A neural network system is proposed, including an input network for extracting, from state data, respective entity data for each a plurality of entities which are present, or at least potentially present, in the environment. The entity data describes the entity. The neural network contains a relational network for parsing this data, which includes one or more attention blocks which may be stacked to perform successive actions on the entity data. The attention blocks each include a respective transform network for each of the entities. The transform network for each entity is able to transform data which the transform network receives for the entity into modified entity data for the entity, based on data for a plurality of the other entities. An output network is arranged to receive data output by the relational network, and use the received data to select a respective action.

A neural network system is proposed, including an input network for extracting, from state data, respective entity data for each a plurality of entities which are present, or at least potentially present, in the environment. The entity data describes the entity. The neural network contains a relational network for parsing this data, which includes one or more attention blocks which may be stacked to perform successive actions on the entity data. The attention blocks each include a respective transform network for each of the entities. The transform network for each entity is able to transform data which the transform network receives for the entity into modified entity data for the entity, based on data for a plurality of the other entities. An output network is arranged to receive data output by the relational network, and use the received data to select a respective action.

A hierarchical resource management system for a building includes one or more processors. The processors implement a plurality of agents that each monitor sensed values, and generate operating scenarios based on the sensed values for corresponding resources. The processors also implement a coordinator that filters the operating scenarios to remove the operating scenarios that violate internal laws of the agents to form an aggregate validated set of operating scenarios. The processors further implement a supervisor that, responsive to receipt of target conditions for the zones and the aggregate validated set of operating scenarios from the coordinator, selects a combination of the operating scenarios from the aggregate validated set of operating scenarios that achieves target conditions and minimizes overall energy consumption by the resources such that some of the operating scenarios of the combination do not minimize energy consumption of the resources corresponding to the some of the operating scenarios.

An input unit 81 receives input of a decision-making history of a subject. A learning unit 82 learns hierarchical mixtures of experts by inverse reinforcement learning based on the decision-making history. An output unit 83 outputs the learned hierarchical mixtures of experts. The learning unit 82 learns the hierarchical mixtures of experts using an EM algorithm, and when a learning result using the EM algorithm satisfies a predetermined condition, learns the hierarchical mixtures of experts by factorized asymptotic Bayesian inference.

The present invention relates to a system and method for creating artificial intelligence-based knowledge bot application. The objective is to cater to various needs of knowledge transition at organizational level. Accordingly, the bot application can collect and extract knowledge from transition artifacts related to multiple applications in any format. Availability of existing knowledge or standard keys in collected artifacts is assessed along with associated knowledge gaps using a machine learning approach.

A method at an analytics module on a computing device, the analytics module being at a tier within a hierarchy of analytics modules and data sources, the method including receiving a first data set from a data source or a lower tier analytics module; analyzing the first data set to create a second data set; providing the second data set to at least one higher tier analytics module, the second data set being derived from the first data set; and providing at least one of an inference and an interdiction to the lower tier analytics module.

Techniques for structuring knowledge bases specific to a user or group of users and techniques for using the knowledge bases to answer user inputs are described. A knowledge base may be populated with information provided by users associated with the knowledge base. Users associated with a knowledge base may be proactive in providing content to the knowledge base and/or a system may solicit an answer to a user input from users associated with a particular knowledge base. When the system receives an answer, the system may populate the knowledge base with the answer and may output the answer to the user that originated the user input. The system may output user inputs to be answered using messages or by establishing two-way communication sessions.

The improved exercise of artificial intelligence by providing a systematic way for a computing system to interface with output from AI models. To do this, the computing system obtains results of an input data set being applied to an AI model. The results are then refined based upon characteristic(s) of the AI model and perhaps the input data set. Based upon characteristic(s) of the AI model and perhaps the input data set, interface element(s) are identified that can be used to interface with the refined results. The interface element(s) are then communicated to an interface element that interfaces with the refined results. The interface element(s) may include, for instance, operator(s) or term(s) that may be used to query against the refined results and/or an identification of visualization(s) that may be used to present to a user results of queries against the refined results.

Graph computing over micro and macro views includes expanding, with a processor at run-time, a set of nodes to include a node generated in response to received data corresponding to an event query. A first inference of an inference ensemble is determined by traversing a base graph whose nodes are associated with a discriminant power that exceeds a predetermined entity threshold. A second inference of the inference ensemble is determined by traversing a micro-view graph whose nodes are selected based on a number of references that exceeds a predetermined reference threshold. A third inference of the inference ensemble is determined by traversing a macro-view graph having one or more committee nodes and computing for each committee node a macro-node vote and generating a response to the event query based on the inference ensemble.

To enable provision of appropriate information for a user query even in a case there are multiple information provision modules which are different in answer generation processing. A query sending unit 212 sends a user query to each one of a plurality of information provision module units 220 that are different in the answer generation processing and that each generate an answer candidate for the user query. An output control unit 214 performs control such that the answer candidate acquired from each one of the plurality of information provision module units 220 is displayed on a display unit 300 on a per-agent basis with information on an agent associated with that information provision module unit 220.