Foundations of Real World Intelligence

Preface
General Introduction
RWI Research Center, Electrotechnical Laboratory
1. Real-World Intelligence and the Real-World Computing Program
Nobuyuki Otsu
2. Theoretical and Algorithmic Foundations of Real-World Intelligence
Hideki Asoh
References
I: Inference and Learning with Graphical Models
RWI Research Center, Electrotechnical Laboratory
3. An Overview of Theoretical Foundation Research in RWI Research Center
Hideki Asoh, Kazuhisa Niki, Koiti Hasida, Shotaro Akaho, Masaru Tanaka, Yoichi Motomura, Tatsuya Niwa, and Kenji Fukumizu
4. BAYONET: Bayesian Network on Neural Network
Yoichi Motomura
5. Multivariate Information Analysis
Kazuhisa Niki, Junpei Hatou, Toshiaki Kawamata, and Ikuo Tahara
6. Dialogue-based Map Learning in an Office Robot
Hideki Asoh, Yoichi Motomura, Toshihiro Matsui, Satoru Hayamizu, and Isao Hara
7. Conclusion
References
II: Approximate Reasoning: Real-World Applications of Graphical Models
RWC Theoretical Foundation SNN Laboratory: Bert Kappen, Stan Gielen, Wim Wiegerinck, Ali Taylan Cemgil, Tom Heskes, Marcel Nijman, and Martijn Leisink
8. Mean Field Approximations
9. Medical Diagnosis
10. Automatic Music Transcription
References
III: Evolutionary Computation and Beyond
RWC Theoretical Foundation GMD Laboratory: Heinz Mühlenbein and Thilo Mahnig
11. Analysis of the Simple Genetic Algorithm
12. The Univariate Marginal Distribution Algorithm (UMDA)
13. The Science of Breeding
14. Graphical Models and Optimization
15. Computing a Bayesian Network from Data
16. System Dynamics Approach to Optimization
17. Three Royal Roads to Optimization
18. Conclusion and Outlook
References
IV: Distributed and Active Learning
RWC Theoretical Foundation NEC Laboratory
19. Distributed Cooperative Bayesian Learning
Kenji Yamanishi
20. Learning Specialist Decision Lists
Atsuyoshi Nakamura
21. The Lob-Pass Problem
Jun'ichi Takeuchi, Naoki Abe, and Shun'ichi Amari
References
V: Computing with Large Random Patterns
RWC Theoretical Foundation SICS Laboratory; Swedish Institute of Computer Science
22. Analogy as a Basis of Computation
Pentti Kanerva
23. The Sparchunk Code: A Method to Build Higher-level Structures in a Sparsely Encoded SDM
Gunnar Sjödin
24. Some Results on Activation and Scaling of Sparse Distributed Memory
Jan Kristoferson
25. A Fast Activation Mechanism for the Kanerva SDM Memory
Roland Karlsson
26. From Words to Understanding
Jussi Karlgren and Magnus Sahlgren
References
Index