Sony Group Corporation

Sony Corporation of America

Varun Kompella

James MacGlashan

Mark Ring

Satinder Singh Baveja

Kaushik Subramanian

Roberto Capobianco

Peter R Wurman

Samuel Barrett

Peter Stone

A method and system for teaching an artificial intelligent agent where the agent can be placed in a state that it would like it to learn how to achieve. By giving the agent several examples, it can learn to identify what is important about these example states. Once the agent has the ability to recognize a goal configuration, it can use that information to then learn how to achieve the goal states on its own. An agent may be provided with positive and negative examples to demonstrate a goal configuration. Once the agent has learned certain goal configurations, the agent can learn policies and skills that achieve the learned goal configuration. The agent may create a collection of these policies and skills from which to select based on a particular command or state.

Method and system for an intelligent artificial agent

A method and system for teaching an artificial intelligent agent includes giving the agent several examples where it can learn to identify what is important about these example states. Once the agent has the ability to recognize a goal configuration, it can use that information to then learn how to achieve the goal states on its own. An agent may be provided with positive and negative examples to demonstrate a goal configuration. Once the agent has learned certain goal configurations, the agent can learn an option to achieve the goal configuration and a distance function that predicts at least one of a distance and a duration to the goal configuration under the learned option. This distance function prediction may be incorporated as a state feature of the agent.

Method and system for continual learning in an intelligent artificial agent

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Aachen, Germany

Varun Kompella