Abstract: Systems and methods may ethically evaluate intelligent systems operating in a real-world environment. The systems and methods may generate a clone of the intelligent system, and test the clone in a simulation environment. If the clone passes the testing, the systems and methods may permit the intelligent system to continue operating in the real-world environment. If the clone fails the testing, the systems and methods may override the intelligent system, such as disabling the intelligent system and assuming control in the real-world environment. The systems and methods may be implemented at a hardware level of a data processing device to prevent interference with the systems and methods by the intelligent system.

Abstract: An intelligent system, such as an autonomous robot agent, includes systems and methods to learn various aspects about a task in response to instructions received from a human instructor, to apply the instructed knowledge immediately during task performance following the instruction, and to instruct other intelligent systems about the knowledge for performing the task. The learning is accomplished free of training the intelligent system. The instructions from the human instructor may be provided in a natural language format and may include deictic references. The instructions may be received while the intelligent system is online, and may be provided to the intelligent system in one shot, e.g., in a single encounter or transaction with the human instructor.

Abstract: Systems and methods may ethically evaluate intelligent systems operating in a real-world environment. The systems and methods may generate a clone of the intelligent system, and test the clone in a simulation environment. If the clone passes the testing, the systems and methods may permit the intelligent system to continue operating in the real-world environment. If the clone fails the testing, the systems and methods may override the intelligent system, such as disabling the intelligent system and assuming control in the real-world environment. The systems and methods may be implemented at a hardware level of a data processing device to prevent interference with the systems and methods by the intelligent system.

Abstract: An intelligent system, such as an autonomous robot agent, includes systems and methods to learn various aspects about a task in response to instructions received from a human instructor, to apply the instructed knowledge immediately during task performance following the instruction, and to instruct other intelligent systems about the knowledge for performing the task. The learning is accomplished free of training the intelligent system. The instructions from the human instructor may be provided in a natural language format and may include deictic references. The instructions may be received while the intelligent system is online, and may be provided to the intelligent system in one shot, e.g., in a single encounter or transaction with the human instructor.

Abstract: Systems and methods are described for sharing components among intelligent agents, such as robot agents that perform tasks autonomously. The intelligent agents may include functional components implemented in a middleware layer that provides an interface among the functional components. The functional components may include components for sensory information processing, managing goals and tasks, planning tasks, knowledge bases, and effector information processing. The middleware layer of the intelligent agents may include a component sharing layer. The component sharing layer may search for and identify components running on agents of an agent group that satisfy one or more constraints specified by a requesting component. The component sharing layer may establish a connection between the requesting component and the identified component. The requesting component may utilize services of the identified component to complete a goal or task.

Abstract: Systems and methods may ethically evaluate intelligent systems operating in a real-world environment. The systems and methods may generate a clone of the intelligent system, and test the clone in a simulation environment. If the clone passes the testing, the systems and methods may permit the intelligent system to continue operating in the real-world environment. If the clone fails the testing, the systems and methods may override the intelligent system, such as disabling the intelligent system and assuming control in the real-world environment. The systems and methods may be implemented at a hardware level of a data processing device to prevent interference with the systems and methods by the intelligent system.

Abstract: An intelligent system, such as an autonomous robot agent, includes systems and methods to learn various aspects about a task in response to instructions received from a human instructor, to apply the instructed knowledge immediately during task performance following the instruction, and to instruct other intelligent systems about the knowledge for performing the task. The learning is accomplished free of training the intelligent system. The instructions from the human instructor may be provided in a natural language format and may include deictic references. The instructions may be received while the intelligent system is online, and may be provided to the intelligent system in one shot, e.g., in a single encounter or transaction with the human instructor.