23 results about "Truth value" patented technology

A tailored interactive language learning system that teaches an individualized set of vocabulary words to users through interactive avatars and stories. The interaction is modeled through probabilistic rules in a semantic network and neural network having objects and relations. Dialog and narration is generated dynamically based on the state of the interactive story model using phrasal rewrite rules and neural network implementiung a four-valued logic system in which truth values of the objects and relations are encoded as true, false, defined, and undefined in a single memory array.

The embodiment of the present application discloses a logic circuit truth table determination method, device and electronic equipment, relates to the technical field of electronic circuits, and can quickly extract the truth table of a logic circuit. The method comprises the following steps: based on a first group of input truth values in an input truth value list, input ports of a logic circuit described by a netlist are valued; the input truth value list is generated in advance according to the input ports of the logic circuit described by the netlist; based on the valuation of the input ports, the level value of the output ports of the logic circuit described by the netlist is calculated; it is judged whether the output ports have level values calculated; if the output ports have level values calculated, the calculated level value of the output ports is taken as the output truth value corresponding to the first group of input truth values. The present application is suitable for extracting the truth table of a logic circuit.

This application discloses a method and related apparatus for grading math problems, relating to the field of artificial intelligence technology. This application converts user responses in natural language into proof skeleton data in a pre-defined formal language, and uses a compiler of the pre-defined formal language as a deterministic oracle. By capturing compilation feedback signals, it determines the final evaluation result of the reasoning validity of the problem-solving process. By using the compiler's strict execution of substitution probability prediction, it provides deterministic logical truth values ​​for fuzzy natural language reasoning. Therefore, it can improve the reliability of the evaluation of the reasoning validity of students' problem-solving process.

A cause-effect graph generation device according to one aspect of the present disclosure comprises: a requirement analysis unit that uses a specification in which a requirement relating to a development target is described in natural language as input and uses a large language model to generate a condition relating to business logic represented by the specification and a truth table representing a relationship between the authenticity of the condition and the authenticity of results related to the business logic; a logical expression generation unit that uses the condition and the truth table as input and uses the large language model to generate, for each of the results, a logical expression which derives the results from the condition; and a cause-effect graph generation unit that uses the logical expression as input to generate a cause-effect graph which visualizes a relationship between the condition and the results.

The invention discloses a formal verification method and system in an industrial control system. The method comprises the following steps: determining a target business entity corresponding to an operation object in a current message in the industrial control system; obtaining at least one target rule corresponding to the target business entity from a verification rule base; executing a logic verification operation on the operand value in the current message based on each target rule; if the judgment result of the logic verification operation shows that the target rule is violated, a safety response action is triggered; wherein the execution process of the logic verification operation comprises the following steps of: generating an instantaneous atomic proposition truth value corresponding to an operation value in a current message; obtaining truth values of other atomic propositions in the target rule from the system state cache to obtain a historical atomic proposition truth value; combining the instantaneous atomic proposition truth value and the historical atomic proposition truth value to obtain an atomic proposition truth value vector of the current message; and obtaining a judgment result corresponding to the true value vector of the atomic proposition, and defending logic attacks which utilize legal instructions but violate the technological process.

The present disclosure relates to a task planning method and device based on a large language model, an electronic device, a storage medium and a computer program product. The method comprises: for each task in a plurality of robot tasks, randomly sampling target parameters from a parameter set corresponding to the task; generating a target task instruction in natural language form based on the target parameters; constructing a structured step true value sequence corresponding to the target task instruction; inputting the target task instruction into a pre-trained large language model; comparing a structured step prediction sequence with the structured step true value sequence; and based on the comparison result, evaluating the inference performance of the large language model. In this way, since the present disclosure can automatically and quantitatively evaluate the structured inference ability of the large language model under fixed task constraints, the adverse effects of human factors on the inference performance evaluation of the large language model caused by relying on artificial labeling or subjective judgment can be effectively avoided.

Provided is a secret logical calculation device in a secret logical calculation system comprising three or more secret logical calculation devices for calculating ternary logic in a secret manner, wherein: a truth value of the ternary logic is expressed using two bits; the first bit x0 from between the two bits (x0, x1 used to represent the truth value of the ternary logic is a flag indicating whether the truth value is Undefined, and the second bit x1 represents a true / false value which is the truth value when the first bit x0 indicates that the truth value is not Undefined (however, when the first bit x0 indicates that the value is Undefined, the second bit (x0, x1) represents Undefined).

Disclosed are a method and system for redundancy switch between two controllers for supplementary damping of a governor, relating to the technical field of control methods. The method includes acquiring a participation condition according to an output result, listing the participation condition in a truth value table and supplementing correlation logic; outputting the correlation logic as an image language, and simplifying the image language; and acquiring switch logic by a simplified image language, and transforming the switch logic into a programming language of the controller. A complex control relation can be decomposed in the disclosure, so that the difficulty can be reduced, and a correct and comprehensive control purpose can be achieved. Moreover, the method has universality.

The invention relates to a method, particularly a computer-implemented method for determining a fatigue value of a person, the method comprising the steps of:i) displaying of:a) a first group (1) of symbols and a second group (2) of symbols,b) a selection rule (3) associating each of the symbols of the first group (1) of symbols to one symbol of the second group (2) of symbols,ii) highlighting one symbol (11) of the first group (1) of symbols,iii) receiving a user input from the person selecting one symbol (12) of the second group (2) of symbols,iv) determining a truth value based on the selected symbol (12) of the second group (2) of symbols, wherein the truth value assumes a true value if the selected symbol (12) is selected in accordance with the selection rule (3) and wherein the truth value takes on a false value if not, wherein the highlighted symbol (11) of the first group (1) of symbols is highlighted for a predetermined time based on a statistical response time of the person, and wherein if the user input is not received within the predetermined time in step iv), the method is set forth with step v) having the truth value set to the false value, or the method is set forth with step vi),v) storing the truth value on a non-transitory memory device,vi) changing the selection rule (3), such that each of the symbols of the first group (1) of symbols is associated anew to one symbol of the second group (2) of symbols,vii) changing the highlighted symbol (11) of the first group (1) of symbols,viii) repeating steps iii) to vii) at least once andix) based on at least some of the stored truth values, determining a fatigue value of the person.

The invention relates to the technical field of water conservancy and hydropower engineering safety monitoring, and discloses a safety monitoring data abnormal value identification method based on a sliding window and morphological logic, and the method comprises the steps: processing an original data sequence through a window sliding at a fixed odd length, calculating a median and a median absolute deviation, and estimating a standard deviation; generating a dynamic upper and lower threshold sequence; comparing with a dead zone tolerance threshold value, converting into a logic vector, identifying a connected region with continuous truth values, and secondarily marking all data points in the region with the length exceeding the maximum allowable value as abnormal to obtain an abnormal value logic sequence; and performing linear interpolation processing on the data points marked as abnormal values in the abnormal value logic sequence to obtain a cleaned data sequence without abnormal values. According to the method, more than 95% of invalid data can be efficiently eliminated while the integrity of real signals is reserved, a high-reliability data basis is provided for data analysis and structural safety evaluation, and the method has important engineering application value.

System for causal-logical validation of AI output values, comprising an AI module and a logic validator, wherein the logic validator has access to an axiomatic reference database (Commons architecture).

The invention discloses a multi-modal data generation method and system for failure event injection in a complex scene, and belongs to the technical field of computer data generation. The method comprises the following steps of: 1, constructing a failure action and a recovery action, including obtaining and expanding an original script and defining and realizing a regularized failure type; 2, generating multi-modal data based on the failure action and the recovery action constructed in the step 1; and step 3, based on the multi-modal data generated in the step 2, forming a data set which can be directly used for VLMs failure perception evaluation through quality control, template design, truth value labeling and data division. The method is used for constructing a multi-modal reasoning data set, the data set comprises various failure events and corresponding recovery actions, and evaluation data in a video question-answer pair form is constructed based on multi-modal data and annotation files.

The invention relates to a resource-optimized dynamic crowd sensing truth value discovery method, and belongs to the field of crowd sensing and crowdsourcing data management. The method comprises the following steps: obtaining initial capability estimation values of users through initial golden standard problem tasks, sorting the initial capability estimation values, and distributing common perception tasks for the users; and performing iterative calculation on perception data of all users participating in the same common perception task to obtain final truth value estimation of the current common perception task, thereby calculating a temporary capability index of each user, constructing a state vector and inputting the state vector into a reinforcement learning model to obtain a decision of a golden standard problem calibration task, and finally obtaining a standard problem calibration task. A gold standard problem calibration task is allocated to the user; and generating a reward signal based on the task release cost to update parameters of the reinforcement learning model, and optimizing generation of a decision. The objective of the invention is to solve the technical problems that in the prior art, a task allocation strategy cannot be dynamically adjusted, and resource waste or insufficient calibration precision is caused by a fixed-frequency GSQ publishing mode.

The embodiment of the invention provides a truth value and label data generation method and system based on multi-source heterogeneous data, and is applied to the technical field of data processing. The method comprises the following steps: acquiring multi-source heterogeneous data; performing multi-source space-time alignment on the multi-source heterogeneous data, and injecting static label data; calculating the truth value of the aligned multi-source heterogeneous data based on a truth value model and a label model and labeling a label, wherein an adaptive calculation engine is selected according to a data processing scene; wherein the data processing scene comprises an offline scene and a real-time scene; loading calculation logic preset by a truth value model and a label model through the calculation engine, performing truth value calculation on the aligned multi-source heterogeneous data, and generating truth value data meeting the requirements of the truth value model; performing label labeling on the aligned multi-source heterogeneous data and truth value data through the calculation engine according to the label attribute definition, and generating label data meeting the requirements of a label model; and performing structured conversion and quality verification on the generated truth value data and label data to generate final truth value and label data. In this way, efficient and automatic true value data and label data generation of mass multi-source heterogeneous multi-modal data can be achieved.

The application relates to the technical field of artificial intelligence, and proposes an intelligent agent performance verification method based on structured information embedding, an electronic device and a computer program product. The method comprises the following steps: converting each truth record in a structured truth record set into a corresponding unstructured sentence, and the truth record set is generated according to a predefined data structure; inserting each unstructured sentence into a background text stream to form a test text stream; inputting the test text stream and an aggregation query instruction generated based on the data structure into a to-be-tested intelligent agent, and obtaining a natural language answer output by the to-be-tested intelligent agent; converting the natural language answer into a structured answer record set; and determining a performance verification result of the to-be-tested intelligent agent by comparing the truth record set with the answer record set. The method can verify whether the intelligent agent can restore discrete distributed fact information from the test text stream without omission, so as to verify the aggregation completeness of the intelligent agent for key information.

The invention discloses a reinforcement learning optimization method and device for an optical character recognition model, and relates to the technical field of optical character recognition, and the method comprises the steps: obtaining a reference OCR model and a pre-training model with an optical character recognition function; a fact reward is introduced, reinforcement learning optimization is carried out on the pre-trained model, and an optimized model is obtained; the fact rewards comprise a semantic consistency reward, a logic fact verification reward and an output length penalty term; wherein the semantic consistency reward reflects the consistency degree of the output of the pre-training model and the annotation truth value; the logic fact verification reward comprises reward of semantic logic self-consistent degree output by the pre-training model and verification of structured fields output by the pre-training model by applying a deterministic rule engine; the output length penalty term reflects the deviation between the length output by the pre-training model and the reference length; the reference length is output by a benchmark OCR model. According to the method, the accuracy of image text recognition can be improved, and the robustness of the model is improved.

The invention relates to the technical field of network security, and discloses a vulnerability chain screening method and system based on a large language model and prompt engineering, and the method comprises the steps: constructing a multi-type vulnerability chain data set containing a truth value label; selecting target large language models adapted to different scenes, and generating an evaluation instruction of a thinking chain and comparison prompt combination; the model carries out dialectical evaluation from two dimensions of association logic and chain integrity, and outputs indexes such as validity judgment, accuracy and precision; screening effective vulnerability chains according to rules; and the screening is completed by approximately replacing the existence proof success rate POVsuccess with the evaluation accuracy rate. By applying the method, the false alarm rate can be reduced by more than 40%, the POV verification efficiency can be improved by more than 80%, the method is adaptive to multiple scenes, the reproducibility is high, and the vulnerability chain construction quality can be improved.

The invention relates to a large language model training method and device, an information processing method and device, electronic equipment, a storage medium and a program product, relates to the technical field of artificial intelligence, and reduces the illusion reply phenomenon of a large language model. The large language model training method comprises the steps of obtaining a target data set, wherein target data in the target data set comprises a preset question and a truth value reply of the preset question; auxiliary information of a preset problem in the target data is acquired; a first prompt, a preset question in the target data and auxiliary information of the preset question are input into a large language model, a reply of the preset question is generated, and the first prompt is used for instructing the large language model to output a negative reply under the condition that the preset question cannot be determined; and checking the reply of the large language model according to the truth value reply of the preset question, and finely adjusting the large language model according to a checking result.

This application discloses a method, apparatus, device, and medium for context retrieval of large language models based on feedback distillation, relating to the fields of artificial intelligence and data distillation technology. The method includes: firstly, constructing a full amplification hypothesis set of unlabeled data through Cartesian product; inferring the truth value through semantic decomposition, noise perturbation, and truth-locking deduction; and then finely stratifying the data into elite, mediocre, and toxic positive sample sets; subsequently, training an initial dense retrieval tool using a loss function with toxicity weights, and distilling out a target dense retrieval tool with value perception capabilities; finally, in the retrieval stage, accurately recalling high-value target context examples from the sample database based on the query text, achieving efficient context retrieval of large language models. This application can identify and avoid toxic positive samples with correct labels but interfering with model inference in context retrieval of large language models, solving the drawback of traditional retrieval tools that only select samples based on surface semantic similarity.

A formal verification method and system for an industrial control system, the method comprising: determining the target business entity corresponding to the operation object in the current message of the industrial control system; obtaining at least one target rule corresponding to the target business entity from a verification rule base; performing a logical verification operation on the operation value in the current message based on each target rule; if the judgment result of the logical verification operation indicates a violation of the target rule, triggering a security response action; wherein the execution process of the logical verification operation includes: generating the instantaneous atomic proposition truth value corresponding to the operation value in the current message; and obtaining the truth values ​​of other atomic propositions in the target rule from the system state cache to obtain the historical atomic proposition truth values; combining the instantaneous atomic proposition truth values ​​and the historical atomic proposition truth values ​​to obtain the atomic proposition truth value vector of the current message; obtaining the judgment result corresponding to the atomic proposition truth value vector to defend against logical attacks that use legal instructions but violate the process flow.

A tailored interactive language learning system that teaches an individualized set of vocabulary words to users through interactive avatars and stories. The interaction is modeled through probabilistic rules in a semantic network and neural network having objects and relations. Dialog and narration is generated dynamically based on the state of the interactive story model using phrasal rewrite rules and neural network implementing a four-valued logic system in which truth values of the objects and relations are encoded as true, false, defined, and undefined in a single memory array.