and failure management method; designing a method for matching, verifying, and performing multi-hop reasoning of temporal information in natural language problems with temporal facts in the knowledge graph; achieving accurate parsing of temporal constraints in dialogue, real-time adaptation of dynamic knowledge, rigorous temporal logic reasoning, and continuous maintenance of contextual temporal consistency; and outputting a temporally accurate, knowledge-reliable, and logically coherent intelligent response, capable of meeting the needs of complex scenarios such as vertical industry decision support and high-reliability intelligent interaction.

The application belongs to the technical field of software dangerous behavior detection, and is a multi-level specification software dangerous behavior detection method, comprising the following steps: setting a positive and negative example set and a distance measurement function for quantifying the similarity degree of software behavior sequences, and dividing the positive example set into a plurality of positive example subsets; constructing a temporal logic specification weight tree as a triple consisting of a root node weight function, a left child formula depth weight function and a right child formula depth weight function; extracting positive examples from the positive example subsets to form a positive example reduced set; inquiring a large language model to obtain software dangerous behavior logic formulas and converting the software dangerous behavior logic formulas into equivalent finite state automata; sampling a negative example reduced set from the automata; inquiring the large language model based on the positive and negative example reduced sets multiple times to obtain formula fragments, dynamically updating the weight tree, and obtaining candidate formulas; and selecting the optimal candidate formula as a temporal logic specification to detect whether the software behavior sequence conforms to the temporal logic specification. The application alleviates the challenges of scarce dangerous behavior data and a too large dangerous behavior search space.

The temporal logic formula generation device 1X mainly includes a target relation logical formula generation means 331X and a target relation logical formula integration means 332X. The target relation logical formula generation means 331X is configured to generate, based on object-to-object relation information representing a relation between objects in a target state relating to a task of the robot, one or more target relation logical formulas that are temporal logic formulas representing relations, in the target state, between respective pair(s) of objects between which the relation is defined. The target relation logical formula integration means 332X is configured to generate a temporal logic formula into which the target relation logical formulas are integrated.

Technology is described for determining a task plan that is usable by a robotic device in a workspace. The method can include converting instructions received for the robotic device into temporal logic (TL) statements and to a non-deterministic Buchi Automaton. A task probabilistic machine learning model can be generated with feasible task plans using the non-deterministic Buchi Automaton. A plurality of task plans can also be created or generated using the task probabilistic machine learning model. A sensor probabilistic machine learning model of the workspace can be constructed using information from sensors of the robotic device. The task plans from the task probabilistic machine learning model can be compared with the sensor probabilistic machine learning model to select the task plan with a high probability of correlation to the workspace.

A qualitative and quantitative (Q2) method for verifying safety of movement of complex learning-enabled cyber-physical systems (Le-CPS) using a computer is provided. The method includes determining an initial set of states of the Le-CPS. The computer is used for determining a reachable set of the Le-CPS using a probstar reachability algorithm saved in memory of the computer. The computer includes a Q2 algorithm saved in the memory that is configured is used to simultaneously check (i) if the reachable set satisfies a predetermined safety constraint (qualitative verification) and (ii) determine a probability of satisfaction that the reachable set will satisfy the predetermined safety constraint (quantitative verification). Movement of the Le-CPS is planned based on the step of using the computer including the Q2 algorithm.

A computer-implemented method of discovering a composite durational event structure through temporal logic includes identifying a plurality of temporally related atomic events from temporal data trajectories of a multivariate dataset according to a definition of an atomic event predicate. At least one composite event having a durational event structure of at least some of the plurality of the temporally related atomic events is discovered by machine learning. An action is performed that is selected from a predetermined list associated with the composite event.

This invention discloses a method and system for planning multi-aircraft flight test subjects considering meteorological window constraints, belonging to the field of aircraft flight test management technology. The method includes: constructing a multi-aircraft flight test mission planning model incorporating immediate logic constraints, resource exclusivity constraints, and meteorological time window constraints; solving the model using an improved gold mining optimization algorithm (I-GRO), representing the joint decision of "mission priority-aircraft allocation" through a two-layer hybrid encoding strategy; introducing a dynamic topology sorting and meteorological window alignment mechanism in the decoding stage to map continuous position vectors into feasible scheduling schemes that satisfy temporal logic; and in the search stage, adaptively adjusting the execution probabilities of migration, gold mining, and cooperation operators through a dynamic strategy selection mechanism, and introducing a linearly decaying elite cooperation and perturbation mechanism. This invention can effectively solve the search stagnation problem under fragmented feasible domains, significantly shorten the total flight test duration, and improve the robustness of the planning scheme.

The present invention discloses a behavior tree-based autonomous driving test method, apparatus, storage medium, and equipment, relating to the field of autonomous driving. The method comprises: obtaining a test map, constructing a test scenario based on the test map and selecting a dynamic target, obtaining an atomic action set of the dynamic target and the behavior trajectory of the dynamic target in each test sub-scenario included in the test scenario; based on the atomic action set, decomposing the behavior trajectory into at least one atomic action, and determining the temporal logic relationship of the atomic actions; combining the temporal logic relationship in each of the test sub-scenario, and creating a behavior tree corresponding to the test scenario based on the combination result; running the behavior tree to obtain the autonomous driving test results of the object under test. The present invention realizes autonomous driving testing in non-solidified and more diverse test scenarios.

Technology is described for determining a task plan that is usable by a robotic device in a workspace. The method can include converting instructions received for the robotic device into temporal logic (TL) statements and to a non-deterministic Buchi Automaton. A task probabilistic machine learning model can be generated with feasible task plans using the non-deterministic Buchi Automaton. A plurality of task plans can also be created or generated using the task probabilistic machine learning model. A sensor probabilistic machine learning model of the workspace can be constructed using information from sensors of the robotic device. The task plans from the task probabilistic machine learning model can be compared with the sensor probabilistic machine learning model to select the task plan with a high probability of correlation to the workspace.

In this command conversion system, a temporal logic analysis means divides an outline command, which is a command represented by temporal logic and expressing an outline of states up to a target state in which a robot is operated, and generates a temporal logic sequence, which is a set of components of the temporal logic. On the basis of a predefined correspondence between the components of the temporal logic and generation character strings (e.g., text) which are character strings used to generate the arrangement of target objects, a character string conversion means converts each component of the temporal logic sequence into a generation character string according to said correspondence, and combines the generation character strings to generate a character string representing the content of the outline command.

This model verification device comprises: an acquisition unit that acquires a state transition model and a temporal logic formula; a conversion unit that converts the temporal logic formula into constraint information encoded with variable-length time intervals on the basis of a mixed integer linear programming method; and a result output unit that outputs information indicating whether the state transition model satisfies the temporal logic formula on the basis of the state transition model and the constraint information.

The application discloses a kind of fusion multi-dimensional perception and physical information's intelligent calculation center resource regulation system and method, belong to intelligent calculation center resource management technical field.System is unified by multi-dimensional perception module Resource task state view is constructed.Intelligent decision module is based on the view driving multi-agent reinforcement learning model to generate collaborative strategy;Its collaborative mechanism is based on generalized reactive temporal logic formalization, by the team strategy of synthesis is decomposed into synchronous reward automaton, to guide multi-agent distributed collaboration.Execution module is linked based on the fine energy efficiency control instruction of physical information neural network, realize resource global optimization.Digital twin module is responsible for strategy verification and model iteration.The application solves the problem of cross-domain collaborative temporal logic security guarantee and physical level fine energy efficiency optimization, significantly improves the autonomy, reliability and energy efficiency of system.

A computer-implemented method for discovering a structure of composite duration events through temporal logic includes identifying a plurality of temporally related atomic events from a duration data trajectory of a multivariate dataset according to a definition of an atomic event predicate. At least one composite event having a duration event structure of at least some of the plurality of temporally related atomic events is discovered by machine learning. An action selected from a predefined list associated with the composite event is executed.

The invention discloses a complex task-oriented multi-level electroencephalogram decoding and dynamic instruction generation method, which comprises the following steps of S1, strategic layer intention recognition: analyzing a task characteristic wave, namely a mu / beta rhythm, in an electroencephalogram signal of a user during imagination, and recognizing a target instruction of the user; s2, tactical layer task decomposition: performing action decomposition on the target instruction identified in the S1, automatically planning execution steps, and matching an optimal execution path by using a'task knowledge base '. According to the method, three-layer assembly line decoding of'strategic layer-tactical layer-action layer 'is adopted, multi-level electroencephalogram decoding and dynamic instruction generation oriented to complex tasks based on electroencephalogram signals are achieved, the complex brain control tasks are converted into natural interaction of'once desired', and the problems of intention expression overload and sequential logic disorder in the prior art can be effectively solved.

The invention provides a counter-example analysis and visualization method based on a CTL sequential logic formula, a medium and equipment, and belongs to the field of software engineering. According to the method, only counter-examples and CTL sequential logic formulas are concerned, the reason of a first error is analyzed in a centralized mode, the concept of the causal relationship is applied to search for the reason causing the first failure of the attribute, and a causal relationship algorithm is designed accordingly; and presenting the counter-example by using three visualization means of a variable table, an attribute view and an attribute formula tree, and meanwhile, displaying failure reason information in a corresponding visualization view according to a reason set provided by a causal relationship algorithm. The method is independent of a counter-example generation tool and the model itself, can be applied to any model inspection tool as a lightweight outer layer, and helps a user to better understand the cause of an error.

An analysis apparatus includes an inputter configured to receive an input of a first temporal logic formula and an input of a second temporal logic formula, and an outputter configured to output a screen for displaying a first block diagram showing the first temporal logic formula and a second block diagram showing the second temporal logic formula in a comparable manner.

The invention belongs to a software dangerous behavior detection technology, and relates to a multilevel protocol software dangerous behavior detection method, which comprises the following steps of: setting a positive and negative example set and a distance measurement function for quantifying the similarity degree of software behavior sequences, and dividing the positive example set into positive example subsets; constructing a tense logic specification weight tree which is a triple composed of a root node weight function and left and right child formula depth weight functions; extracting positive examples from the positive example subsets to form a positive example simplified set; inquiring the large language model to obtain a software dangerous behavior logic formula, and converting the software dangerous behavior logic formula into an equivalent finite state automaton; sampling a counter-example simplified set from the automaton; inquiring the large language model for multiple times based on a positive and negative example simplified set to obtain a formula fragment so as to dynamically update the weight tree and obtain a candidate formula; and selecting the optimal candidate formula as a tense logic specification, and detecting whether the software behavior sequence conforms to the tense logic specification or not. According to the method, the challenges of scarcity of dangerous behavior data and overlarge dangerous behavior search space are relieved.

The invention relates to the technical field of knowledge maps, and discloses an event prediction method and device based on a time knowledge map and a large language model, and the method comprises the steps: carrying out the migration in a directed graph of the time knowledge map through employing a transition distribution formula of time random migration, and generating a path with time information; analyzing the path with the time information to obtain a time pattern of which the occurrence frequency is greater than or equal to a preset time threshold value, converting the time pattern into a time logic rule, and storing the time logic rule to obtain a time logic rule base; and selecting a time logic rule from the time logic rule base according to the current occurrence event to generate a large language model generation prompt template, and generating the prompt template according to the large language model generation prompt template to predict an event possibly occurring in the future. When the method is applied to time knowledge maps with complex data scales such as financial insurance and intelligent diagnosis and treatment, the cross-domain and cross-time generalization ability of the time knowledge maps of the type can be improved.

The information processing device 1A mainly includes a logical formula conversion unit 322A, a constraint condition information acquisition unit 323A, and a constraint condition addition unit 324A. The logical formula conversion unit 322A is configured to convert an objective task, which is a task to be performed by a robot, into a logical formula that is based on a temporal logic. The constraint condition information acquisition unit 323A is configured to acquire constraint condition information I2 indicative of a constraint condition to be satisfied in performing the objective task. The constraint condition addition unit 324A is configured to generate a target logical formula Ltag that is a logical formula obtained by adding a proposition indicative of the constraint condition to the logical formula generated by the logical formula conversion unit 322A.

In various examples, systems and methods are disclosed related to facilitating management of evaluator logic. In particular, evaluator logic is generated in association with a requirement in an effective and efficient manner. To efficiently generate evaluator logic, artificial intelligence (AI) technology may be used to perform various aspects of the evaluator logic generation. In particular, a logical formula that represents the requirement may be generated using a temporal logic. The logical formula may then be used to generate, via one or more machine learning models, an evaluator logic in an executable format. In accordance with efficiently generating evaluator logic, the evaluator logic may be implemented to evaluate a product, a system, or other technology.