Browse > Article

Behavioral motivation-based Action Selection Mechanism with Bayesian Affordance Models  

Lee, Sang-Hyoung (Department of computer Science & Engineering, Hanyang University)
Suh, Il-Hong (Department of computer Science & Engineering, Hanyang University)
Publication Information
Journal of the Institute of Electronics Engineers of Korea SC / v.46, no.4, 2009 , pp. 7-16 More about this Journal
Abstract
A robot must be able to generate various skills to achieve given tasks intelligently and reasonably. The robot must first learn affordances to generate the skills. An affordance is defined as qualities of objects or environments that induce actions. Affordances can be usefully used to generate skills. Most tasks require sequential and goal-oriented behaviors. However, it is usually difficult to accomplish such tasks with affordances alone. To accomplish such tasks, a skill is constructed with an affordance and a soft behavioral motivation switch for reflecting goal-oriented elements. A skill calculates a behavioral motivation as a combination of both presently perceived information and goal-oriented elements. Here, a behavioral motivation is the internal condition that activates a goal-oriented behavior. In addition, a robot must be able to execute sequential behaviors. We construct skill networks by using generated skills that make action selection feasible to accomplish a task. A robot can select sequential and a goal-oriented behaviors using the skill network. For this, we will first propose a method for modeling and learning Bayesian networks that are used to generate affordances. To select sequential and goal-oriented behaviors, we construct skills using affordances and soft behavioral motivation switches. We also propose a method to generate the skill networks using the skills to execute given tasks. Finally, we will propose action-selection-mechanism to select sequential and goal-oriented behaviors using the skill network. To demonstrate the validity of our proposed methods, "Searching-for-a-target-object", "Approaching-a-target-object", "Sniffing-a-target-object", and "Kicking-a-target-object" affordances have been learned with GENIBO (pet robot) based on the human teaching method. Some experiments have also been performed with GENIBO using the skills and the skill networks.
Keywords
Action-Selection-Mechanism; Bayesian network; Affordance; Skill; Behavioral motivation;
Citations & Related Records
Times Cited By KSCI : 1  (Citation Analysis)
연도 인용수 순위
1 S. Russell and P. Norvig, Artificial Intelligence: A Modern Approach, Prentice Hall, ch. 14, 2003
2 D. Heckerman, 'A tutorial on learning with Bayesian network,' Technical report, MS Research Advanced Technology Division, 1995
3 http://www.dasarobot.com
4 E. de Sevin and D. Thalmann, 'A Motivation Model of Action Selection for Virtual Humans,' in Proc. of Computer Graphics International 2005 (CGI'05), Stony Brook, NY, USA, June 22-24, 2005   DOI
5 E. Lazkano, B. Sierra, A. Astigarrage, and J. M. Otzeta, 'On the use of Bayesian networks to develop behaviours for mobile robots,' Robotics and Autonomous Systems, vol. 55, pp. 253-265, 2007   DOI   ScienceOn
6 O. Lebeltel, P. Bessiere, J. Diard, and E. Mazer, 'Bayesian Robot Programming,' Autonomous Robots, pp. 49-79, 2004   DOI   ScienceOn
7 A. Stoychev, 'Behavior-Grounded Representation of Tool Affordances,' in Proc. of the 2005 IEEE International Conference on Robotics and Auto- mation, Barcelona, Spain, April, 2005
8 J. J. Gibson, The Ecological Approach to Visual Perception, Houghton Mifflin, Boston, 1979
9 W. Lidwell, K. Holden, and J. Butler, Universal Principles of Design, Rockport Publishers, pp. 20, 2003
10 L. Montesano, M. Lopes, A. Bernardino, and J. Santos-Victor, 'Modeling Affordances using Bayesian networks,' in Proc. of IEEE/RSJ Int. Conference in Intelligent Robots and Systems, San Diego, USA, October, 2007   DOI
11 Dweck and S. Carol, 'Motivational processes affecting learning,' in Proc. of American Psychological Association, vol. 41, pp. 1040-1048, 1986   DOI
12 P. Perez-Paredes and M. Sanchez-Tornel, Handbook of Research on E-Learning Methodologies for Language Acquisition, Information Science Reference, pp. 3, 2008
13 A. Dearden and Y. Demiris, 'Learning Forward Models for Robots,' in Proc. of the International Joint Conference on Artificial Intelligence, pp. 1440-1445, Edinburgh, , 2005
14 M. Raubal and R. Moratz, 'A Functional Model for Affordance-Based Agents,' In: E. Rome et al. (Eds.): Affordance-Based Robot Control, LNAI 4760, pp. 91-105, 2008   DOI   ScienceOn
15 T. Kiyoharu, K. Kenta, and I. Katsumi, 'Approach to Artificial Skill from Affordance Theory-Memory and Embodiment,' in Proc. of Journal of the Robotics Society of Japan, vol. 22, no. 7, pp. 892-900, 2004   DOI
16 S. Lee, I. H. Suh, and W. Y. Kwon, 'A Motivation-based Action-Selection-Mechanism involving Reinforcement Learning,' in Proc. of International Journal of Control, Automation, and Systems, vol. 6, no. 6, pp. 904-914, December, 2008   과학기술학회마을   ScienceOn
17 E. Rome, J. Hertzberg, G. Dorffiner, and P. Doherty, 'Towards Affordance-based Robot Control,' in Proc. of Dagstuhl Seminar 062031, June 5-9, 2006   DOI
18 E. Beaudry, Y. Brosseau, C. Cote, C. Raievsky, D. Letourneau, F. Kabanza, and F. Michaud, 'Reactive Planning in a Motivated Behavioral Architecture,' in Proc. of American Association for Artificial Intelligence, pp.1242-1247, 2005