Importance

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Abstract

A modular artificial intelligence learning entity (a 'golem') which is replicated many times to form a super-entity that shows intelligent behavior transcending that of its individual constituents. Within the group of golems, individual golems may occupy roles, and are role differentiated, in that structurally identical entities perform different functions and exhibit different behavior depending on their personas and the learning they have completed as driven by other entities. The group of golems is hierarchically organized, in the sense that `superior` entities issue policies to `subordinate` entities. In addition to responding to `sense` input from its environment, the golem responds to policy requirements set by other entities, including its superiors, and in turn sets policy requirements for its subordinates. Actions of the golem are measured for successful compliance with that golem's policies by its superior, who directs the golem's learning process. The super-entity thus gains intelligence through the policy reinforcement occurring in each superior-subordinate relationship. This scheme is well adapted to working over a network with logically separated but communicating golems. Its flexibility allows its application both to single complex problems and to repetitively occurring simple problems. Opportunities for its use arise in operating environments, in simulation and gaming, and in research.

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First Claim