single-rb.php

JRM Vol.12 No.6 pp. 640-649
doi: 10.20965/jrm.2000.p0640
(2000)

Paper:

Semiotic Approach to Perceptual State Construction for Behavior-Based Robot through Recursive and Progressive Deepening Utilization of Memory

Tetsuo Sawaragi* and Satoshi Iwatsu**

*Department of Precision Engineering, Graduate School of Engineering, Kyoto University, Yoshida Honmachi, Sakyo, Kyoto 606-8501, Japan

**Industrial Electronics and Systems Lab., Mitsubishi Electric Corp., Tsukaguchi-Honmachi, Amagasaki, Hyogo 661-8661, Japan

Received:
October 21, 2000
Accepted:
December 8, 2000
Published:
December 20, 2000
Keywords:
Semiotic learning, Reinforcement learning, Concept formation, Inductive learning, State grounding
Abstract

The sources of intelligence for primitively facilitated robotic agents exist in their reconceptualizing capability of what they physically sense from the environment. To implement this capability, we adopt an inductive learning method of conceptual formation for adaptively organizing a state space and develop a new algorithm for a robot to construct its task-relevant state space efficiently through matching encountering states with the similar situations in the past and through generalizing them. We propose a methodology to dynamically increase the resolution of state spaces both adaptively and selectively by applying a concept formation technique from machine learning recursively to a record of a sensorimotor history of a learning agent. By connecting this with conventional reinforcement learning, we showed our algorithm can perform tasks without suffering from hidden state problems in an artificial maze environment, and also present its robustness even for a robot whose perceptual resources are quite restricted and/or bounded.

Cite this article as:
Tetsuo Sawaragi and Satoshi Iwatsu, “Semiotic Approach to Perceptual State Construction for Behavior-Based Robot through Recursive and Progressive Deepening Utilization of Memory,” J. Robot. Mechatron., Vol.12, No.6, pp. 640-649, 2000.
Data files:

*This site is desgined based on HTML5 and CSS3 for modern browsers, e.g. Chrome, Firefox, Safari, Edge, Opera.

Last updated on Sep. 21, 2021