34 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.

Disclosed in the present application are a question answering method and apparatus based on a temporal knowledge graph. The method comprises: acquiring a target question; determining a plurality of first entities in the target question, and determining first semantic role information in the target question, wherein the first semantic role information at least comprises a subject, a predicate, an object and time information; determining from a target temporal knowledge graph a plurality of pieces of first knowledge associated with the plurality of first entities, and determining a first entity relationship between second entities in each piece of first knowledge; determining a piece of first knowledge among the plurality of pieces of first knowledge as target knowledge, which piece of first knowledge corresponds to the first entity relationship matching the first semantic role information; and performing temporal logic reasoning on the basis of the time information and the target knowledge, so as to obtain an answer to the target question.

The invention relates to the technical field of time sequence knowledge maps, and discloses a narrative-driven time sequence knowledge map reasoning method, which comprises the following steps of: initializing basic data and a narrative-driven prompt template; key historical events related to query are screened from the time sequence knowledge graph; a retrieval strategy based on a time logic rule is utilized, the time logic rule of the time sequence knowledge graph is mined, a rule base is formed, and historical events most relevant to given query in time and logic are retrieved; quantizing the temporal proximity of the historical events; a narrative story with clear time sequence and rigorous logic is generated; performing fine adjustment on the narrative stories based on a sequential reasoning large language model; and predicting to obtain a long-tail entity by using historical events and the narrative stories. According to the method, the generalization ability is enhanced, the problem of data sparseness is effectively solved, accurate reasoning on the entities out of distribution is achieved, the wide application potential is achieved, a reasoning scheme for long-tail entities and complex events is provided, and the method is suitable for construction and prediction of the knowledge graph in the dynamic data environment.

The invention relates to a digital textbook knowledge explanation method based on a time sequence knowledge graph, which comprises the following steps: receiving an explanation request from a user side, and associating the explanation request with a specific teaching content unit in a pre-constructed time sequence knowledge graph by performing intention analysis on the explanation request; based on the current learning progress of the user and a target teaching content unit corresponding to the explanation request, extracting a knowledge explanation constraint sub-graph conforming to the sequential logic from the sequential knowledge graph; and based on the knowledge explanation constraint subgraph, generating explanation content conforming to the sequential logic, and outputting the explanation content to a user side. The explanation content is strictly constrained to the current learning progress of the student, concept outlines are avoided, and the explanation accuracy and acceptability are improved; a complex problem solving method is systematically decomposed and is clearly explained according to a teaching sequence, so that the understanding quality is improved; the confusion of students caused by outline exceeding or complex steps is reduced, and the learning efficiency and effect are improved.

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.

The invention relates to a fuzzy traffic rule optimization method based on sequential logic, and the method comprises the following steps: sorting and classifying natural language traffic rules related to a target scene, and carrying out the classification and induction of the rules according to the state, behavior and sequential relation of an interactive vehicle; adopting sequential logic to digitalize the natural language traffic rule, and identifying a fuzzy proposition; key parameters of the fuzzy proposition are supplemented from three dimensions of behavior, time and space in combination with empirical parameters and existing research; building a simulation test platform; the parameter range of the fuzzy proposition is determined in combination with statistical analysis and machine learning methods, optimization is carried out through a genetic algorithm according to the simulation result of the simulation test platform, the optimal parameter of the fuzzy proposition is obtained, and traffic rule optimization is achieved. Compared with the prior art, the method has the advantages that the adaptability, interpretability and execution efficiency of the fuzzy traffic rule in an automatic driving scene are remarkably improved, and the scientificity of rule optimization and the safety of an automatic driving system are enhanced.

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.).

To provide an autonomous testing device for generating an object description described in a time-phase logic expression, and autonomously performing a test using the generated object description related to autonomous performance when a test object is changed in model checking.SOLUTION: An autonomous testing device 100 for executing model testing is provided with a test execution determining unit 90 and a time-phase logic expression generating unit 50. The test execution determining unit 90 determines whether or not to execute a test for a test target by using a test execution status for a target function and product update information that is information indicating the version of the target function. The time-phase logic expression generating unit 50 generates the object description of a time-phase logic expression by using multiplicity information indicating the multiplicity of each stereotype corresponding to each product included in the test object, metamodel information indicating the relationship between each operand name and each stereotype, and pattern information indicating the relationship between a template of the time-phase logic expression and a description rule of the object of the time-phase logic expression.SELECTED DRAWING: Figure 1

The invention discloses a monitoring data quality control method based on deep learning and a self-built rule. According to the method, the reliability and the practicability of the natural resource monitoring data are remarkably improved by combining flexible rule configuration and an advanced time sequence analysis technology. Firstly, the method supports a user to self-define multi-dimensional quality inspection rules according to actual scene requirements, the multi-dimensional quality inspection rules comprise abnormal values, threshold values, sudden change values, time continuity, logicality and the like, and the limitation of traditional fixed rules is broken through. By introducing time logic check, the system can effectively identify abnormalities caused by seasonal or periodic changes in the data, such as reasonable fluctuation ranges of vegetation indexes in different seasons, thereby more accurately capturing time features of the data. Besides, the multi-device consistency comparison function realizes cross verification of cross-device data, device faults or local environment anomalies can be found in time, and logic consistency and overall credibility of multi-source data are ensured.

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 present invention discloses a general complex network attack inference method based on the Bayesian ATT&CK network, which relates to the field of network security technology. Using the kill chain and empirical knowledge as the research basis, it conducts a temporal logic modeling of the tactics in the ATT&CK framework to ensure that the generated attack sequences are logically coherent and reflect the behavior patterns of real complex network attacks; based on the tactical temporal logic sequence, it extends the guidance of expert knowledge on tactical transfer to the guidance of technique transfer, and constructs a technique dependency network; it uses an anonymized dataset from real network attack events and further optimizes the technique dependency network by utilizing the co-occurrence relationship of techniques in the dataset; based on the technique dependency network and the co-occurrence relationship of techniques in the real network attack dataset, the Bayesian ATT&CK network performs structure learning and parameter learning to construct a network model independent of specific threat detection systems, realizing the inference of complex network attacks independent of specific systems.

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.

The present invention discloses a multi-robot assembly line method and system based on a temporal logic control strategy, comprising: expressing the robot's task specification based on a control strategy synthesized by temporal logic of a parity check game, constructing a reward automaton with a potential energy function according to the acceptance condition of the synthesized strategy to assign a reward value to the robot's behavior; decomposing the comprehensive strategy of a robot group consisting of a generalized reactive specification of rank 1 into a reward automaton for each robot, and expanding the reward automaton with potential energy on the MDP; proposing a reward shaping algorithm based on value iteration and a distributed Q-learning algorithm to improve the speed at which the robot group learns the optimal strategy; the present invention captures the temporal attributes of the task based on temporal logic, decomposes the generated comprehensive strategy into multiple individual reward automata to guide the robots to learn the optimal strategy, and proposes a reward shaping algorithm based on value iteration to improve the efficiency of the robot group in learning the optimal assembly strategy and avoid falling into the problem of falling into local optimality.

A computer-implemented system / framework is described for performing deduction using generalized annotated logic that captures many of the desired capabilities seen in various neuro symbolic frameworks including fuzzy, open world, temporal, and graph-based reasoning. Specifically, the system includes a core capability to reason about first order (FOL) and propositional logic statements that can be annotated with either elements of a lattice structure or functions over that lattice. The system incorporates a multiple logic method that eliminates the weaknesses associated with using a single logic system and enhances the unique strengths of all the logic systems by using them in concert with each other.

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.

The invention belongs to the technical field of robot repair requesting, and particularly relates to a time standardization method for a robot repair requesting scene, which comprises the following steps: S1, performing multi-round dialogue time association processing, receiving a time text provided by a user in the current round, if the time text is a fuzzy expression, extracting identification date information in the previous round, and if the time text is not a fuzzy expression, extracting identification date information in the previous round; combining the date information and the current time text into an input parameter, judging whether the input parameter contains an identified date or not, if not, entering S2, and if yes, entering S3; s2, date reasoning: matching date logic in a user text through semantic keywords, and outputting a standardized date; and S3, time reasoning: matching time logic in the user text based on the semantic keywords, and outputting initial time in combination with a preset default time point. Through the keyword processing mode and multi-round context fusion, precise analysis of complex time expression is achieved at a low-computing-power terminal, dependence on high-performance computing resources is not needed, and the method can adapt to the light-weight operation environment of edge equipment.

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.