A policy learning apparatus of the present invention includes: a first unit configured to select a first action element based on a selection rate for each of choices of the first element whose number of choices does not depend on a state; a second unit configured to apply the selected first action element and further apply each of choices of a second action element whose number of choices depends on the state to obtain another state for each of the choices, and determine the other state based on a reward obtained by shifting to the other state and a value of the other state; and a third unit configured to further learn a model by using learning data generated based on information used when determining the other state.

Certain aspects of the present disclosure provide techniques for updating a policy of an agent, including receiving a first transaction file associated with an entity; predicting, by the agent, an expected reward for each respective string of a plurality of strings associated with the first transaction file based on a policy of the agent, wherein the policy is determined based on a context comprising at least an attribute of the entity; determining a first string based on a highest expected reward; providing, to an environment, the first string; receiving a response to the first string, wherein the response comprises an actual reward; and updating the policy of the agent based on the response to the first string.

Techniques for in-band modification of event notification preferences for server events are provided. One method comprises obtaining an event notification; providing the event notification to a target device based on rule-based preferences of a user associated with the target device;

obtaining a reply to the event notification from the target device, wherein the reply comprises event preferences of the user; and updating the rule-based preferences of the user based on the event preferences of the user. The updating of the rule-based preferences of the user may comprise creating, modifying and/or canceling at least one event preference rule of the user. A plurality of the event preference rules matching the event notification may be resolved in an order determined by one or more event preference rule resolution criteria.

This document describes the autonomous collection, generation, distribution, and management of online web content. The devices, systems, and methods described herein can be used to collect and generate online web content and communications in an automatic and autonomous manner. Specifically, the disclosed methods, devices, and systems may be employed to produce one or more communications and/or advertising campaigns, as well as for monitoring, managing, defining the efficiency, effectiveness, and workability of the campaign with respect to generating predicted user engagements, thereby accurately determining the cost benefits of the communication campaign. The system may track, evaluate, and provide analytic results that may then be used to better guide the system parameters for customizing autonomous communications directed one or more characteristics of a defined target audience.

A method includes assessing an input in a buffer against a rule in a first node of a rule tree to determine that an action should be performed and updating the buffer with results of performing the action. The method also includes inserting an indication of the input, the rule, and the results of performing the action into a tracker log and passing the updated buffer to a second node in the rule tree in response to determining that the first node points to the second node.

A method includes storing data in a knowledge graph stored in a database. The knowledge graph is defined by nodes connected by edges, in which a root node of the knowledge graph is connected to a first version node by a first edge. The root node represents a data asset, and the first version node represents a first version of the data asset. A status indicator associated with the first version node has a first state indicating that the first version of the data asset is an editable draft version of the data asset. Responsive to receiving an instruction to publish the first version of the data asset, the state of the status indicator is changed to a second state that indicates that the first version of the data asset is a published version of the data asset.

A system (100) includes a feature extraction engine (130), a finding code comparison engine (140), and a mapping interface (160). The feature extraction engine (130) extracts features of a statement of a finding code in a source vocabulary (110) and features of a second statement of a second finding code in a target vocabulary (112). The finding code comparison engine (140) determines a mapping between the statement of the source vocabulary and the second statement of the target vocabulary by comparing the extracted features based on at least one identified concept that comprises the extracted features. The mapping interface (160) presents the determined mapping on a display device (162).

Provided are techniques for unified cognition for a virtual personal cognitive assistant. Internet of Things (IoT) devices are coupled to a cognitive model, wherein the cognitive model includes a cognitive classifier, and wherein the cognitive classifier includes a cognitive dimension map and a recognition process. Input from one or more of the IoT devices is received. The cognitive dimension map is used to identify rules based on the input. The recognition process is used to identify events based on the rules. Then, the events are issued to one or more of the IoT devices, wherein the one or more IoT devices execute actions in response to the events.

A device receives real-time data and historical data associated with a product or a service, wherein the real-time data includes data indicating real-time interactions between customers of the product or the service and customer service personnel. The device processes the real-time data and the historical data to generate processed data, and utilizes a first artificial intelligence model with the processed data to determine future events associated with the product or the service, and preventative solutions for the future events. The device extracts one or more features from the processed data to provide extracted features, and utilizes a second artificial intelligence model with the extracted features to determine priorities associated with the preventative solutions. The device performs one or more particular preventative solutions based on the priorities associated with the preventative solutions.

One example method includes for each pillar in a group of AI ethics pillars, storing, in a datastore, context data concerning the AI ethics pillar, and the context data is determined using context rules. The method further includes storing, in the datastore, the context rules as minimum context requirements, and receiving, by the datastore, a request from a user to register an asset in the datastore. When user-supplied context information for the asset meets ethical requirements specified by the context rules, registering the asset in the datastore, and ensuring that an assessment mechanism is able to access, and assess, the context data for each AI ethics pillar.