A platform, method, and system for a digital assistant are disclosed. The digital assistant uses natural language processing to respond to user queries. The digital assistant is extensible, because it can interface with a variety of devices, with a variety of natural language understanding providers, and with a variety of handlers. One method includes receiving a query from a user device, standardizing the query, and transmitting it to one of a plurality of natural language understanding providers. The digital assistant then receives intent data related to the intent of the query. The digital assistant then transmits the intent data to one of a plurality of handlers. The handler processes the intent data and returns content to the digital assistant. The digital assistant then adapts the content and sends it to the user device.

Embodiments of the present invention provide computer-implemented methods, computer program products and systems. Embodiments of the present invention can run preemptable tasks distributed according to a distributed environment, wherein each task of a plurality of preemptable tasks has been assigned two or more of the training data samples to process during each iteration. Embodiments of the present invention can, upon verifying that a preemption condition for each iteration is satisfied: preempt any task of the preemptable tasks that have started processing training data samples assigned to it, and update the cognitive model based on outputs obtained from completed tasks, including outputs obtained from both the preempted tasks and completed tasks that have finished processing all training data samples as assigned to it.

A method, system and computer readable medium for performing a cognitive search operation comprising: receiving training data, the training data comprising information based upon user interaction with cognitive attributes; performing a machine learning operation on the training data; generating a cognitive profile based upon the information generated by performing the machine learning operation; and, performing a cognitive search operation on a corpus of content based upon the cognitive profile, the cognitive search operation returning cognitive results specific to the cognitive profile of the user.

A method, system and computer readable medium for performing a cognitive search operation comprising: receiving training data, the training data comprising information based upon user interaction with cognitive attributes; performing a machine learning operation on the training data; generating a cognitive profile based upon the information generated by performing the machine learning operation; and, performing a cognitive search operation on a corpus of content based upon the cognitive profile, the cognitive search operation returning cognitive results specific to the cognitive profile of the user.

Disclosed is a multiagent system with agents in communication with each other via a communication network. The agents have heterogeneous time-invariant dynamics such that all of the agents have a primary set of synchronization roles that are different from a secondary set of synchronization roles of a subset of the agents.

Systems and methods are provided relating to a complex adaptive command guided swarm system including an operator section comprising a first command and control section and a plurality of networked swarm of semi-autonomously agent controlled system of systems platforms (SAASoSPs). The first command and control section includes a user interface, computer system, network interface, and plurality of command and control systems executed or running on the computer system. The networked SAASoSPs each include a second command and control section, wherein the second command and control section utilizes artificial intelligence (AI) configured with a combination of both symbolic and probabilistic machine learning for various functions including pattern recognition and new pattern identification. The AI is also configured to combine advice-based learning with active learning, wherein the AI solicits advice from a domain expert user via the first command and control section as necessary during both training and operational stages of the system.

Systems and methods are provided relating to a complex adaptive command guided swarm system including an operator section comprising a first command and control section and a plurality of networked swarm of semi-autonomously agent controlled system of systems platforms (SAASoSPs). The first command and control section includes a user interface, computer system, network interface, and plurality of command and control systems executed or running on the computer system. The networked SAASoSPs each include a second command and control section, wherein the second command and control section utilizes artificial intelligence (AI) configured with a combination of both symbolic and probabilistic machine learning for various functions including pattern recognition and new pattern identification. The AI is also configured to combine advice-based learning with active learning, wherein the AI solicits advice from a domain expert user via the first command and control section as necessary during both training and operational stages of the system.

An agent interfaces with a sending computing device and a receiving computing device to automatically adjust one-way or two-way real-time audio and real-time video transmission parameters responsive to changing network conditions and/or application requirements. The agent incorporates a reinforcement learning model that adjusts transmission parameters to maximize an expected value of a sum of future rewards; the expected value of the sum of future rewards is based on a current state of the sending computing, a current action (e.g. a current set of transmission parameters) at the sending computing device and a reward provided by the receiving computing device. The reward is representative of a user-perceived quality of experience at the receiving computing device.

An agent interfaces with a sending computing device and a receiving computing device to automatically adjust one-way or two-way real-time audio and real-time video transmission parameters responsive to changing network conditions and/or application requirements. The agent incorporates a reinforcement learning model that adjusts transmission parameters to maximize an expected value of a sum of future rewards; the expected value of the sum of future rewards is based on a current state of the sending computing, a current action (e.g. a current set of transmission parameters) at the sending computing device and a reward provided by the receiving computing device. The reward is representative of a user-perceived quality of experience at the receiving computing device.

Systems and methods for controlling operations of a computer system. The methods comprises: collecting, by at least one computing device, information about events occurring in the computer system; performing automated ontogenesis operations by the at least one computing device using the collected information to determine a context of a given situation associated with the computer system, define parameters for a plurality of different sets of actions that could occur in the context of the given situation, and simulate the sets of actions to generate predicted consequences resulting from the performance of certain behaviors by nodes of the computer system; and using the parameters of at least one of the predicted consequences to control operations of the computer system.