Abstract: A method includes obtaining a binary code of a controller. The method also includes decompiling the binary code of the controller to generate a source code. The method further includes generating one or more abstract syntax trees based on the source code. The method further includes generating an interpretable model based on the one or more abstract syntax trees. The interpretable model is interpretable by subject matter experts.

Abstract: A method includes obtaining a binary code of a controller. The method also includes decompiling the binary code of the controller to generate a source code. The method further includes generating one or more abstract syntax trees based on the source code. The method further includes generating an interpretable model based on the one or more abstract syntax trees. The interpretable model is interpretable by subject matter experts.

Abstract: One or more documents are received. Each document of the one or more documents is partitioned into segments using stylistic cues from a textual format of each document. Each of the segments is mapped to a respective embedding based on one or more language models. A dependency graph is computed based on the embeddings. A rooted, ordered tree is produced based on the dependency graph. The rooted, ordered tree represents a hierarchical structure of each document.

Abstract: A method includes receiving a subject-matter expert (SME) interpretable model. The method further includes converting, by a processing device, the SME interpretable model into a functional mockup unit (FMU). The method further includes integrating the FMU into a control software project (CSP). The method further includes compiling the CSP into binary code.

Abstract: In some natural language understanding (NLU) applications, results may not be tailored to the user's query. In an embodiment of the present invention, a method includes tagging elements of automated speech recognition (ASR) data based on an ontology stored in a memory. The method further includes indexing tagged elements to an entity of the ontology. The method further includes generating a logical form of the ASR data based on the tagged elements and the indexed entities. The method further includes mapping the logical form to a query to a respective corresponding database stored in the memory. The method further includes issuing the query to the respective corresponding databases. The method further includes presenting results of the query to the user via a display or a voice response system.

Abstract: Presenting natural-language-understanding (NLU) results can include redundancies and awkward sentence structures. In an embodiment of the present invention, a method includes, responsive to receiving a result to a NLU query, loading a matching template of a plurality of templates stored in a memory. Each template has mask fields associated with at least one property. The method compares the properties of the mask fields of each of the templates to properties of the query and properties of the result, and selects the matching template. The method further completes the matching template by inserting fields of the result into corresponding mask fields of the matching template. The method may further suppress certain mask fields of the matching template to increase brevity and improve the naturalness of the response when appropriate based on the results of the NLU query. The method further presents the completed matching template to a user via a display.

Abstract: Presenting natural-language-understanding (NLU) results can include redundancies and awkward sentence structures. In an embodiment of the present invention, a method includes, responsive to receiving a result to a NLU query, loading a matching template of a plurality of templates stored in a memory. Each template has mask fields associated with at least one property. The method compares the properties of the mask fields of each of the templates to properties of the query and properties of the result, and selects the matching template. The method further completes the matching template by inserting fields of the result into corresponding mask fields of the matching template. The method may further suppress certain mask fields of the matching template to increase brevity and improve the naturalness of the response when appropriate based on the results of the NLU query. The method further presents the completed matching template to a user via a display.

Abstract: In some natural language understanding (NLU) applications, results may not be tailored to the user's query. In an embodiment of the present invention, a method includes tagging elements of automated speech recognition (ASR) data based on an ontology stored in a memory. The method further includes indexing tagged elements to an entity of the ontology. The method further includes generating a logical form of the ASR data based on the tagged elements and the indexed entities. The method further includes mapping the logical form to a query to a respective corresponding database stored in the memory. The method further includes issuing the query to the respective corresponding databases. The method further includes presenting results of the query to the user via a display or a voice response system.

Abstract: A knowledge representation system which allows a user to utilize a description language to generate, maintain and access information in a knowledge base is disclosed. The knowledge representation system includes an explanation subsystem which explains where the derived information that is stored for a particular object was obtained from. A first level explanation function will provide a user with an explanation of the immediate source of the pieces of derived information that are stored for an object in the knowledge base. A complete explanation function provides a complete explanation by continuing to explain how the immediate source for each piece of the information itself received the information, until the source of the information is told information or the result of a rule firing. The explanation subsystem also explains subsumption inferences, which explain why a particular concept or rule antecedent is satisfied by a particular object.

Abstract: A knowledge representation system which allows a user to generate, maintain and access information in a knowledge base using a description language is disclosed. The knowledge representation system maintains the dependency relationships between the different objects that are stored in the knowledge base so that pieces of information that are related to or dependent upon added or updated information may be properly classified at all times. The knowledge representation system includes a mechanism for processing dependency information that is returned from user-defined test functions in order to accurately maintain the dependency information implicated upon execution of the user-defined procedural test functions. A subsumption function determines if a particular individual in the knowledge base satisfies a concept. The subsumption function evaluates each component of a concept description and maintains the dependency information implicated by the concept component.