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Abstract

A software agent which performs autonomous learning in a real-world environment, implemented in a case-based reasoning system and coupled to a sensor for gathering information from, and to an effector for manipulating, its environment. A case base which is tuned in response to an evaluation of how well the agent is operating in that environment. A memory of cases, the contents of that memory being determined by a genetic technique, including producing new cases which may never have been encountered in the environment, evaluating cases in response to a history of previous matches and in response to an external stimulus, and selecting a limited set of cases which provides a preferred model of the environment. A selection technique based on multiple measures, such as match quality, case accuracy, or case utility, which may induce experimentation by the autonomous agent, such as by employing a random or pseudorandom effect in selecting cases, and which may also distinguish between those actions which solve problems and those actions which gather further information so as to better solve problems. Multiple autonomous agents may form a collective entity and may cooperate to select an action to be performed by that collective entity.

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First Claim