Learning qualitative models by an autonomous robot

  • This thesis introduces and demonstrates a novel method for learning qualitative models of the world by an autonomous robot. The method makes possible generation of qualitative models that can be used for prediction as well as directing the experiments to improve the model. The qualitative models form the knowledge representation of the robot and consists of qualitative trees and non-deterministic finite automaton. An efficient exploration algorithm that lets the robot collect the most relevant learning samples is also introduced. To demonstrate the use of the methodology, representation and algorithm, two experiments are described. The first experiment is conducted using a mobile robot and a ball, where the robot observes the ball and learns the effect of its actions on the observed attributes of the world. The second experiment is conducted using a mobile robot and five boxes, two non-movable boxes and three movable boxes. The robot experiments actively with the objects and observes the changes in the attributes of the world. The main difference with the two experiments is that the first one tries to learn by observation while the second tries to learn by experimentation. In both experiments the robot learns qualitative models from its actions and observations. Although the primary objective of the robot is to improve itself by being able to predict the outcome of its actions, the models Learned were also used at each step of the learning process to direct the experiments so that the model converges to the final model as quickly as possible.

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Metadaten
Document Type:Report
Language:English
Author:Ashok Mohan
Pagenumber:64
ISBN:978-3-96043-002-5
ISSN:1869-5272
URN:urn:nbn:de:hbz:1044-opus-35
DOI:https://doi.org/10.18418/978-3-96043-002-5
Publishing Institution:Hochschule Bonn-Rhein-Sieg
Date of first publication:2008/07/01
Note:
The work described in this article has been [partially] funded by the European Commission’s Sixth Framework Programme under contract no. 029427 as part of the Specific Targeted Research Project XPERO (”Robotic Learning by Experimentation”).
Series (Volume):Technical Report / Hochschule Bonn-Rhein-Sieg University of Applied Sciences, Department of Computer Science (03-2008)
GND Keyword:Robotik
Departments, institutes and facilities:Fachbereich Informatik
Dewey Decimal Classification (DDC):0 Informatik, Informationswissenschaft, allgemeine Werke / 00 Informatik, Wissen, Systeme / 004 Datenverarbeitung; Informatik
Entry in this database:2010/03/10
Licence (Multiple languages):License LogoIn Copyright (Urheberrechtsschutz)