PEXIS: Probabilistic experience representation based adaptive interaction system for personal robots

Systems and Computers in Japan

Volumn 35, Issue 6, 2004, Pages 98-109

  Inamura, Tetsunari ^a,b   Inaba, Masayuki ^b   Inoue, Hirochika ^b  

^a JAPAN SCIENCE AND TECHNOLOGY AGENCY   (Japan)

^b UNIVERSITY OF TOKYO   (Japan)

Author keywords

Adaptation; Bayesian Network; Human robot interaction; Learning; Personal robot

Indexed keywords

KNOWLEDGE BASED SYSTEMS; LEARNING SYSTEMS; MATHEMATICAL MODELS; MECHATRONICS; MOBILE ROBOTS; OBJECT ORIENTED PROGRAMMING; VOCABULARY CONTROL;

ADAPTATION; BAYESIAN NETWORKS; HUMAN-ROBOT INTERACTION; LEARNING; PERSONAL ROBOTS;

INTELLIGENT ROBOTS;

EID: 2442536995     PISSN: 08821666     EISSN: None     Source Type: Journal    
DOI: 10.1002/scj.10034     Document Type: Review

References (20)

1. Kuniyoshi Y, Inaba M, Inoue H. Learning by watching: Extracting reusable task knowledge from visual observation of human performance. IEEE Trans Robotics Autom 1994;10:799-822.
2. Pearl J. Probabilistic reasoning in intelligent systems. Morgan Kaufmann; 1988.
3. Basye K, Dean T, Kirman J, Lejter M. A decision-theoretic approach to planning perception and control. IEEE Expert 1992;7:58-65.
4. Asoh H, Motomura Y, Hara I, Akaho S, Hayamizu S, Matsui T. Acquiring a probabilistic map with dialogue-based learning. Proc ROBOLEARN-96.
5. Heckerman D, Mamdani A, Wellman MP. Real-world applications of Bayesian networks. Commun ACM 1995;38:24-57.
6. Akiba T, Tanaka H. A Bayesian approach for user modeling in dialogue systems. Technical Report, Department of Computer Science, Tokyo Institute of Technology, 1994.
7. Charniak E. Bayesian networks without tears. AI Magazine, Winter, p 50-63, 1991.
8. Russell SJ, Norvig P. Artificial intelligence: A modern approach. Prentice-Hall; 1995.
9. Tanaka M, Motoyoshi F, Yamanashi M. Cognitive psychology 3 using LISP: Language comprehension. University of Tokyo Press; 1983.
10. Matsui T. Multithread object-oriented language EusLisp for parallel and asynchronous programming in robotics. Workshop on Concurrent Object-based Systems, IEEE 6th Symposium on Parallel and Distributed Processing, 1994.
11. Pomerleau DA. Neural network perception for mobile robot guidance. Kluwer Academic Publishing; 1993.
12. Asada M, Uchibe E, Hosoda K. Cooperative behavior acquisition for mobile robots in dynamically changing real worlds via vision-based reinforcement learning and development. Artif Intell 1999;110:275-292.
13. Shibata T, Matsumoto Y, Kuwahara T, Inaba M, Inoue H. Development and integration of generic components for a teachable vision-based mobile robot. IEEE/ASME Trans Mechatron 1996;1:230-236.
14. Inamura T, Inaba M, Inoue H. Acquisition of probabilistic behavior decision model based on the interactive teaching method. Proc 9th Int Conf Advanced Robotics, p 523-528, 1999.
15. Inamura T, Inaba M, Inoue H. Natural language interaction between users and robots with personalized vocabulary acquisition: Function 3 of PEXIS, a human-robot interaction system. Proc Fifth Robotics Symposia, p 146-151, 2000.
16. Inamura T, Okabe M, Inaba M, Inoue H. Query and confirmation generation for users based on certainty factor of task achievement. Japan Society of Mechanical Engineers Robotics Mechatronics Conference '00, p 2A1-76-112.
17. Okada T, Hiraki K, Yasunishi H. A robot command learning system Acorn-II and an evaluation of this system. J Japanese Soc Artif Intell 1994;9:882-889.
18. Nagao T. Multimodal human-computer interaction - Agent orientation and real-world orientation. J Soc Instrum Control Eng 1996;35:65-70.
19. Matsui H. A plan for a circumstantial robot to be called a meddlesome robot. bit 1997;29:4-11.
20. Maes P. Agents that reduce work and information overload. Commun ACM 1994;37:30-40.