LEARNING DEVICE, LEARNING CONTROL METHOD, AND ITS PROGRAM

• Main Author: ANDO TANICHI
• Format: Patent
• Language: English; Japanese
• Published: 20.12.2018
• Subjects: CALCULATING; CHAMBERS PROVIDED WITH MANIPULATION DEVICES; COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS; COMPUTING; CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE ORDIFFERENT FUNCTION; CONTROL OR REGULATING SYSTEMS IN GENERAL; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; CONTROLLING; COUNTING; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; HAND TOOLS; MANIPULATORS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS ORELEMENTS; PERFORMING OPERATIONS; PHYSICS; PORTABLE POWER-DRIVEN TOOLS; REGULATING; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TOTHE CONTROL OF A PARTICULAR SUB-UNIT; TRANSPORTING; VEHICLES IN GENERAL

To provide a technique for reducing time required for a learning device to achieve the purposes of learning without through manual handling.SOLUTION: A learning device that learns the control of movements related to a predetermined task includes: a learning data reception unit for receiving learning data including the purposes of learning; a neural network for executing learning on the basis of the learning data; and an output unit for outputting the results of learning by the neural network. The neural network executes; first learning for achieving initial stages of the purposes of learning; second learning for learning control to reach a state where movements related to learning cannot be continued, based on the result of the first learning; and third learning for achieving the purposes of learning by elimination of the control for reaching the state where the movements cannot be continued, based on the result of the second learning.SELECTED DRAWING: Figure 1 【課題】人の手を介さずに、学習装置が学習目的を達成するのに要する時間を短縮するための技術を提供する。【解決手段】所定のタスクに係る動作の制御を学習する学習装置であって、学習目的を含む学習データを受け付ける学習データ受付部と、学習データに基づいて、学習を実行するニューラルネットワークと、ニューラルネットワークによる学習結果を出力する出力部と、を備え、ニューラルネットワークは、学習目的の初期段階を達成するための第１学習を実行し、当該第１学習の結果に基づいて、学習に係る動作を続行不能となる状態に至る制御を学習する第２学習を実行し、第２学習の結果に基づいて、続行不能となる状態に至る制御を除外して学習目的を達成するための第３学習を実行する。【選択図】図１