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1.

電子ジャーナル

EJ
出版情報: Institute of Physics , ENGLAND : IOP
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2.

電子ジャーナル

EJ
出版情報: JSTOR Arts and Sciences III , Chicago, Ill. : The MIT Press
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3.

電子ジャーナル

EJ
出版情報: NII-REO OUP Archive Full , ENGLAND : Oxford University Press
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4.

電子ジャーナル

EJ
出版情報: JSTOR Arts and Sciences VI , [Dordrecht] : Springer
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5.

電子ジャーナル

EJ
出版情報: Oxford University Press Journals Current , ENGLAND : Oxford University Press
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6.

電子ブック

EB
SGAI International Conference on Innovative Techniques and Applications of Artificial Intelligence, Max A. Bramer, Frans Coenen, Miltos Petridis, British Computer Society.
出版情報: Springer eBooks Computer Science , Springer London, 2009
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7.

電子ブック

EB
Michael R. Berthold, Christian Borgelt, Frank H?ppner, Frank Klawonn
出版情報: Springer eBooks Computer Science , Springer London, 2010
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目次情報: 続きを見る
Introduction / 1:
Motivation / 1.1:
Data and Knowledge / 1.1.1:
Tycho Brahe and Johannes Kepler / 1.1.2:
Intelligent Data Analysis / 1.1.3:
The Data Analysis Process / 1.2:
Methods, Tasks, and Tools / 1.3:
How to Read This Book / 1.4:
References
Practical Data Analysis: An Example / 2:
The Setup / 2.1:
Data Understanding and Pattern Finding / 2.2:
Explanation Finding / 2.3:
Predicting the Future / 2.4:
Concluding Remarks / 2.5:
Project Understanding / 3:
Determine the Project Objective / 3.1:
Assess the Situation / 3.2:
Determine Analysis Goals / 3.3:
Further Reading / 3.4:
Data Understanding / 4:
Attribute Understanding / 4.1:
Data Quality / 4.2:
Data Visualization / 4.3:
Methods for One and Two Attributes / 4.3.1:
Methods for Higher-Dimensional Data / 4.3.2:
Correlation Analysis / 4.4:
Outlier Detection / 4.5:
Outlier Detection for Single Attributes / 4.5.1:
Outlier Detection for Multidimensional Data / 4.5.2:
Missing Values / 4.6:
A Checklist for Data Understanding / 4.7:
Data Understanding in Practice / 4.8:
Data Understanding in KNIME / 4.8.1:
Data Understanding in R / 4.8.2:
Principles of Modeling / 5:
Model Classes / 5.1:
Fitting Criteria and Score Functions / 5.2:
Error Functions for Classification Problems / 5.2.1:
Measures of Interestingness / 5.2.2:
Algorithms for Model Fitting / 5.3:
Closed Form Solutions / 5.3.1:
Gradient Method / 5.3.2:
Combinatorial Optimization / 5.3.3:
Random Search, Greedy Strategies, and Other Heuristics / 5.3.4:
Types of Errors / 5.4:
Experimental Error / 5.4.1:
Sample Error / 5.4.2:
Model Error / 5.4.3:
Algorithmic Error / 5.4.4:
Machine Learning Bias and Variance / 5.4.5:
Learning Without Bias? / 5.4.6:
Model Validation / 5.5:
Training and Test Data / 5.5.1:
Cross-Validation / 5.5.2:
Bootstrapping / 5.5.3:
Measures for Model Complexity / 5.5.4:
Model Errors and Validation in Practice / 5.6:
Errors and Validation in KNIME / 5.6.1:
Validation in R / 5.6.2:
Data Preparation / 5.7:
Select Data / 6.1:
Feature Selection / 6.1.1:
Dimensionality Reduction / 6.1.2:
Record Selection / 6.1.3:
Clean Data / 6.2:
Improve Data Quality / 6.2.1:
Construct Data / 6.2.2:
Provide Operability / 6.3.1:
Assure Impartially / 6.3.2:
Maximize Efficiency / 6.3.3:
Complex Data Types / 6.4:
Data Integration / 6.5:
Vertical Data Integration / 6.5.1:
Horizontal Data Integration / 6.5.2:
Data Preparation in Practice / 6.6:
Data Preparation in KNIME / 6.6.1:
Data Preparation in R / 6.6.2:
Finding Patterns / 7:
Hierarchical Clustering / 7.1:
Overview / 7.1.1:
Construction / 7.1.2:
Variations and Issues / 7.1.3:
Notion of (Dis-)Similarity / 7.2:
Prototype-and Model-Based Clustering / 7.3:
Density-Based Clustering / 7.3.1:
Self-organizing Maps / 7.4.1:
Frequent Pattern Mining and Association Rules / 7.5.1:
Deviation Analysis / 7.6.1:
Finding Patterns in Practice / 7.7.1:
Finding Patterns with KNIME / 7.8.1:
Finding Patterns in R / 7.8.2:
Finding Explanations / 7.9:
Decision Trees / 8.1:
Bayes Classifiers / 8.1.1:
Regression / 8.2.1:
Two Class Problems / 8.3.1:
Rule learning / 8.4:
Prepositional Rules / 8.4.1:
Inductive Logic Programming or First-Order Rules / 8.4.2:
Finding Explanations in Practice / 8.5:
Finding Explanations with KNIME / 8.5.1:
Using Explanations with R / 8.5.2:
Finding Predictors / 8.6:
Nearest-Neighbor Predictors / 9.1:
Artifical Neural Networks / 9.1.1:
Support Vector Machines / 9.2.1:
Ensemble Methods / 9.3.1:
Finding Predictors in Practice / 9.4.1:
Finding Predictors with KNIME / 9.5.1:
Using Predictors in R / 9.5.2:
Evaluation and Deployment / 10:
Evaluation / 10.1:
Deployment and Monitoring / 10.2:
Statistics / A:
Terms and Notation / A.1:
Descriptive Statistics / A.2:
Tabular Representations / A.2.1:
Graphical Representations / A.2.2:
Characteristic Measures for One-Dimensional Data / A.2.3:
Characteristic Measures for Multidimensional Data / A.2.4:
Principal Component Analysis / A.2.5:
Probability Theory / A.3:
Probability / A.3.1:
Basic Methods and Theorems / A.3.2:
Random Variables / A.3.3:
Characteristic Measures of Random Variables / A.3.4:
Some Special Distributions / A.3.5:
Inferential Statistics / A.4:
Random Samples / A.4.1:
Parameter Estimation / A.4.2:
Hypothesis Testing / A.4.3:
The R Project / B:
Installation and Overview / B.1:
Reading Files and R Objects / B.2:
R Functions and Commands / B.3:
Libraries/Packages / B.4:
R Workspace / B.5:
Finding Help / B.6:
Knime / B.7:
Building Workflows / C.1:
Example Flow / C.3:
R Integration / C.4:
Index / Appendix A:
Introduction / 1:
Motivation / 1.1:
Data and Knowledge / 1.1.1:
8.

電子ブック

EB
Danny Weyns
出版情報: Springer eBooks Computer Science , Springer Berlin Heidelberg, 2010
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目次情報: 続きを見る
Introduction / 1:
Software Architecture and Middleware / 1.1:
Software Architecture / 1.1.1:
Middleware / 1.1.2:
Agent-Oriented Methodologies / 1.2:
Case Study / 1.3:
Overview of the Book / 1.4:
Overview of Architecture-Based Design of Multi-Agent Systems / 2:
General Overview of the Approach / 2.1:
Architectural Design in the Development Life Cycle / 2.1.1:
Steps of Architecture-Based Design of Multi-Agent Systems / 2.1.2:
Functional and Quality Attribute Requirements / 2.2:
Architectural Design / 2.3:
Architectural Patterns / 2.3.1:
ADD Process / 2.3.2:
Middleware Support for Multi-Agent Systems / 2.4:
Documenting Software Architecture / 2.5:
Architectural Views / 2.5.1:
Architectural Description Languages / 2.5.2:
Evaluating Software Architecture / 2.6:
From Software Architecture to Downstream Design and Implementation / 2.7:
Summary / 2.8:
Capturing Expertise in Multi-Agent System Engineering with Architectural Patterns / 3:
Situated Multi-Agent Systems / 3.1:
Single-Agent Systems / 3.1.1:
Multi-Agent Systems / 3.1.2:
Target Domain of the Pattern Language for Situated Multi-Agent Systems / 3.2:
Overview of the Pattern Language / 3.3:
Pattern Template / 3.4:
Virtual Environment / 3.5:
Primary Presentation / 3.5.1:
Architectural Elements / 3.5.2:
Interface Descriptions / 3.5.3:
Design Rationale / 3.5.4:
Situated Agent / 3.6:
Selective Perception / 3.6.1:
Roles and Situated Commitments / 3.7.1:
Free-Flow Trees Extended with Roles and Situated Commitments / 3.8.1:
Protocol-Based Communication / 3.9:
Architectural Design of Multi-Agent Systems / 3.9.1:
Designing and Documenting Multi-Agent System Architectures / 4.1:
Designing and Documenting Architecture in the Development Life Cycle / 4.1.1:
Inputs and Outputs of ADD / 4.1.2:
Overview of the ADD Activities / 4.1.3:
The Domain of Automated Transportation Systems / 4.2:
Business Case / 4.2.2:
System Requirements / 4.2.3:
General Overview of the Design / 4.3:
Challenges at the Outset / 4.3.1:
The System and Its Environment / 4.3.2:
Design Process / 4.3.3:
High-Level Design / 4.3.4:
Architecture Documentation / 4.4:
Introduction to the Architecture Documentation / 4.4.1:
Deployment View / 4.4.2:
Module Uses View / 4.4.3:
Collaborating Components View / 4.4.4:
Middleware for Distributed Multi-Agent Systems / 4.5:
Middleware Support for Distributed, Decentralized Coordination / 5.1:
Middleware in Distributed Software Systems / 5.1.1:
Middleware in Multi-Agent Systems / 5.1.2:
Scope of the Middleware and Requirements / 5.2:
Objectplaces / 5.2.2:
Views / 5.2.3:
Coordination Roles / 5.2.4:
Middleware Architecture / 5.3:
High-Level Module Decomposition / 5.3.1:
Group Formation / 5.3.2:
View Management / 5.3.3:
Role Activation / 5.3.4:
Collision Avoidance in the AGV Transportation System / 5.4:
Collision Avoidance / 5.4.1:
Collision Avoidance Protocol / 5.4.2:
Software Architecture: Communicating Processes for Collision Avoidance / 5.4.3:
Task Assignment / 5.5:
Schedule-Based Task Assignment / 6.1:
FiTA: Field-Based Task Assignment / 6.2:
Coordination Fields / 6.2.1:
Adaptive Task Assignment / 6.2.2:
Dealing With Local Minima / 6.2.3:
DynCNET Protocol / 6.3:
Monitoring the Area of Interest / 6.3.1:
Convergence / 6.3.3:
Synchronization Issues / 6.3.4:
Evaluation / 6.4:
Test Setting / 6.4.1:
Test Results / 6.4.2:
Tradeoff Analysis / 6.4.3:
Evaluation of Multi-Agent System Architectures / 6.5:
Evaluating Multi-Agent System Architectures with ATAM / 7.1:
Architecture Evaluation in the Development Life Cycle / 7.1.1:
Objectives of a Multi-Agent System Architecture Evaluation / 7.1.2:
Overview of the ATAM Activities / 7.1.3:
AGV Transportation System for a Tea Processing Warehouse / 7.2:
Evaluation Process / 7.2.2:
Quality Attribute Workshop / 7.2.3:
Analysis of Architectural Approaches / 7.2.4:
Reflection on ATAM for Evaluating a Multi-Agent System Architecture / 7.3:
ATAM Follow-Up and Demonstrator / 7.4:
Related Approaches / 7.5:
Architectural Approaches and Multi-Agent Systems / 8.1:
Architectural Styles / 8.1.1:
Reference Models and Architectures for Multi-Agent Systems / 8.1.2:
Middleware for Mobile Systems / 8.2:
Work Related to Views / 8.2.1:
Work Related to Coordination Roles / 8.2.2:
Scheduling and Routing of AGV Transportation Systems / 8.3:
AI and Robotics Approaches / 8.3.1:
Multi-Agent System Approaches / 8.3.2:
Conclusions / 9:
Reflection on Architecture-Based Design of Multi-Agent Systems / 9.1:
It Works! / 9.1.1:
Reflection on the Project with Egemin / 9.1.2:
Lessons Learned and Challenges / 9.2:
Dealing with Quality Attributes / 9.2.1:
Designing a Multi-Agent System Architecture / 9.2.2:
Integrating a Multi-Agent System with Its Software Environment / 9.2.3:
Impact of Adopting a Multi-Agent System / 9.2.4:
?-ADL Specification of the Architectural Patterns / A:
Language Constructs / A.1:
Virtual Environment Pattern / A.2:
Situated Agent Pattern / A.3:
Synchronization in the DynCNET Protocol / B:
Synchronization of Abort and Bound Messages / B.1:
Synchronization of Scope Dynamics / B.2:
Overview / C:
Invariant / C.2:
Maintaining the Invariant / C.3:
Glossary
References
Index
Introduction / 1:
Software Architecture and Middleware / 1.1:
Software Architecture / 1.1.1:
9.

電子ブック

EB
Honghai Liu, Dongbing Gu, Honghai Liu
出版情報: Springer eBooks Computer Science , Springer London, 2010
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目次情報: 続きを見る
Programming-by-Demonstration of Robot Motions / Alexander Skoglund ; Boyko Iliev ; Rainer Palm1:
Grasp Recognition by Fuzzy Modeling and Hidden Markov Models / Bourhane Kadmiry2:
Distributed Adaptive Coordinated Control of Multi-Manipulator Systems Using Neural Networks / Zeng-Guang Hou ; Long Cheng ; Min Tan ; Xu Wang3:
A New Framework for View-Invariant Human Action Recognition / Xiaofei Ji ; Honghai Liu ; Yibo Li4:
Using Fuzzy Gaussian Inference and Genetic Programming to Classify 3D Human Motions / Mehdi Khoury5:
Obstacle Detection Using Cross-Ratio and Disparity Velocity / Huiyu Zhou ; Andrew M. Wallace ; Patrick R. Green6:
Learning and Vision-Based Obstacle Avoidance and Navigation / Jiandong Tian ; Yandong Tang7:
A Fraction Distortion Model for Accurate Camera Calibration and Correction / Yonghuai Liu ; Ala Al-Obaidi ; Anthony Jakas ; Junjie Liu8:
A Leader-Follower Flocking System Based on Estimated Flocking Center / Zongyao Wang ; Dongbing Gu9:
A Behavior Based Control System for Surveillance UAVs / John Oyekan ; Bowen Lu ; Bo Li ; Huosheng Hu10:
Hierarchical Composite Anti-Disturbance Control for Robotic Systems Using Robust Disturbance Observer / Lei Guo ; Xin-Yu Wen ; Xin Xin11:
Autonomous Navigation for Mobile Robots with Human-Robot Interaction / James Ballantyne ; Edward Johns ; Salman Valibeik ; Charence Wong ; Guang-Zhong Yang12:
Prediction-Based Perceptual System of a Partner Robot for Natural Communication / Naoyuki Kubota ; Kenichiro Nishida13:
Index
Programming-by-Demonstration of Robot Motions / Alexander Skoglund ; Boyko Iliev ; Rainer Palm1:
Grasp Recognition by Fuzzy Modeling and Hidden Markov Models / Bourhane Kadmiry2:
Distributed Adaptive Coordinated Control of Multi-Manipulator Systems Using Neural Networks / Zeng-Guang Hou ; Long Cheng ; Min Tan ; Xu Wang3:
10.

電子ブック

EB
International Conference on Agents and Artificial Intelligence, Joaquim Filipe, Ana Fred, Bernadette Sharp
出版情報: Springer eBooks Computer Science , Springer Berlin Heidelberg, 2010
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Invited Speakers
Past, Present and Future of Ambient Intelligence and Smart Environments / Juan Carlos Augusto
Artificial Intelligence / Part I:
Modelling Social Learning of Adolescence-Limited Criminal Behaviour / Tibor Bosse ; Charlotte Gerritsen ; Michel C.A. Klein
How Do Emotions Induce Dominant Learners' Mental States Predicted from Their Brainwaves? / Alicia Heraz ; Claude Frasson
A Multiagent Semantics for the Game Description Language / Stephan Schiffel ; Michael Thielscher
Verifying Context-Dependent Reduction Relations for Knowledge Specifications / Alexei Sharpanskykh ; Jan Treur
Combining Artificial Intelligence Techniques for the Training of Power System Control Centre Operators / Luiz Faria ; António Silva ; Zita Vale ; Carlos Ramos
Adaptive State Space Abstraction Using Neuroevolution / Robert Wright ; Nathaniel Gemelli
Goal-Based Game Tree Search for Complex Domains / Viliam Lisý ; Branislav Bosanský ; Michal Jakob ; Michal Pechoucek
Generating Incomplete Data with DataZapper / Yingying Wen ; Kevin B. Korb ; Ann E. Nicholson
Extending Learning Vector Quantization for Classifying Data with Categorical Values / Ning Chen ; Nuno C. Marques
Action Knowledge Acquisition with Opmaker2 / T.L. McCluskey ; S.N. Cresswell ; N.E. Richardson ; M.M. West
Application of Hidden Topic Markov Models on Spoken Dialogue Systems / Hamid R. Chinaei ; Brahim Chaib-draa ; Luc Lamontagne
Gossip Galore: An Embodied Conversational Agent for Collecting and Sharing Pop Trivia from the Web / Feiyu Xu ; Peter Adolphs ; Hans Uszkoreit ; Xiwen Cheng ; Hong Li
Biosignal Based Discrimination between Slight and Strong Driver Hypovigilance by Support-Vector Machines / David Sommer ; Martin Golz ; Udo Trutschel ; Dave Edwards
Agents / Part II:
Tiered Logic for Agents in Contexts / Rosalito Perez Cruz ; John Newsome Crossley
HomeManager: Testing Agent-Oriented Software Engineering in Home Intelligence / Ambra Molesini ; Enrico Denti ; Andrea Omicini
Developing Multi-Agent Systems through Integrating Prometheus, INGENIAS and ICARO-T / Antonio Fernández-Caballero ; José M. Gascueña
An Efficient Winner Approximation for a Series of Combinatorial Auctions / Naoki Fukuta ; Takayuki Ito
How to Integrate Personalization and Trust in an Agent Network / Laurent Lacomme ; Yues Demazeau ; Valérie Camps
Modeling Two Stage Preventive Medical Checkup Systems with Social Science Approaches / Andreas Martischnig ; Siegfried Voessner ; Gerhard Stark
Translating Discrete Multi-Agents Systems into Cellular Automata: Application to Diffusion-Limited Aggregation / Antoine Spicher ; Nazim Fatès ; Olivier Simonin
Using Values to Turn Agents into Characters / Rossana Damiano ; Vincenzo Lombardo
Author Index
Invited Speakers
Past, Present and Future of Ambient Intelligence and Smart Environments / Juan Carlos Augusto
Artificial Intelligence / Part I:
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