Abstract: Systems and methods to facilitate the identification of connections or relationships in a network that are high impact in order to generate recommendations for future network growth are disclosed. The embodiments convert network maps into graphs comprising nodes and edges. The system identifies the edge that, when removed, causes the greatest impact on the network as a whole. In one embodiment, the system can be used to identify locations for installation of electric vehicle charging stations.

Abstract: Aspects of the present disclosure provide systems, methods, apparatus, and computer-readable storage media that support creating and leveraging digital twins to model multiple physical systems of an enterprise as a monolithic computer system. A digital twin platform may create an abstracted virtual model of an enterprise's system, the model representing a digital twin of a distributed collection of systems that as a group serve a larger goal of the enterprise. Because the abstracted virtual model is logically organized as a monolithic system that maps to multiple physical systems, the abstracted virtual model may be leveraged to provide system health monitoring and scoring from data gathered from the physical systems. The health monitoring, in addition to generation of insights for improving system health, may be easier to understand and more familiar to a user, thereby enabling meaningful determination of actions to perform to maintain or improve system health.

Abstract: A device may receive state data, actions, and rewards associated with a network of RL agents monitoring a microgrid environment, and may model the network of RL agents as a spatiotemporal representation. The device may represent interactions of the RL agents as edge attributes in the spatiotemporal representation, and may determine edge attributes, transmissibility, connectedness, and communication delay for each of the RL agents in the spatiotemporal representation. The device may determine, based on the transmissibility, the connectedness, and the communication delay, localized clusters of the RL agents, and may process the localized clusters, with a first machine learning model, to identify consensus master RL agents. The device may process the consensus master RL agents, with a second machine learning model, to identify a final master RL agent for the network of RL agents, and cause the final master RL agent to control the microgrid environment.

Abstract: A system and method of deploying and managing an integration for a hybrid computing environment is disclosed. The proposed systems and methods provide an intelligent hybrid cloud architecture and environment that offers reduced deployment times, and little to no errors. The system incorporates an artificial intelligence (AI) powered solution that is API-enabled and pre-integrated with system chatbots, as well as providing a secure, accelerated integration with available cloud ecosystems. The proposed solution is able to analyze business processes and derive and build deep insights toward the enterprise cloud integration, improving security, design, and performance of the hybrid architecture.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for resolving a contextual ticket. The methods, systems, and apparatus include actions of receiving a request from a user to generate a contextual ticket that indicates an issue with an integration, obtaining baseline information for the issue, generating, based on the baseline information, a ticket knowledge graph, providing the ticket knowledge graph to a machine-learning trained action determination engine, receiving, from the machine-learning trained action determination engine, an indication of an action for resolving the issue, and initiating, based on the indication of the action for resolving the issue, the action for resolving the issue.

Abstract: A system and method of deploying and managing an integration for a hybrid computing environment is disclosed. The proposed systems and methods provide an intelligent hybrid cloud architecture and environment that offers reduced deployment times, and little to no errors. The system incorporates an artificial intelligence (AI) powered solution that is API-enabled and pre-integrated with system chatbots, as well as providing a secure, accelerated integration with available cloud ecosystems. The proposed solution is able to analyze business processes and derive and build deep insights toward the enterprise cloud integration, improving security, design, and performance of the hybrid architecture.