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 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 basement system includes a concrete basement and a drain channel. The concrete basement includes a basement slab and a basement wall. The basement slab presents a floor surface, with the basement wall extending about the floor surface. The basement slab and basement wall cooperatively define a trench that extends along the basement wall and below the floor surface to receive water in the basement. The drain channel is located at least partly in the trench to collect water therein. The drain channel includes an elongated channel tube and an upright riser integrally formed with the channel tube. The channel tube includes axially-extending opposite side walls that at least partly define an interior to receive water, with at least one of the side walls presenting openings that permit water ingress from the trench to the interior. The riser extends upwardly from one of the side walls and engages the basement wall.

Abstract: A process includes receiving a request via an API, determining a query based on a set of query parameters, and determining a target graph portion template based on the query, where the request includes a callback address. The process may include searching a set of directed graphs to determine a set of graph portions based on the query. Each respective directed graph of the set of directed graphs may include a set of vertices and a set of directed edges connecting respective pairs of vertices among the set of vertices, where each respective vertex of the set of vertices is associated with a respective category label of a set of mutually exclusive categories. The process may include selecting a set of event records and sending a value of the set of event records to the callback address.

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 executing an instance of an application, where a program state includes a set of vertices and a set of directed graph edges, serializing the set of vertices in a serialized data format, and storing a record in a first persistent storage of a first computing device. The method includes distributing the serialized set of vertices to a second computing device and deserializing the serialized set of vertices to generate a second instance of a directed graph in non-serialized data format. The method includes storing the second instance in a second persistent storage, receiving a message encoding a graph portion template, and querying the second persistent storage based on the graph portion template to retrieve a subset of vertices encoded in the second instance. The method includes computing a response value based on the subset of vertices and transmitting the response value.

Abstract: A method includes obtaining a directed graph of a self-executing protocol, the directed graph including a set of vertices associated with mutually exclusive category labels, where the self-executing protocol identifies a first entity. The method may include obtaining a first graph portion template that includes a vertex template and an edge template. The vertex template is associated with a category of the mutually exclusive category labels. The method may include determining whether the first graph portion template matches a graph portion in the directed graph and an edge of the directed graph matching the edge template. The method may include determining an outcome score based on the graph portion template matching the graph portion, determining whether the outcome score satisfies an outcome score threshold, and storing a value indicating that the outcome score satisfies the outcome score threshold.

Abstract: A method includes obtaining program state of a self-executing protocol, wherein the program state includes a set of conditional statements and a directed graph including a set of vertices and a set of directed edges, each respective vertex associated with a respective category label of a set of mutually exclusive categories. The method may include receiving an event message including a set of parameters, selecting a first subset of vertices triggered by the event message and a second subset of vertices based on the first subset of vertices. The method may include determining an aggregated parameter based on a subset of conditional statements, where each respective conditional statement is associated with a respective vertex that is associated with a first category label of the set of mutually exclusive categories. The method may include storing the aggregated parameter in persistent storage.

Abstract: A method includes obtaining program state of a self-executing protocol, wherein the program state includes a set of conditional statements and a directed graph including a set of vertices and a set of directed edges, each respective vertex associated with a respective category label of a set of mutually exclusive categories. The method may include receiving an event message including a set of parameters, selecting a first subset of vertices triggered by the event message and a second subset of vertices based on the first subset of vertices. The method may include determining an aggregated parameter based on a subset of conditional statements, where each respective conditional statement is associated with a respective vertex that is associated with a first category label of the set of mutually exclusive categories. The method may include storing the aggregated parameter in persistent storage.

Abstract: A method includes obtaining program state of a self-executing protocol, wherein the program state includes a set of conditional statements and a directed graph including a set of vertices and a set of directed edges, each respective vertex associated with a respective category label of a set of mutually exclusive categories. The method may include receiving an event message including a set of parameters, selecting a first subset of vertices triggered by the event message and a second subset of vertices based on the first subset of vertices. The method may include determining an aggregated parameter based on a subset of conditional statements, where each respective conditional statement is associated with a respective vertex that is associated with a first category label of the set of mutually exclusive categories. The method may include storing the aggregated parameter in persistent storage.

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 determining a set of features associated with a set of vertices of a directed graph, obtaining a set of feature values associated with the set of vertices, where each respective vertex of set of vertices is associated with a respective subset of feature values. The method includes determining updatable features based on the set of features, selecting a first subset of features based on the set of updatable features. Selecting the first subset of features includes determining candidate subsets of features, determining feature subset scores associated with the candidate subsets of features based on a category label, and selecting the first subset of features based on the feature subset scores. The method includes performing a first operation to determine extracted feature values by determining feature extraction input values.

Abstract: A process includes obtaining a directed graph of a symbolic artificial intelligence model used by a first entity, the directed graph comprising a first set of vertices and a first set of edges associating pairs of vertices of the first set of vertices. The method also includes determining a set of features based on the directed graph that includes an identifier of a graph portion template, where each respective vertex of the graph portion template of the graph portion template is labeled with a same category from the set of mutually-exclusive categories as a corresponding respective vertex of a graph portion of the directed graph. The method also includes obtaining a set of model parameter values for a machine learning model based on the graph portion template and providing the set of model parameter values and the graph portion templates the first entity.

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.

Abstract: Information throttle that modifies a device's access to applications, features, or information to enforce workplace use policies. May be used with “bring your own devices” that belong to a user but are also used at work. For example, the system may disable games, media players, or social media when it determines that a device is used at work. A usage context monitor determines the context in which the device is currently used. The monitor may determine that a device is used for work when the time matches working hours, the device location matches a work site location, or the network connected to a device matches a workplace network. In addition to blocking access to inappropriate features, the information throttle may allow access to sensitive information only when the device is at work. Access may also depend on factors such as user credentials and the identity and configuration of the device.

Abstract: Information throttle that modifies a device's access to applications, features, or information to enforce workplace use policies. May be used with “bring your own devices” that belong to a user but are also used at work. For example, the system may disable games, media players, or social media when it determines that a device is used at work. A usage context monitor determines the context in which the device is currently used. The monitor may determine that a device is used for work when the time matches working hours, the device location matches a work site location, or the network connected to a device matches a workplace network. In addition to blocking access to inappropriate features, the information throttle may allow access to sensitive information only when the device is at work. Access may also depend on factors such as user credentials and the identity and configuration of the device.

Abstract: Information throttle that modifies a device's access to applications, features, or information to enforce workplace use policies. May be used with “bring your own devices” that belong to a user but are also used at work. For example, the system may disable games, media players, or social media when it determines that a device is used at work. A usage context monitor determines the context in which the device is currently used. The monitor may determine that a device is used for work when the time matches working hours, the device location matches a work site location, or the network connected to a device matches a workplace network. In addition to blocking access to inappropriate features, the information throttle may allow access to sensitive information only when the device is at work. Access may also depend on factors such as user credentials and the identity and configuration of the device.