%{search_type} search results

• On the History of the Growth Optimal Portfolio (M M Christensen)
• Empirical Log-Optimal Portfolio Selections: A Survey (L Gyorfi et al.)
• Log-Optimal Portfolio Selection with Proportional Transaction Costs (L Gyorfi & H Walk)
• Log-Optimal Portfolio with Short Selling and Leverage (M Horvath & A Urban)
• Nonparametric Sequential Prediction of Stationary Time Series (L Gyorfi & G Ottuscak)
• Empirical Pricing American Put Options (L Gyorfi & A Telcs).
• (source: Nielsen Book Data)

This volume investigates algorithmic methods based on machine learning in order to design sequential investment strategies for financial markets. Such sequential investment strategies use information collected from the market's past and determine, at the beginning of a trading period, a portfolio; that is, a way to invest the currently available capital among the assets that are available for purchase or investment.The aim is to produce a self-contained text intended for a wide audience, including researchers and graduate students in computer science, finance, statistics, mathematics, and engineering.
(source: Nielsen Book Data)

• On the History of the Growth Optimal Portfolio (M M Christensen)
• Empirical Log-Optimal Portfolio Selections: A Survey (L Gyorfi et al.)
• Log-Optimal Portfolio Selection with Proportional Transaction Costs (L Gyorfi & H Walk)
• Log-Optimal Portfolio with Short Selling and Leverage (M Horvath & A Urban)
• Nonparametric Sequential Prediction of Stationary Time Series (L Gyorfi & G Ottuscak)
• Empirical Pricing American Put Options (L Gyorfi & A Telcs).
• (source: Nielsen Book Data)

This volume investigates algorithmic methods based on machine learning in order to design sequential investment strategies for financial markets. Such sequential investment strategies use information collected from the market's past and determine, at the beginning of a trading period, a portfolio; that is, a way to invest the currently available capital among the assets that are available for purchase or investment.The aim is to produce a self-contained text intended for a wide audience, including researchers and graduate students in computer science, finance, statistics, mathematics, and engineering.
(source: Nielsen Book Data)

• Tile Page; Preface; Organization; Contents; An Assistance Infrastructure for Open MAS; On the Modularity of Industrial SAT Instances; On 2SAT-MaxOnes with Unbalanced Polarity: From Easy Problems to Hard MaxClique Problems; Experimenting with the Instances of the MaxSAT Evaluation; Lazy Learning Methods for Quality of Life Assessment in People with Intellectual Disabilities; Probabilistic Appearance-Based Mapping and Localization Using the Feature Stability Histogram; Towards an Efficient Use of Resources in All-Optical Networks; Depth of Valleys Accumulation Algorithm for Object Detection.

This book is a collection of the papers accepted for presentation at the 14th International Conference of the Catalan Association for Artificial Intelligence (CCIA 2011), held at the University of Lleida in October 2011. From the 28 papers submitted, 24 were accepted for inclusion in this book and for oral presentation at the conference after a careful reviewing process. The conference also featured invited talks by two outstanding international researchers: Christian Bessiere of CNRS, University of Montpellier, France, and Bart Selman of Cornell University, USA. The CCIA conference brings tog.

• Machine generated contents note: Invited Talks
• Playing Games with Games / Michael Wooldridge
• User-Generated AI for Interactive Digital Entertainment / Ashwin Ram
• Perspectives on Artificial Intelligence in a Business Environment / Peter Nordin
• Tutorials
• Probabilistic Decision Graphs for Optimization Under Uncertainty / Finn Verner Jensen
• Coordinating Multi-Agent Systems Using Social Laws / Thomas Agotnes
• Full Papers
• Machine Learning
• User-Oriented Assessment of Classification Model Understandability / Niklas Lavesson
• Concurrent Learning of Large-Scale Random Forests / Henrik Bostrom
• Learning Multi-Label Predictors Under Sparsity Budget / Tapio Salakoski
• Machine Learning Methods for Spatial Clustering on Precision Agriculture Data / Rudolf Kruse
• Planning
• On Constraint Models for Parallel Planning: The Novel Transition Scheme / Roman Bartak
• Trajectory Planning on Grids: Considering Speed Limit Constraints / Lukas Chrpa
• Safe Reinforcement Learning for Continuous Spaces Through Lyapunov-Constrained Behavior / Erik Kyrkjebø
• Parallel Monte Carlo Tree Search on GPU / Reiji Suda
• Exploiting Global Properties in Path-Consistency Applied on SAT / Pavel Surynek
• Applications
• Incremental Stream Clustering for Anomaly Detection and Classification / Jan Ekman
• Respiratory Motion Prediction: A Fuzzy Logic Approach / Manish Kakar
• Case-Based Reasoning in a System Architecture for Intelligent Fish Farming / Agnar Aamodt
• Overview of Fault Detection Techniques in Automated Monitoring Systems / Shengtong Zhong
• Robotics and Cognition
• View-Independent Human Gait Recognition Using CBR and HMM / Odd Erik Gundersen
• Self-Exploration of Autonomous Robots Using Attractor-Based Behavior Control and ABC-Learning / Matthias Kubisch.

• pt. 1. Basic principles
• pt. 2. Continuous time swarms
• pt. 3. Discrete time swarms
• pt. 4. Swarm based optimization methods.

Swarming species such as flocks of birds or schools of fish exhibitfascinating collective behaviors during migration and predatoravoidance. Similarly, engineered multi-agent dynamic systems such as groups of autonomous ground, underwater, or air vehicles ("vehicle swarms") exhibit sophisticated collective behaviors while maneuvering. In this book we show how to model and control a wide range of suchmulti-agent dynamic systems and analyze their collective behaviorusing both stability theoretic and simulation-based approaches. Inparticular, we investigate problems such as group aggregat

• Title page; Preface; Conference Organization; Contents; Invited Talks; Agents and Multi-Agents Systems; AI Real-World Applications; Data Mining, Machine Learning and Soft Computing; Logics, Constraint Satisfaction and Reasoning; Robotics, Vision and Perception; Subject Index; Author Index.

Artificial Intelligence (AI) is one of the hottest areas of research with hundreds of potentially real life applications and one of the disciplines that is most intertwined with all sorts of fields related to human experience. A wide range of disciplines, sectors and technologies could benefit from the results of AI breakthroughs; from robotics to search engines; healthcare or environment; screening, monitoring or supervision techniques. This book compiles the 33 accepted papers presented at the 13th International Conference of the Catalan Association for Artificial Intelligence. Although most o.

• New Hybrid Intelligent Systems to Solve Linear and Quadratic Optimization Problems and Increase Guaranteed Optimal Convergence Speed of Recurrent ANN
• A Novel Optimization Algorithm Based on Reinforcement Learning
• The Use of Opposition for Decreasing Function Evaluations in Population-Based Search
• Search Procedure Exploiting Locally Regularized Objective Approximation. A Convergence Theorem for Direct Search Algorithms
• Optimization Problems with Cardinality Constraints
• Learning Global Optimization Through a Support Vector Machine Based Adaptive Multistart Strategy
• Multi-Objective Optimization Using Surrogates
• A Review of Agent-Based Co-Evolutionary Algorithms for Multi-Objective Optimization
• A Game Theory-Based Multi-Agent System for Expensive Optimisation Problems
• Optimization with Clifford Support Vector Machines and applications
• A Classification method based on principal component analysis and differential evolution algorithm applied for prediction diagnosis from clinical EMR heart data sets
• An Integrated Approach to Speed Up GA-SVM Feature Selection Model
• Computation in Complex Environments;
• Project Scheduling: Time-Cost Tradeoff Problems
• Systolic VLSI and FPGA Realization of Artificial Neural Networks
• Application of Coarse-Coding Techniques for Evolvable Multirobot Controllers.

This collection of recent studies spans a range of computational intelligence applications, emphasizing their application to challenging real-world problems. Covers Intelligent agent-based algorithms, Hybrid intelligent systems, Machine learning and more.

• Invited Presentations.- Property Testing: A Learning Theory Perspective.- Spectral Algorithms for Learning and Clustering.- Unsupervised, Semisupervised and Active Learning I.- Minimax Bounds for Active Learning.- Stability of k-Means Clustering.- Margin Based Active Learning.- Unsupervised, Semisupervised and Active Learning II.- Learning Large-Alphabet and Analog Circuits with Value Injection Queries.- Teaching Dimension and the Complexity of Active Learning.- Multi-view Regression Via Canonical Correlation Analysis.- Statistical Learning Theory.- Aggregation by Exponential Weighting and Sharp Oracle Inequalities.- Occam's Hammer.- Resampling-Based Confidence Regions and Multiple Tests for a Correlated Random Vector.- Suboptimality of Penalized Empirical Risk Minimization in Classification.- Transductive Rademacher Complexity and Its Applications.- Inductive Inference.- U-Shaped, Iterative, and Iterative-with-Counter Learning.- Mind Change Optimal Learning of Bayes Net Structure.- Learning Correction Grammars.- Mitotic Classes.- Online and Reinforcement Learning I.- Regret to the Best vs. Regret to the Average.- Strategies for Prediction Under Imperfect Monitoring.- Bounded Parameter Markov Decision Processes with Average Reward Criterion.- Online and Reinforcement Learning II.- On-Line Estimation with the Multivariate Gaussian Distribution.- Generalised Entropy and Asymptotic Complexities of Languages.- Q-Learning with Linear Function Approximation.- Regularized Learning, Kernel Methods, SVM.- How Good Is a Kernel When Used as a Similarity Measure?.- Gaps in Support Vector Optimization.- Learning Languages with Rational Kernels.- Generalized SMO-Style Decomposition Algorithms.- Learning Algorithms and Limitations on Learning.- Learning Nested Halfspaces and Uphill Decision Trees.- An Efficient Re-scaled Perceptron Algorithm for Conic Systems.- A Lower Bound for Agnostically Learning Disjunctions.- Sketching Information Divergences.- Competing with Stationary Prediction Strategies.- Online and Reinforcement Learning III.- Improved Rates for the Stochastic Continuum-Armed Bandit Problem.- Learning Permutations with Exponential Weights.- Online and Reinforcement Learning IV.- Multitask Learning with Expert Advice.- Online Learning with Prior Knowledge.- Dimensionality Reduction.- Nonlinear Estimators and Tail Bounds for Dimension Reduction in l 1 Using Cauchy Random Projections.- Sparse Density Estimation with ?1 Penalties.- ?1 Regularization in Infinite Dimensional Feature Spaces.- Prediction by Categorical Features: Generalization Properties and Application to Feature Ranking.- Other Approaches.- Observational Learning in Random Networks.- The Loss Rank Principle for Model Selection.- Robust Reductions from Ranking to Classification.- Open Problems.- Rademacher Margin Complexity.- Open Problems in Efficient Semi-supervised PAC Learning.- Resource-Bounded Information Gathering for Correlation Clustering.- Are There Local Maxima in the Infinite-Sample Likelihood of Gaussian Mixture Estimation?.- When Is There a Free Matrix Lunch?.
• (source: Nielsen Book Data)

This book constitutes the refereed proceedings of the 20th Annual Conference on Learning Theory, COLT 2007, held in San Diego, CA, USA in June 2007. It covers unsupervised, semisupervised and active learning, statistical learning theory, inductive inference, regularized learning, kernel methods, SVM, online and reinforcement learning, learning algorithms and limitations on learning, dimensionality reduction, as well as open problems.
(source: Nielsen Book Data)

• Title page; ECCAI Member Societies; Conference Organization; ECAI Programme Committee; Additional Reviewers; Preface; Contents; Invited Talks; Socially Intelligent Robots; Managing Diversity in Knowledge; The Truth About Defaults; SmartWeb: Getting Answers on the Go; Papers; Cognitive Modelling; Constraints and Search; Distributed AI/Agents; Knowledge Representation and Reasoning; Machine Learning; Natural Language Processing; Planning and Scheduling; PAIS; Perception; Robotics; Posters; Cognitive Modeling; Constraints and Search; Distributed AI/Agents; Knowledge Representation and Reasoning.

In the summer of 1956, John McCarthy organized the famous Dartmouth Conference which is commonly viewed as the founding event for the field of Artificial Intelligence. Knowledge Representation & Reasoning is traditionally strong in Europe and remains the biggest area of ECAI 2006.

• A new way to acquire knowledge, H.-Y. Wang
• an SPN knowledge representation scheme, J. Gattiker and N. Bourbakis
• on the deep structures of word problems and their construction, F. Gomez
• resolving conflicts in inheritance reasoning with statistical approach, C. Lee
• integrating high and low level computer vision for scene understanding, R. Malik and S. So
• the evolution of commercial AI tools - the first decade, F. Hayes-Roth
• reengineering - the AT generation - billions on the table, J.S. Minor, Jr.
• an intelligent tool for discovering data dependencies in relational DBS, P. Gavaskar and F. Golshani
• a case-based reasoning (CBR) tool to assist traffic flow, B. Das and S. Bayles
• a study of financial expert system based on flops, T. Kaneko and K. Takenaka
• an associative data parallel compilation model for tight integration of high performance knowledge retrieval and computation, A. Bansal
• software automation - from silly to intelligent, X. Jiafu et al
• software engineering using artificial intelligence - the knowledge based software assistant, D. White
• knowledge based derivation of programmes from specs, T. Weight et al
• automatic functional model generation for parallel fault design error simulations, S.E. Chang and S. Szygenda
• visual reverse engineering using SPN for automated diagnosis and functional simulation of digital circuits, J. Gattiker and S. Mertoguno
• the impact of AI in VLSI design automation, M. Mortazavi and N. Bourbakis
• the automated acquisition of subcategorization of verbs, nouns and adjectives from sample sentences, F. Gomez
• general method for planning and rendezvous problems, K. Trovato
• learning to improve path planning performance, P.C. Chen
• incremental adaptation as a method to improve reactive behaviour, A.J. Hendriks and D.M. Lyons
• an SPN-neural planning methodology for coordination of multiple robotic arms with constrained placement, N. Bourbakis and A. Tascillo.
• (source: Nielsen Book Data)

Covering artificial intelligence and automation, these contributions discuss: the evolution of AI tools; an SPN knowledge representation scheme; software engineering using AI; the impact of AI in VLSI design automation; incremental adaptation as a method to improve reactive behaviour; and more.
(source: Nielsen Book Data)

• An intelligent image-based computer-aided education system: the prototype BIRDS / A.A. David, O. Thiery & M. Crehange
• PLAYMAKER: a knowledge-based approach to characterizing hydrocarbon plays / G. Biswas [and others]
• An expert system for interpreting mesoscale features in oceanographic satellite images / N. Krishnakumar [and others]
• An expert system for tuning particle beam accelerators / D.L. Lager, H.R. Brand & W.J. Maurer
• Expert system approach to assessments of bleeding predispositions in tonsillectomy/adenoidectomy patients / N.J. Pizzi & J.M. Gerrard
• Expert system approach using graph representation and analysis for variable-stroke internal-combustion engine design / S.N.T. Shen, M.S. Chew & G.F. Issa
• A comparison of two new techniques for conceptual clustering / S.L. Crawford & S.K. Souders
• Querying an object-oriented database using free language / P. Trigano [and others]
• Adaptive planning for air combat maneuvering / I.C. Hayslip, J.P. Rosenking & J. Filbert
• AM/AG model: a hierarchical social system metaphor for distributed problem solving / D.G. Shin & J. Leone
• CAUSA
• A tool for model-based knowledge acquisition / W. Dilger & J. Moller
• PRIOPS: a real-time production system architecture for programming and learning in embedded systems / D.E. Parson & G.D. Blank.

This volume contains a well-balanced set of applications and theory papers in artificial intelligence advances. The applications papers each discuss a system that is (or is close to being) a fielded system that solves real problems using one or more AI techniques. They cover areas such as education, physics, energy, control, medicine and mechanical engineering.The theory papers, representing recent advances in various theoretical aspects of AI technology, concern themselves with "building block" issues, i.e. theories, algorithms, architectures, and software tools that can or will be used for modules within future systems. The topics covered are: clustering, natural language, adaptive algorithms, distributed processing, knowledge acquisition, and systems programming.
(source: Nielsen Book Data)

• Identification of the Conjugate Pair to Estimating Object Distance: An Application of the Ant Colony Algorithm.-Design of Palm Acupuncture Points Indicator.- Low-rank Representation and Locality-constrained Regression for Robust Low-Resolution Face Recognition.- Face Recognition Benchmark with ID Photos.- Scene Relighting using a Single Reference Image through Material Constrained Layer Decomposition.- Applying Adaptive Actor-critic Learning to Human Upper Lime Lifting Motion.- A Demand-based Allocation Mechanism for Virtual Machine.- A Joint Hierarchy Model for Action Recognition Using Kinect.- QoS-Based Medical Program Evolution.- An Improved 3D Surface Reconstruction Method based on Three Wavelength Phase Shift Profilometry.- The Research on the Lung Tumor Imaging based on the Electrical Impedance Tomography.- Combining CNN and MRF for Road Detection.- The Increasing of Discrimination Accuracy of Waxed Apples based on Hyperspectral Imaging Optimized by Spectral Correlation Analysis.- A Diffeomorphic Demons Approach to Statistical Shape Modeling.
• (source: Nielsen Book Data)

This book highlights selected papers presented at the 2nd International Symposium on Artificial Intelligence and Robotics 2017 (ISAIR2017), held in Nakamura Centenary Memorial Hall, Kitakyushu, Japan on November 25-26, 2017. Today, the integration of artificial intelligence and robotic technologies has become a topic of growing interest for both researchers and developers from academic fields and industries worldwide, and artificial intelligence is poised to become the main approach pursued in next-generation robotics research. The rapidly growing number of artificial intelligence algorithms and big data solutions has significantly extended the number of potential applications for robotic technologies. However, it also poses new challenges for the artificial intelligence community. The aim of this symposium is to provide a platform for young researchers to share the latest scientific achievements in this field, which are discussed in these proceedings.
(source: Nielsen Book Data)

• 1. Swarm Intelligence and Bio-Inspired Computation: An Overview
• 2. Review and Analysis of Swarm-intelligence Based Algorithms 3. Levy Flights and Global Optimization 4. Self-Adaptive Memetic Firefly Algorithm 5. Modelling and Simulation of Labor Division in An Ant Colony: A Problem-Oriented Approach 6. Particle Swarm Optimization and Their Variants: Convergence and Applications 7. A Survey of Swarm Algorithms Applied to Discrete Optimization Problems 8. A Comprehensive Survey of Test Functions for Global Optimization 9. Binary Bat Algorithm for Feature Selection 10. Intelligent Music Composition 11. The Development and Applications of the Cuckoo Search Algorithm 12. Bio-Inspired Models and the Semantic Web 13. Discrete Firefly Algorithm for Travelling Salesman Problem: A New Movement Scheme 14. Modelling to Generate Alternatives Using Biologically-Inspired Algorithms 15. Structural Optimization Using Krill Herd Algorithm 16. Artificial Plant Optimization Algorithm 17. Genetic Algorithms for the Berth Allocation Problem in Real Time 18. Opportunities and Challenges of Integrating Bio-Inspired Optimization and Data Mining Algorithms 19. Improvement of PSO Algorithm by Memory Based Gradient Search: Application in Inventory Management.
• (source: Nielsen Book Data)

• 5.2.2.3 Time-Dependent Response Threshold Model
• 5.2.3 Some Analysis
• 5.3 Modeling and Simulation of Ant Colony's Labor Division with Multitask
• 5.3.1 Background Analysis
• 5.3.2 Design and Implementation of Ant Colony's Labor Division Model with Multitask
• 5.3.2.1 Design of Ant Colony's Labor Division Model with Multitask
• Environmental Stimuli
• Agent Attributes
• Probability of Participation and Exit
• Simulation Principle
• 5.3.2.2 Implementation of Ant Colony's Labor Division Model with Multitask
• 5.3.3 Supply Chain Virtual Enterprise Simulation
• 5.3.3.1 Simulation Example and Parameter Settings
• 5.3.3.2 Simulation Results and Analysis
• 5.3.4 Virtual Organization Enterprise Simulation
• 5.3.4.1 Simulation Example and Parameter Settings
• 5.3.4.2 Simulation Results and Analysis
• 5.3.5 Discussion
• 5.4 Modeling and Simulation of Ant Colony's Labor Division with Multistate
• 5.4.1 Background Analysis
• 5.4.2 Design and Implementation of Ant Colony's Labor Division Model with Multistate
• 5.4.2.1 Design of Ant Colony's Labor Division Model with Multistate
• Stimulus Values in Multitask Environment
• Relative Environment Stimulus Value sαβ and Relative Threshold θαβ
• Agent State Transformation
• 5.4.2.2 Implementation of Ant Colony's Labor Division Model with Multistate
• 5.4.3 Simulation Example of Ant Colony's Labor Division Model with Multistate
• 5.4.3.1 Simulation and Experiment Environment
• 5.4.3.2 Parameters of the Simulation Model
• 5.4.3.3 Simulation Results
• 5.4.3.4 Analysis of Results
• 5.5 Modeling and Simulation of Ant Colony's Labor Division with Multiconstraint
• 5.5.1 Background Analysis
• 5.5.2 Design and Implementation of Ant Colony's Labor Division Model with Multiconstraint
• 5.5.2.1 Design of Ant Colony's Labor Division Model with Multiconstraint.

Swarm Intelligence and bio-inspired computation have become increasing popular in the last two decades. Bio-inspired algorithms such as ant colony algorithms, bat algorithms, bee algorithms, firefly algorithms, cuckoo search and particle swarm optimization have been applied in almost every area of science and engineering with a dramatic increase of number of relevant publications. This book reviews the latest developments in swarm intelligence and bio-inspired computation from both the theory and application side, providing a complete resource that analyzes and discusses the latest and future trends in research directions. It can help new researchers to carry out timely research and inspire readers to develop new algorithms. With its impressive breadth and depth, this book will be useful for advanced undergraduate students, PhD students and lecturers in computer science, engineering and science as well as researchers and engineers.
(source: Nielsen Book Data)

• Preface; Acknowledgments;
• Chapter 1. Introduction; 1.1 Background; 1.2 Cognitive Architectures; 1.3 Soar; 1.4 Research Strategy; 1.5 Preview of Chapters 2-14;
• Chapter 2. Requirements for Cognitive Architectures; 2.1 Characteristics of Environments, Tasks, and Agents; 2.2 Architectural Requirements;
• Chapter 3. The Problem-Space Computational Model; 3.1 Task Environments; 3.2 The Problem-Space Framework; 3.3 Knowledge Search; 3.4 Problem-Space Computational Models; 3.5 Impasses and Substates; 3.6 Using Multiple Sources of Knowledge; 3.7 Acquiring Knowledge; 3.8 Alternative PSCMs

• Chapter 4. Soar as an Implementation of the PSCM4.1 Production Systems; 4.2 Mapping Production Systems onto the PSCM; 4.3 The Soar Processing Cycle; 4.4 Demonstrations of Basic PSCM; 4.5 Discussion; 4.6 Analysis of Requirements;
• Chapter 5. Impasses and Substates: The Basis for Complex Reasoning; 5.1 Impasses; 5.2 Substates; 5.3 Problem Solving in Substates; 5.4 Substate Results; 5.5 Maintaining Consistency; 5.6 Demonstrations of Impasses and Substates; 5.7 Discussion; 5.8 Analysis of Requirements;
• Chapter 6. Chunking; 6.1 Chunking in Soar; 6.2 Implications of Chunking in Soar

• 6.3 Demonstrations of Chunking6.4 Assumptions Inherent to Chunking;
• Chapter 7. Tuning Procedural Knowledge: Reinforcement Learning; 7.1 Reinforcement Learning in Soar; 7.2 Learning over Large State Spaces; 7.3 Demonstrations of Reinforcement Learning; 7.4 Analysis of Requirements;
• Chapter 8. Semantic Memory; 8.1 Semantic Memory in Soar; 8.2 Encoding and Storage; 8.3 Retrieval; 8.4 Demonstrations of Semantic Memory; 8.5 Analysis of Requirements;
• Chapter 9. Episodic Memory; 9.1 Episodic Memory in Soar; 9.2 Encoding and Storage; 9.3 Retrieval; 9.4 Use of Episodic Memory

• 9.5 Demonstrations of Episodic Memory9.6 Comparison of Episodic Memory and Semantic Memory; 9.7 Analysis of Requirements;
• Chapter 10. Visuospatial Processing with Mental Imagery; 10.1 Visual and Spatial Representations; 10.2 Visuospatial Domains; 10.3 SVS; 10.4 Demonstrations of Spatial and Visual Imagery; 10.5 Analysis of Requirements;
• Chapter 11. Emotion; 11.1 Appraisal Theories of Emotion; 11.2 Abstract Functional Cognitive Operations; 11.3 Unifying Cognitive Control and Appraisal; 11.4 Emotion, Mood, and Feeling; 11.5 Emotion and Reinforcement Learning

• 11.6 Demonstrations of Emotion Processing11.7 Analysis of Requirements;
• Chapter 12. Demonstrations of Multiple Architectural Capabilities; 12.1 Learning to Use Episodic Memory with Reinforcement Learning; 12.2 Using Mental Imagery with Reinforcement Learning; 12.3 Diverse Forms of Action Modeling; 12.4 Analysis of Requirements;
• Chapter 13. Soar Applications; 13.1 Applications; 13.2 TacAir-Soar; 13.3 Imagining TacAir-Soar 2.0;
• Chapter 14. Conclusion; 14.1 Soar from a Structural Perspective; 14.2 Soar from a Functional Perspective; 14.3 Evaluating Soar on Architectural Requirements; References

The definitive presentation of Soar, one AI's most enduring architectures, offering comprehensive descriptions of fundamental aspects and new components. In development for thirty years, Soar is a general cognitive architecture that integrates knowledge-intensive reasoning, reactive execution, hierarchical reasoning, planning, and learning from experience, with the goal of creating a general computational system that has the same cognitive abilities as humans. In contrast, most AI systems are designed to solve only one type of problem, such as playing chess, searching the Internet, or scheduling aircraft departures. Soar is both a software system for agent development and a theory of what computational structures are necessary to support human-level agents. Over the years, both software system and theory have evolved. This book offers the definitive presentation of Soar from theoretical and practical perspectives, providing comprehensive descriptions of fundamental aspects and new components. The current version of Soar features major extensions, adding reinforcement learning, semantic memory, episodic memory, mental imagery, and an appraisal-based model of emotion. This book describes details of Soar's component memories and processes and offers demonstrations of individual components, components working in combination, and real-world applications. Beyond these functional considerations, the book also proposes requirements for general cognitive architectures and explicitly evaluates how well Soar meets those requirements.
(source: Nielsen Book Data)

• 1. Introduction
• 2. Machine learning and statistics overview
• 3. Performance measures I
• 4. Performance measures II
• 5. Error estimation
• 6. Statistical significance testing
• 7. Data sets and experimental framework
• 8. Recent developments
• 9. Conclusion
• Appendix A: statistical tables
• Appendix B: additional information on the data
• Appendix C: two case studies.
• (source: Nielsen Book Data)

The field of machine learning has matured to the point where many sophisticated learning approaches can be applied to practical applications. Thus it is of critical importance that researchers have the proper tools to evaluate learning approaches and understand the underlying issues. This book examines various aspects of the evaluation process with an emphasis on classification algorithms. The authors describe several techniques for classifier performance assessment, error estimation and resampling, obtaining statistical significance as well as selecting appropriate domains for evaluation. They also present a unified evaluation framework and highlight how different components of evaluation are both significantly interrelated and interdependent. The techniques presented in the book are illustrated using R and WEKA, facilitating better practical insight as well as implementation. Aimed at researchers in the theory and applications of machine learning, this book offers a solid basis for conducting performance evaluations of algorithms in practical settings.
(source: Nielsen Book Data)

• Acknowledgments Douglas Hofstadter: Foreword Stefano Franchi and Francesco Bianchini: Introduction: On the Historical Dynamics of Cognitive Science: a View from the Periphery The cybernetic suburb Stefan Franchi: Life, Death, and Resurrection of the Homeostat Peter Galison: The Ontology of the Enemy: Norbert Wiener and the Cybernetic Vision Peter Asaro: Computers as Models of the Mind: On Simulations, Brains, and the Design of Computers AI's peripheries Claudio Pogliano: At the Periphery of the Rising Empire: the Case of Italy (1945-1968) Patrice Maniglier: Processing Cultures: "Structuralism" in the History of Artificial Intelligence Slava Gerovitch: Artificial Intelligence With a National Face: American and Soviet Cultural Metaphors for Thought Margins of computations Francesco Bianchini: The Cartesian-Leibnizian Turing Test Maurizio Matteuzzi: Turing Computability and Leibniz Computability Christopher M. Kelty: Logical Instruments: Regular Expressions, AI, and Thinking about Thinking At the thresholds of computability Solomon Feferman: Goedel, Nagel, Minds, and Machines Rossella Lupacchini: Entangling Effective Procedures: From Logic Machines to Quantum Automata Giorgio Sandri: Turing 1948 vs. Goedel 1972 Works Cited Index About the Contributors.
• (source: Nielsen Book Data)

The book brings into relief the variety of approaches and disciplines that have informed the quest for a theory of cognition. The center of interest are the historical, geographical, and theoretical peripheries of classic AI's mainstream research program. The twelve chapters bring back into focus the variety of strategies and theoretical questions that researchers explored while working toward a scientific theory of cognition and pre-cognition. The volume is organized in four parts, each one including three essays. The first one deals with cybernetics, the approach that may be considered as the most important periphery of classic AI research. The second part focuses on the geographical periphery of AI research. It examines how the theories and techniques developed on AI's home ground were translated into countries with different cultures and traditions: Italy, France, and the Soviet Union. The third part focuses on AI's periphery understood in the cultural and historical meaning of the term. It contains essays that locate some of the central concepts of AI, like representation and computability, within a broader philosophical (Descartes, Aristotle, Leibniz) and technical background (programming theory and practice). The fourth and final part of the volume is focused directly on the limitation of Turing's classic computability theory and its possible alternatives, some of which were studied in the early years of AI's research (e.g. Ashby's re-entrant information model), while others have been intensely studied in recent times (quantum automata).
(source: Nielsen Book Data)

• Foreword; Preface; Contents; Argumentation Theory: A Very Short Introduction; Douglas Walton; Description Logic; Part I Abstract Argument Systems; Pietro Baroni and Massimiliano Giacomin; Semantics of Abstract Argument Systems; Bayesian Networks; Abstract Argumentation and Values; Trevor Bench-Capon and Katie Atkinson; Bipolar abstract argumentation systems; Claudette Cayrol and Marie-Christine Lagasquie-Schiex; Complexity of Abstract Argumentation; Paul E. Dunne and Michael Wooldridge; Proof Theories and Algorithms for Abstract Argumentation Frameworks; Sanjay Modgil and Martin Caminada.

Examines the intersection between two fields of inquiry, including Argumentation Theory and Artificial Intelligence. This book presents an overview of key concepts in argumentation theory and of formal models of argumentation in AI. It offers a review of the foundational issues in argumentation and formal argument modeling.

• The History and Future of Stiquito, a Hexapod Insectoid Robot.- Learning Legged Locomotion.- Salamandra Robotica: a Biologically Inspired Amphibious Robot that Swims and Walks.- Multi-Locomotion Robot: Novel Concept, Mechanism and Control of Bio-Inspired Robot.- Self-Regulatory Hardware: Evolutionary Design for Mechanical Passivity on a Pseudo Passive Dynamic Walker.- Perception for Action in Roving Robots: a Dynamical System Approach.- Nature-Inspired Single-Electron Computers.- Tribolon: Water Based Self-Assembly Robots.- Artificial Symbiosis in Ecobots.- The Phi-Bot: A Robot Controlled by a Slime Mould.- Reaction-Diffusion Controllers for Robots.- Index.
• (source: Nielsen Book Data)

Hopping, climbing and swimming robots, nano-size neural networks, motorless walkers, slime mould and chemical brains - "Artificial Life Models in Hardware" offers unique designs and prototypes of life-like creatures in conventional hardware and hybrid bio-silicon systems. Ideas and implementations of living phenomena in non-living substrates cast a colorful picture of state-of-art advances in hardware models of artificial life.
(source: Nielsen Book Data)

• I. Introduction to dataset shift
• 1. When training and test sets are different: characterizing learning transfer / Amos Storkey
• 2. Projection and projectability / David Corfield
• II. Theoretical views on dataset and covariate shift
• 3. Binary classification under sample selection bias / Matthias Hein
• 4. On Bayesian transduction: implications for the covariate shift problem / Lars Kai Hansen
• 5. On the training/test distributions gap: a data representation learning framework / Shai Ben-David
• III. Algorithms for covariate shift
• 6. Geometry of covariate shift with applications to active learning / Takafumi Kanamori and Hidetoshi Shimodaira
• 7. A conditional expectation approach to model selection and active learning under covariate shift / Masashi Sugiyama, Neil Rubens and Klaus-Robert Muller
• 8. Covariate shift by kernel mean matching / Arthur Grellon, Alex Smola, Jiayuan Huang, Marcel Schmittfull, Karsten Borgwardt and Bernhard Scholkopf
• 9. Discriminative learning under covariate shift with a single optimization problem / Steffen Bickel, Michael Bruckner and Tobias Scheffer
• 10. An adversarial view of covariate shift and a minimax approach / Amir Globerson, Choon Hui Teo, Alex Smola and Sam Roweis
• IV. Discussion
• 11. Author comments / Hidetoshi Shimodaira, Masashi Sugiyama, Amos Storkey, Arthur Gretton and Shai-Ben David.

An overview of recent efforts in the machine learning community to deal with dataset and covariate shift, which occurs when test and training inputs and outputs have different distributions. Dataset shift is a common problem in predictive modeling that occurs when the joint distribution of inputs and outputs differs between training and test stages. Covariate shift, a particular case of dataset shift, occurs when only the input distribution changes. Dataset shift is present in most practical applications, for reasons ranging from the bias introduced by experimental design to the irreproducibility of the testing conditions at training time. (An example is -email spam filtering, which may fail to recognize spam that differs in form from the spam the automatic filter has been built on.) Despite this, and despite the attention given to the apparently similar problems of semi-supervised learning and active learning, dataset shift has received relatively little attention in the machine learning community until recently. This volume offers an overview of current efforts to deal with dataset and covariate shift. The chapters offer a mathematical and philosophical introduction to the problem, place dataset shift in relationship to transfer learning, transduction, local learning, active learning, and semi-supervised learning, provide theoretical views of dataset and covariate shift (including decision theoretic and Bayesian perspectives), and present algorithms for covariate shift. Contributors Shai Ben-David, Steffen Bickel, Karsten Borgwardt, Michael Bruckner, David Corfield, Amir Globerson, Arthur Gretton, Lars Kai Hansen, Matthias Hein, Jiayuan Huang, Choon Hui Teo, Takafumi Kanamori, Klaus-Robert Muller, Sam Roweis, Neil Rubens, Tobias Scheffer, Marcel Schmittfull, Bernhard Schoelkopf Hidetoshi Shimodaira, Alex Smola, Amos Storkey, Masashi Sugiyama.
(source: Nielsen Book Data)

• FOUNDATIONS COMPUTATIONAL INTELLIGENCE EVOLUTIONARY COMPUTATION CONCEPTS AND PARADIGMS EVOLUTIONARY COMPUTATION IMPLEMENTATIONS NEURAL NETWORK CONCEPTS AND PARADIGMS NEURAL NETWORK IMPLEMENTATIONS FUZZY SYSTEMS CONCEPTS AND PARADIGMS FUZZY SYSTEMS IMPLEMENTATIONS COMPUTATIONAL INTELLIGENCE IMPLEMENTATIONS PERFORMANCE METRICS ANALYSIS AND EXPLANATION CASE STUDY SUMMARIES.
• (source: Nielsen Book Data)

Computational Intelligence: Concepts to Implementations provides the most complete and practical coverage of computational intelligence tools and techniques to date. This book integrates various natural and engineering disciplines to establish Computational Intelligence. This is the first comprehensive textbook on the subject, supported with lots of practical examples. It asserts that computational intelligence rests on a foundation of evolutionary computation. This refreshing view has set the book apart from other books on computational intelligence. This book lays emphasis on practical applications and computational tools, which are very useful and important for further development of the computational intelligence field. Focusing on evolutionary computation, neural networks, and fuzzy logic, the authors have constructed an approach to thinking about and working with computational intelligence that has, in their extensive experience, proved highly effective. The book moves clearly and efficiently from concepts and paradigms to algorithms and implementation techniques by focusing, in the early chapters, on the specific con. It explores a number of key themes, including self-organization, complex adaptive systems, and emergent computation. It details the metrics and analytical tools needed to assess the performance of computational intelligence tools. The book concludes with a series of case studies that illustrate a wide range of successful applications. This book will appeal to professional and academic researchers in computational intelligence applications, tool development, and systems.
(source: Nielsen Book Data)