A Guided Tour of Artificial Intelligence Research: Vol. 3 Interfaces and Applications of AI

General Presentation of the Guided Tour of Artificial Intelligence Research
Contents
Preface: Interfaces and Applications of Artificial Intelligence
Foreword: From Cybernetics to Artificial Intelligence
	Analog and Digital: An Unfinished Debate
	A Case Study: The Paradox of Operational Units in Analog Computing
	Old and New Singularities
Theoretical Computer Science: Computability, Decidability and Logic
	1 Introduction
		1.1 Theoretical Computer Science and the Core Themes in IA
		1.2 What We Pick and Choose in Theoretical Computer Science
	2 Emergence of the Notion of Computability
		2.1 Discrete Computation Models Based on Mathematics
		2.2 Discrete Computation Models Based on Sequential Machines
		2.3 Discrete Computation Models Based on Random Access Machines
		2.4 Chomsky Type-0 Grammars
		2.5 A Model for Discrete Computation with Massive Parallelism: Cellular Automata
		2.6 A Model Far Apart: Lambda-Calculus
		2.7 Church Thesis, Diverse Formulations
		2.8 Gandy\'s Axiomatization of Church Thesis
	3 Computability Theory
		3.1 Unhalting Computations
		3.2 Three Wonderful Theorems in Partial Computability
		3.3 Two ``Negative\'\' Results in Partial Computability
	4 Formalization of the Notion of Algorithm
		4.1 Denotational versus Operational
		4.2 Getting Operational Completeness
		4.3 Gurevich\'s Axiomatization of the Notion of Algorithm
		4.4 Operational Completeness and Recursion
	5 Deduction and Computation: The Algorithmic Nature of Constructive (i.e. Intuitionistic) Proofs
		5.1 Constructive Proofs as a Programming Language
		5.2 Formal Logical Systems
		5.3 Constructive Proofs and Their Denotations
		5.4 Reduction of Constructive Proofs
		5.5 Brouwer–Heyting–Kolmogorov Interpretation
		5.6 Theories
		5.7 Other Extensions
		5.8 Cut Elimination and Consistency Results
	6 Decidable versus Undecidable
		6.1 Automatic Deduction and Decidability of Logical Theories
		6.2 A Few Other Problems
		6.3 Deciding…with High Probability To Be Correct
	7 Computability on Reals
		7.1 Computable Analysis
		7.2 Blum, Shub and Smale Machines
		7.3 Shannon\'s General Purpose Analog Computer
		7.4 Computation Models with Continuous Time
		7.5 Unifying These Models?
		7.6 Computability Beyond Church–Kleene–Turing Thesis?
	8 Conclusion
	References
Theoretical Computer Science: Computational Complexity
	1 Introduction
		1.1 Complexity of Human Beings, Complexity of Machines
		1.2 What We Pick and Choose
	2 A Finer Look at Computability: Complexity Theory
		2.1 Physical Resources Limitations
		2.2 Complexity of a Few Particular Problems
		2.3 Complexity Theory
		2.4 Complexity Classes
		2.5 Characterization of Complexity Classes
		2.6 Complexity and Data Representation: Avižienis Parallel Addition
	3 Finite Automata
		3.1 Finite String Automata
		3.2 Beyond Finite String Automata
		3.3 Automata Over Discrete Structures
		3.4 Automata and Applications
	4 Quantum Computing
	5 Algorithmic Information Theory
		5.1 Shannon Entropy
		5.2 Kolmogorov Complexity
		5.3 A Formal Notion of Random Infinite Sequence of Bits
		5.4 Practical Applications of Kolmogorov Complexity
	6 Conclusion
	References
Databases and Artificial Intelligence
	1 Introduction
	2 Modeling Relational Databases with Logic
		2.1 Seminal Work
		2.2 Domain-Independent Formulas
	3 Integrity Constraints
		3.1 Integrity Constraints and First Order Logic
		3.2 Dynamic Constraints: First Order and Temporal Logics
		3.3 Concluding Remarks
	4 Database Preferences Queries
		4.1 Introduction
		4.2 Quantitative Approaches
		4.3 Qualitative Approaches
		4.4 Concluding Remarks
	5 Database Integration
		5.1 Motivations
		5.2 Query Answering By Rewriting
		5.3 Decidability and Complexity
	6 Conclusion
	References
Artificial Intelligence and Language
	1 Introduction
	2 The First Efforts
	3 Logic, AI, and NLP
		3.1 Logic for Syntax and Semantics
		3.2 Discourse Structure
		3.3 Lexical Semantics
		3.4 Pragmatics and Non-Monotonic Logic
		3.5 Reasoning and Dialogue
	4 Machine Learning in NLP
		4.1 The Rise of Large Text Corpora
		4.2 Casting NLP Tasks as Prediction Problems
		4.3 Unsupervised Learning for NLP
		4.4 Learning Semantic Representations
	5 Conclusion
	References
Information Retrieval and Artificial Intelligence
	1 Introduction
	2 Information Retrieval: Background
	3 Artificial Intelligence for Information Retrieval
	4 Document Representation
		4.1 Phrase-Based Indexing
		4.2 Semantic-Based Representation
		4.3 Word Embedding Representation
	5 Information Need Representation
	6 Retrieval Models: Relevance Modelling
		6.1 Logic-Based Models
		6.2 Fuzzy Models
		6.3 Bayesian Networks
		6.4 Machine Learning Based Models: Learning To Rank, Deep Learning
		6.5 Evolutionary Computation
	7 IR Approaches Based on Other AI Frameworks
	8 Conclusion
	References
Semantic Web
	1 Introduction
	2 Publishing Data on the Web
		2.1 RDF: Simple Conceptual Graphs
		2.2 The Web of Data
		2.3 Querying RDF with SPARQL
		2.4 Beyond SPARQL: Streams and Navigation
	3 A Little Knowledge Representation Goes a Long Way
		3.1 RDFS
		3.2 OWL: Description Logics on the Web
		3.3 Expressiveness/Efficiency Trade-Off
		3.4 Reasoning
		3.5 Querying Modulo Ontologies
		3.6 Rules
		3.7 Robust Inference
	4 Dealing with Heterogeneity
		4.1 From Alignments to Networks of Ontologies
		4.2 Semantics of Alignments
		4.3 Reasoning in Networks of Ontologies
		4.4 Ontology Matching
		4.5 Data Interlinking
	5 Perspectives
	6 Conclusion
	References
Artificial Intelligence and Bioinformatics
	1 Introduction
		1.1 A Major Application Field for Artificial Intelligence
		1.2 Bioinformatics: Analyzing Life Data at the Molecular Level
	2 Data and Knowledge Management
		2.1 Information Extraction in Biological Literature
		2.2 Biological Ontologies
	3 Gene and Non-coding RNA Prediction
	4 Protein Structure Prediction and Computational  Protein Design
		4.1 Secondary Structure Prediction, A Benchmark Model  for Structural Bioinformatics
		4.2 Folding in Space
	5 Network Modelling
	6 Understanding Evolution
		6.1 Multiple Sequence Alignment
		6.2 Building Phylogenetic Trees
	7 Drug Discovery
	8 Glycobiology
	9 Conclusion
	References
Artificial Intelligence in Biological Modelling
	1 Introduction
	2 Modelling Biochemical Interaction Networks
		2.1 Reaction Systems
		2.2 Influence Systems
		2.3 Logic Programming
	3 Automated Reasoning on Model Structures
		3.1 Petri Net Invariants
		3.2 Graph Matching
	4 Modelling Dynamical Behaviours
		4.1 Propositional Temporal Logics
		4.2 First-Order Quantitative Temporal Logics
	5 Automated Reasoning on Model Dynamics
		5.1 Symbolic Model-Checking of Biochemical Circuits
		5.2 Parameter Sensitivity and Robustness Computation
		5.3 Parameter Search with Temporal Logic Constraints
		5.4 Turing Completeness and Automated Synthesis  of Reaction Networks
	6 Learning Mechanistic Models from Temporal Data
		6.1 Probably Approximatively Correct Learning
		6.2 Answer Set Programming
		6.3 Budgeted Learning
	7 Conclusion
	References
When Artificial Intelligence and Computational Neuroscience Meet
	1 Introduction
	2 From Neurons to Symbols
	3 Sensory Perception, Cortex and Unsupervised Learning
	4 The Hippocampus for Multimodal Binding
		4.1 Functional Organization of the Hippocampus: Implication for Learning
		4.2 The Hippocampus in Navigation Tasks: An Example  of Multimodal Integration
		4.3 Grid Cells in the Entorhinal Cortex: Information Compression and Coding
		4.4 Implication of the Hippocampus in Planning  and Transition Recognition
	5 Action Selection, Reinforcement Learning  and the Basal Ganglia
		5.1 The Basal Ganglia as a Central Action Selection  Device in the Brain
		5.2 The Basal Ganglia as a Center for Reinforcement Learning
	6 Language and the Prefrontal Cortex
	7 Conclusion
	References
Artificial Intelligence and Pattern Recognition, Vision, Learning
	1 Introduction
	2 AI for Computer Vision and Pattern or Object Recognition
		2.1 Knowledge
		2.2 Spatial Relations
		2.3 Knowledge Representation and Organization
		2.4 Uncertainty
		2.5 Example: Recognition of Brain Structures in 3D MRI
	3 Code Supervision for Automatic Image Processing
		3.1 Formulation of Application Objectives
		3.2 Code Supervision
		3.3 Conclusion
	4 Machine Learning for Robotics
		4.1 Machine Learning Methods and Robotics
		4.2 Bio-inspired Learning and Robotics
		4.3 Current Challenges
	5 Conclusion
	References
Cross-Fertilisation Between Human-Computer Interaction  and Artificial Intelligence
	1 Introduction
	2 History of Interfaces Between HCI and AI: A Genesis
	3 Intelligent User Interfaces
	4 Affective Embodied Conversational Agents
	5 Consolidating, Formalizing and Exploiting Usability Knowledge for Designing and Evaluating Interactive Systems
	6 Visualisation and Data Mining
	7 Conclusion
	References
Robotics and Artificial Intelligence
	1 Introduction
	2 Overview of Robotics
	3 Motion Planning, Mapping and Navigation
		3.1 Motion Planning with Probabilistic Road Maps
		3.2 Simultaneous Localization and Mapping
		3.3 Navigation
	4 Task Planning and Acting
		4.1 Deterministic Approaches
		4.2 Temporal Approaches
		4.3 Probabilistic Approaches
		4.4 Integrating Motion and Task Planning
	5 Interaction
		5.1 Multi-robot Interaction
		5.2 Human–Robot Interaction
	6 Learning
		6.1 Reinforcement Learning
		6.2 Learning from Demonstration
	7 Integration and Software Architecture
		7.1 Architecture Paradigms
		7.2 Robustness, Validation and Verification
	8 Conclusion
	References
Artificial Intelligence: Philosophical  and Epistemological Perspectives
	1 Introduction
	2 Three Classical Debates: Turing\'s Test, Searle\'s Chinese Chamber, Dreyfus\' Phenomenological Arguments
		2.1 Turing\'s Test
		2.2 Searle\'s Chinese Room
		2.3 AI and the Phenomenological Approach
	3 Initial Challenges of AI Program of Research
	4 How Evolution of AI Shifts Epistemological Perspectives on Intelligence
	5 The Right Place of Philosophical Challenges
	6 Attempts to Take Up New Challenges
	7 The Laboratory of Agent-Based-Simulation
	8 Conclusion
	References
Artificial Intelligence and High-Level Cognition
	1 Introduction
	2 Core Empirical Results on High-Level Cognition
		2.1 Psychological Findings
		2.2 Features of Human Reasoning and Problem Solving
	3 The Cognition of Reasoning and Problem Solving
		3.1 Mental Models
		3.2 Models of Cognition Inspired by Nonmonotonic Logics
		3.3 Syntactic Approaches
		3.4 Probabilistic and Heuristic Approaches
		3.5 The Cognition of Analogical Reasoning
	4 The Architecture of Cognition and Cognitive Models
		4.1 Evaluation Criteria for Cognitive Models
		4.2 The Architecture of High-Level Human Cognition
	5 Challenges in High-Level Cognition Research
	6 Conclusion
	References
Artificial Intelligence and Literature
	1 Introduction
		1.1 Intelligence Versus Creativity
		1.2 Different Kinds of Creativity
	2 Mechanical Creativity
	3 Rule-Based and Template-Based Methods
		3.1 Computational Humour
		3.2 Metaphor
		3.3 Poetry Generation
		3.4 Story Generation
	4 Stochastic Methods
		4.1 Computational Humour
		4.2 Metaphor
		4.3 Poetry Generation
		4.4 Story Generation
	5 Conclusion
	References
Music and Artificial Intelligence
	1 Prelude
	2 Music, Language and Reasoning
		2.1 Music and Rationality
		2.2 Music and Language: Striking Similarities?
		2.3 Music and Cognitive Science: Prominence of the Brain
		2.4 The Contribution of Music to the Development of AI
	3 Main Features of Musical Structure: Musical Knowledge Representation
		3.1 Basic Musical Knowledge Representation Features
		3.2 A Model for Music Knowledge Representation Based on Typed Feature Structures
		3.3 Representation of a Melody
		3.4 Representing Polyphony and Chords
		3.5 A Few Generic Operations on Feature Structures
		3.6 Model Evolutions in Contemporary Music
	4 AI and Music: Main Topics and Research Areas
		4.1 Modeling Emotions
		4.2 Problem Solving for Music Analysis and Production
		4.3 Machine Learning for Music Analysis and Performance
		4.4 Multi-modal Intelligent Environments
		4.5 Intelligent Tutoring Systems
		4.6 Music Composition Tools
	5 Conclusion
	References
Afterword—A Note on Other Areas in Relation with AI
Epilogue: A Plea for a Unified View of Artificial Intelligence as a Science
1 Why this Book
2 Different Views of AI
3 AI as a Science
4 Conclusion
Index