| 摘要 |
An agent learning apparatus comprises a sensor ( 301 ) for acquiring a sense input, an action controller ( 307 ) for creating an action output in response to the sense input and giving the action output to a controlled object, an action state evaluator ( 303 ) for evaluating the behavior of the controlled object, a selective attention mechanism ( 304 ) for storing the action output and the sense input corresponding to the action output in one of the columns according to the evaluation, calculating a probability model from the action outputs stored in the columns, and outputting, as a learning result, the action output related to a newly given sense input in the column where the highest confidence obtained by applying the newly given sense input to the probability model is stored. By thus learning, the selective attention mechanism ( 304 ) obtains a probability relationship between the sense input and the column. An action output is calculated on the basis of the column evaluated as a stable column. As a result, the dispersion of the action output is quickly minimized, and thereby the controlled object can be stabilized.
|
