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Foundations of Real World Intelligence cover

Foundations of Real World Intelligence

edited by Yoshinori Uesaka, Pentti Kanerva, and Hideki Asoh

In 1992 Japan's Ministry of International Trade and Industry (MITI) began a research program in Real World Computing, as a successor to the Fifth Generation Computing program of the previous decade, complementing the fifth-generation approach. Its objective is to lay a foundation and to pursue the technical realization of humanlike flexible and intelligent information processing. This book collects results of ten years of original research by six research laboratories, three Japanese and three European, whose research focus has been the theoretical and algorithmic foundations of intelligence as manifested in the real world an in our dealing with it.

Real-world intelligent systems handle complex, uncertain, dynamic, multimodal information in real time. Both explicit and implicit information are important. Hence we need to develop a novel integrated framework of representing knowledge and making inferences based in it. It is impossible to preprogram all the knowledge needed for coping with the variety and complexity of real environments, and therefore learning and adaptation are keys to intelligence. Learning is a kind of metaprogramming strategy. Instead of writing programs for specific tasks, we must write programs that modify themselves based on a system's interaction with its environment.

The book includes chapters on Inference and learning with graphical models, Approximate reasoning, Evolutionary computation and beyond, Methodology of distributed and active learning, Symbol pattern integration using multilinear functions, and Computing with large random patterns. The treatment is mathematically rigorous, yet accessible, and the discussion of issues is of general interest to an educated reader at large. The book provides excellent reading for graduate courses in Computer Science, Cognitive Science, Artificial Intelligence, and Applied Statistics.

Read an excerpt from this book.

Yoshinori Uesaka is a professor at Science University of Tokyo. Pentti Kanerva is a senior researcher at Swedish Institute of Computer Science. Hideki Asoh is a senior researcher at Electrotechnical Laboratory in Tsukuba City, Japan.

Contents

  • Preface
  • General Introduction RWI Research Center, Electrotechnical Laboratory
    • 1 Real-World Intelligence and the Real-World Computing Program Nobuyuki Otsu
      • 1.1 Outline of the RWC Program
      • 1.2 Real-World Intelligence
      • 1.3 Concluding Remarks
    • 2 Theoretical and Algorithmic Foundations of Real-World Intelligence Hideki Asoh
      • 2.1 Objective
      • 2.2 Approach
      • 2.3 Research Issues
      • 2.4 Organizations of R&D and This Book
      • 2.5 Concluding Remarks
    • 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
      • 3.1 Models and Algorithms
      • 3.2 Frameworks of Learning
    • 4 BAYONET: Bayesian Network on Neural Network Yoichi Motomura
      • 4.1 Bayesian Networks Based on Neural Networks
      • 4.2 Implementation
      • 4.3 Application
      • 4.4 Conclusion
    • 5 Multivariate Information Analysis Kazhisa Niki, Junpei Hatou, Toshiaki Kawamata, and Ikou Tahara
      • 5.1 Expression of Multivariate Analysis
      • 5.2 Simulation
      • 5.3 Structure Analyses of fMRI Data
      • 5.4 Extended Functional Connectivity Analysis
      • 5.5 Conclusion
    • 6 Dialogue-based Map Learning in an Office Robot Hideki Asoh, Yoichi Motomura, Toshihiro Matsui, Satoru Hayamizu, and Isao Hara
      • 6.1 Dialogue-based Map Acquisition
      • 6.2 System and Experiment
      • 6.3 Discussion
      • 6.4 Related Work
      • 6.5 Conclusion and Future Work
    • 7 Conclusion
    • References
  • II Approximate and 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
      • 8.1 Mean Field Approximation with Structure
      • 8.2 Boltzmann Machine Learning Using Mean Field Theory and Linear Response Correction
      • 8.3 Second-order Approximations for Probability Models
      • 8.4 Discussion
    • 9 Medical Diagnosis
      • 9.1 Probabilistic Modeling in the Medical Domain
      • 9.2 Promedas, a Demonstrations DSS
      • 9.3 Discussion
    • 10 Automatic Music Transcription
      • 10.1 Dynamical Systems and the Kalman Filter
      • 10.2 Tempogram Representation
      • 10.3 Model Training
      • 10.4 Evaluation
      • 10.5 Discussion and Conclusions
  • III Evolutionary Computation and Beyond RWC Theoretical Foundation GMD Laboratory Heinz Mühlenbein and Thilo Mahnig
    • 11 Analysis of the Simple Genetic Algorithm
      • 11.1 Definitiosn
      • 11.2 Proportionate Selection
      • 11.3 Recombinaton
      • 11.4 Selection and Recombination
      • 11.5 Schema Analysis Demystified
    • 12 The Univariate Marginal Distribution Algorithm (UMDA)
      • 12.1 Definitiosn of UMDA
      • 12.2 Computing the Average Fitness
    • 13 The Science of Breeding
      • 13.1 Single Trait Theory
      • 13.2 Tournament Selection
      • 13.3 Analytical Results for Linear Functions
      • 13.4 Numerical Results for UMDA
      • 13.5 Royal Road Function
      • 13.6 Multimodal Functions Suited for UMDA Optimization
      • 13.7 Deceptive Functions
      • 13.8 Numerical Investigations of the Science of Breeding
    • 14 Graphical Models and Optimization
      • 14.1 Bolzmann Selection and Convergence
      • 14.2 Factorization of the Distribution and the FDA
      • 14.3 A New Annealing Schedule Schedule for the Boltzmann Distribution
      • 14.4 Finite Populations
      • 14.5 Population Size, Mutations, and Bayesian Prior
      • 14.6 Constraint Optimization Problems
    • 15 Computing a Bayesian Network from Data
      • 15.1 LFDA—Learning a Bayesian Factorization
      • 15.2 Optimization, Dependencies, and Search Distributions
    • 16 System Dynamics Approach to Optimization
      • 16.1 The Replicator Equation
      • 16.2 Boltzmann Selection and the Replicator Equation
      • 16.3 Some System Dynamics Equations for Optimization
      • 16.4 Optimization of Binary Functions
    • 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
      • 19.1 Introduction
      • 19.2 Plain Model
    • 20 Learning Special Decision Lists Atsuyoshi Nakamura
      • 20.1 Preliminaries
      • 20.2 Algorithm S-Loss-Update
      • 20.3 Algorithm
      • 20.4 Algorithm S-Fixed-Share-Update
    • 21 The Lob-Pass Problem Jun'ichi Takeuchi, Naoki Abe, and Shun-ichi Amari
      • 21.1 Preliminaries
      • 21.2 Upper Bounds on the Expected Regret
      • 21.3 Concluding Remarks
    • 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
      • 22.1 Computer as a Brain and Brain as a Computer
      • 22.2 Artificial Neural Nets as Biologically Motivated Models of Computing
      • 22.3 Description vs. Explanation
      • 22.4 The Brain as a Computer for Modeling the World, and Our Model of the Brain's Computing
      • 22.5 Pattern Space Representations
      • 22.6 Simple Analogical Retreival
      • 22.7 Learning from Examples
      • 22.8 Toward a New Model of Computing
    • 23 The Sparchunk Code: A Method to Building Higher-level Structures in a Sparsely Encoded SDM Gunnar Sjödin
      • 23.1 Encoding Higher-Level Concepts
      • 23.2 The SDM Model
      • 23.3 Nonscaling for a Constant Error Probability
      • 23.4 Sparse Coding
      • 23.5 The Sparchunk Code
      • 23.6 Clean-up of the Sparchunk Code
      • 23.7 Summary
      • 23.8 Appendix
    • 24 Some Results on Activation and Scaling of Sparse Distributed Memory Jan Kristoferson
      • 24.1 Different Activation Probabilities for Writing and Reading?
      • 24.2 Scaling Up the Memory
    • 25 A Fast Activation Mechanism for the Kanerva SDM Memory Roland Karlsson
      • 25.1 The Jaeckel Selected-Coordinate Design
      • 25.2 The New Selected-Coordinate Design
      • 25.3 Results
      • 25.4 Conclusion
    • 26 From Words to Understanding Jussi Karlgren and Magnus Sahlgren
      • 26.1 The Meaning of ‘Meaning’
      • 26.2 A Case in Point: Information Access
      • 26.3 Words as Content Indicators
      • 26.4 Latent Semantic Analysis
      • 26.5 Random Indexing
      • 26.6 What Is Text, from the Perspective of Linguistics
      • 26.7 The TOEFL-Test
      • 26.8 Expeimental Set-Up
      • 26.9 Results and Analysis
      • 26.10 Some Cognititive Implications
      • 26.11 Implications for Information Access
      • 26.12 Meaning in Text
    • References
  • Index

9/1/2001

ISBN (Paperback): 1575863383 (9781575863382)
ISBN (Cloth): 1575863391 (9781575863399)
ISBN (Electronic): 1684000165 (9781684000166)

Subject: Artificial intelligence; Neural networks; Evolutionary programming

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