Abstract: A computer-implemented process includes obtaining a natural-language-text document comprising a first and second clause and determining first and second embedding sequences based on n-grams of the first and second clauses. The process includes generating data model objects based on the embedding sequences and determining an association between the first data model object and the second data model object based on a shared parameter of the first and second clauses. The process includes receiving a query including the first category and the first n-gram and causing a presentation of a visualization of data model objects that includes shapes based on the data model objects and a third shape based on the association between the first data model object and the second data model object.

Abstract: A method includes obtaining identifiers of entities and symbolic artificial intelligence (AI) models configured to produce outputs responsive to inputs based on events caused by at least one of the entities. At least some of the entities are associated with outputs of respective symbolic AI models and have respective scores corresponding to the respective outputs of the symbolic AI models. The method may include obtaining scenarios, where each scenario includes simulated inputs corresponding to one or more simulated events, and at least some scenarios include a plurality of simulated inputs. The method may also include determining a population of scores of a given entity among the entities, where respective members of the population of scores correspond to respective outputs of the plurality of symbolic AI models, and where the respective outputs correspond to respective scenarios among the scenarios and storing the population of scores in memory.

Abstract: A method includes obtaining identifiers of entities and symbolic artificial intelligence (AI) models configured to produce outputs responsive to inputs based on events caused by at least one of the entities. At least some of the entities are associated with outputs of respective symbolic AI models and have respective scores corresponding to the respective outputs of the symbolic AI models. The method may include obtaining scenarios, where each scenario includes simulated inputs corresponding to one or more simulated events, and at least some scenarios include a plurality of simulated inputs. The method may also include determining a population of scores of a given entity among the entities, where respective members of the population of scores correspond to respective outputs of the plurality of symbolic AI models, and where the respective outputs correspond to respective scenarios among the scenarios and storing the population of scores in memory.

Abstract: A computer-implemented process includes obtaining a natural-language-text document comprising a first and second clause and determining first and second embedding sequences based on n-grams of the first and second clauses. The process includes generating data model objects based on the embedding sequences and determining an association between the first data model object and the second data model object based on a shared parameter of the first and second clauses. The process includes receiving a query including the first category and the first n-gram and causing a presentation of a visualization of data model objects that includes shapes based on the data model objects and a third shape based on the association between the first data model object and the second data model object.

Abstract: A method includes finding a smart contract that includes an associative array of entities, an associative array of conditions, and a serialized array of vertices. The method also includes deserializing the serialized array of vertices to generate a directed graph and determining a set of triggered vertices based on the directed graph and the event. Each of the set of triggered vertices is indicated as triggerable and is associated with a norm condition that is triggered by the event. The method includes updating the directed graph by updating a norm status associated with the triggered vertices and updating child vertices of the triggered vertices. The method includes updating the serialized array of vertices by serializing the updated directed graph and persisting the serialized array of vertices to a storage of the computer system.

Abstract: A method includes obtaining a symbolic AI model, where the symbolic AI model is configured to produce an outcome state responsive to an input based on events. The method also includes obtaining a first scenario and a second scenario, where the first scenario causes the failure of a condition associated with a norm of the symbolic AI model and the second scenario satisfies the condition associated with the norm of the symbolic AI model. The method also includes obtaining a failure penalty value, determining a first outcome state based on the symbolic AI model, the first scenario, and the failure penalty value. The method also includes determining a second outcome state based on the symbolic AI model and the second scenario. The method also includes determining an outcome score based on the first outcome state and the second outcome state.

Abstract: A method includes obtaining identifiers of entities and symbolic artificial intelligence (AI) models configured to produce outputs responsive to inputs based on events caused by at least one of the entities. At least some of the entities are associated with outputs of respective symbolic AI models and have respective scores corresponding to the respective outputs of the symbolic AI models. The method may include obtaining scenarios, where each scenario includes simulated inputs corresponding to one or more simulated events, and at least some scenarios include a plurality of simulated inputs. The method may also include determining a population of scores of a given entity among the entities, where respective members of the population of scores correspond to respective outputs of the plurality of symbolic AI models, and where the respective outputs correspond to respective scenarios among the scenarios and storing the population of scores in memory.

Abstract: A method includes obtaining a program state encoding a directed graph, the directed graph comprising a set of norm vertices and directed graph edges. The method also includes obtaining an amendment request encoding a set of conditional statement parameters and determining a set of active norm vertices in the directed graph. The method also includes selecting a set of target norm vertices of the set of active norm vertices based on the set of conditional statement parameters and determining a set of selected entities based on the set of target norm vertices. The method also includes determining whether a set of criteria associated with the set of selected entities is satisfied and updating the set of conditional statement parameters and entities based on the set of conditional statements in response to a determination that the set of criteria is satisfied.