Cloud infrastructure planning systems and methods can utilize artificial intelligence/machine learning agents for developing a plan of demand, plan of record, plan of execution, and plan of availability for developing cloud infrastructure plans that are more precise and accurate, and that learn from previous planning and deployments. Some agents include one or more of supervised, unsupervised, and reinforcement machine learning to develop accurate predictions and perform self-tuning alone or in conjunction with other agents.

A computing device may execute a conversational agent that may receive language input. The conversational agent may analyze the language input based on configured goals to determine conclusions regarding the language input. The conversational agent may determine whether to modify the truth of one or more of the conclusions, and whether to include or omit the one or more conclusions or modified conclusions in an output response. The conversational agent may also store justifications for including or omitting each conclusion or modified conclusion. The conversational agent may output a response that indicates the conclusions and/or modified conclusions that were selected for output. A user may request that the conversational agent output the justifications for generating the output response. The conversational agent may output the justifications based on receiving the request.

A computing device may execute a conversational agent that may receive language input. The conversational agent may analyze the language input based on configured goals to determine conclusions regarding the language input. The conversational agent may determine whether to modify the truth of one or more of the conclusions, and whether to include or omit the one or more conclusions or modified conclusions in an output response. The conversational agent may also store justifications for including or omitting each conclusion or modified conclusion. The conversational agent may output a response that indicates the conclusions and/or modified conclusions that were selected for output. A user may request that the conversational agent output the justifications for generating the output response. The conversational agent may output the justifications based on receiving the request.

Restricted access tokens are cognitively generated that provide cyber forensic specialists restricted access to applications that require investigation. Cognitive analysis is performed on case details and, in some instances, evidence logs of previously investigated applications to determine parties involved in the investigation and applications requiring investigation. In response to identifying one of the applications, the case details, applicable evidence logs and the identified application are cognitively analyzed to determine operations that are required to be performed in the application and a time required to perform the operations. A restricted access token is generated that is specific to the assigned specialist, the case, and the application. The restricted access token grants the assigned specialist access to only data in the application associated with the one or more parties, rights to perform only the one or more operations in the application, and access to the initial application for a usage time that is based on the time required to perform the operations.

Exemplary embodiments of the present invention provide a virtual dispatch assist system in which various types of Intelligent Agents are deployed (e.g., as part of a new CAD system architecture or as add-ons to existing CAD systems) to analyze vast amounts of historic operational data and provide various types of dispatch assist notifications and recommendations that can be used by a dispatcher or by the CAD system itself (e.g., autonomously) to make dispatch decisions.

A method for A method for automatic emergency braking (AEB) of a vehicle, the method may include obtaining sensed information regarding an environment of the vehicle and regarding a motion of the vehicle; determining an occurrence of current situation, based on the sensed information; selecting, based on the current situation, a selected narrow AI agent out of multiple narrow AI agents; wherein different narrow AI agents are trained, using a machine learning process, to perform AEB related decisions at different scenarios; wherein the selected narrow AI agent is associated with the occurrence of the current situation; processing, by the selected narrow AI agent, at least one out of (a) at least a first part of the sensed information, and (b) an outcome of a pre-processing of at least a second part of the sensed information; wherein the processing results in a AEB related decision; and responding to the AEB related decision, wherein the responding comprises at least one out of (a) executing the AEB related decision, and (b) suggesting executing the AEB related decision.

A secured exploration agent for reinforcement learning (RL) is provided. Securitizing an exploration agent includes training the exploration agent to avoid dead-end states and dead-end trajectories. During training, the exploration agent “learns” to identify and avoid dead-end states of a Markov Decision Process (MDP). The secured exploration agent is utilized to safely and efficiently explore the environment, while significantly reducing the training time, as well as the cost and safety concerns associated with conventional RL. The secured exploration agent is employed to guide the behavior of a corresponding exploitation agent. During training, a policy of the exploration agent is iteratively updated to reflect an estimated probability that a state is a dead-end state. The probability, via the exploration policy, that the exploration agent chooses an action that results in a transition to a dead-end state is reduced to reflect the estimated probability that the state is a dead-end state.

A secured exploration agent for reinforcement learning (RL) is provided. Securitizing an exploration agent includes training the exploration agent to avoid dead-end states and dead-end trajectories. During training, the exploration agent “learns” to identify and avoid dead-end states of a Markov Decision Process (MDP). The secured exploration agent is utilized to safely and efficiently explore the environment, while significantly reducing the training time, as well as the cost and safety concerns associated with conventional RL. The secured exploration agent is employed to guide the behavior of a corresponding exploitation agent. During training, a policy of the exploration agent is iteratively updated to reflect an estimated probability that a state is a dead-end state. The probability, via the exploration policy, that the exploration agent chooses an action that results in a transition to a dead-end state is reduced to reflect the estimated probability that the state is a dead-end state.

A system, method, and computer-readable medium are disclosed for cognitive information processing. The cognitive information processing includes processing data from a plurality of data sources to provide cognitively processed insights via a augmented intelligence platform, the augmented intelligence platform comprising a cognitive process foundation platform, the cognitive process foundation platform comprising a cognitive composition platform, the cognitive composition platform being implemented to create custom extensions to the augmented intelligence platform.

A system, method, and computer-readable medium are disclosed for cognitive information processing. The cognitive information processing includes processing data from a plurality of data sources to provide cognitively processed insights via a augmented intelligence platform, the augmented intelligence platform comprising a cognitive process foundation platform, the cognitive process foundation platform comprising a cognitive composition platform, the cognitive composition platform being implemented to create custom extensions to the augmented intelligence platform.