Connectionists: New book: Neural-Symbolic Cognitive Reasoning, from Springer's Cognitive Technologies Series

[Luis Lamb]

Dear Connectionists,

I would like to announce the publication of
"Neural-Symbolic Cognitive Reasoning", by
Artur S. d'Avila Garcez, Luís C. Lamb, and Dov M. Gabbay.
Springer, 2009.
http://www.springer.com/978-3-540-73245-7

About the book:
Humans are often extraordinary at performing practical reasoning.
There are cases where the human computer, slow as it is,
is faster than any artificial intelligence system.
Are we faster because of the way we perceive knowledge
as opposed to the way we represent it?

The authors address this question by presenting neural
network models that integrate the two most fundamental
phenomena of cognition: our ability to learn from experience,
and our ability to reason from what has been learned.
This book is the first to offer a self-contained presentation
of neural network models for a number of computer science
logics, including modal, temporal, and epistemic logics.
By using a graphical presentation, it explains neural networks
through a sound neural-symbolic integration methodology,
and it focuses on the benefits of integrating effective
robust learning with expressive reasoning capabilities.

The book will be invaluable reading for academic researchers,
graduate students, and senior undergraduates in computer science,
artificial intelligence, machine learning, cognitive science
and engineering. It will also be of interest to computational
logicians, and professional specialists on applications of
cognitive, hybrid and artificial intelligence systems.

Keywords: Artificial intelligence; Artificial neural networks;
Connectionist non-classical logics; Logic for computer science;
Machine learning; Neural computation; Neural-symbolic integration;
Neural-symbolic learning systems

Table of Contents:
-----------------
1 Introduction
1.1 Motivation
1.2 Methodology and Related Work
1.3 Structure of the Book
-- 
2 Logic and Knowledge Representation
2.1 Preliminaries
2.2 Classical Logic
2.2.1 Propositional Logic
2.2.2 First-Order Logic
2.3 Nonclassical Logics
2.4 Nonmonotonic Reasoning
2.5 Logic Programming
2.5.1 Stable-Model and Answer Set Semantics
2.6 Discussion
-- 
3 Artificial Neural Networks
3.1 Architectures of Neural Networks
3.2 Learning Strategy
3.3 Recurrent Networks
3.4 Evaluation of Learning Models
3.5 Discussion
-- 
4 Neural-Symbolic Learning Systems
4.1 The CILP System
4.2 Massively Parallel Deduction in CILP
4.3 Inductive Learning in CILP
4.4 Adding Classical Negation
4.5 Adding Metalevel Priorities
4.6 Applications of CILP
4.7 Discussion
-- 
5 Connectionist Modal Logic
5.1 Modal Logic and Extended Modal Programs .
5.1.1 Semantics for Extended Modal Logic Programs
5.2 Connectionist Modal Logic
5.2.1 Computing Modalities in Neural Networks
5.2.2 Soundness of Modal Computation
5.2.3 Termination of Modal Computation
5.3 Case Study: The Muddy Children Puzzle
5.3.1 Distributed Knowledge Representation in CML
5.3.2 Learning in CML
5.4 Discussion
-- 
6 Connectionist Temporal Reasoning
6.1 Connectionist Temporal Logic of Knowledge
6.1.1 The Language of CTLK.
6.1.2 The CTLK Algorithm
6.2 The Muddy Children Puzzle (Full Solution)
6.2.1 Temporal Knowledge Representation
6.2.2 Learning in CTLK
6.3 Discussion
-- 
7 Connectionist Intuitionistic Reasoning
7.1 Intuitionistic Logic and Programs
7.2 Connectionist Intuitionistic Reasoning
7.2.1 Creating the Networks
7.2.2 Connecting the Networks
7.3 Connectionist Intuitionistic Modal Reasoning
7.4 Discussion
-- 
8 Applications of Connectionist Nonclassical Reasoning
8.1 A Simple Card Game
8.2 The Wise Men Puzzle
8.2.1 A Formalisation of the Wise Men Puzzle
8.2.2 Representing the Wise Men Puzzle Using CML
8.3 Applications of Connectionist Intuitionism
8.3.1 Representing the Wise Men Puzzle Using CIL
8.4 Discussion
-- 
9 Fibring Neural Networks
9.1 The Idea of Fibring
9.2 Fibring Neural Networks
9.3 Examples of the Fibring of Networks
9.4 Definition of Fibred Networks
9.5 Dynamics of Fibred Networks
9.6 Expressiveness of Fibred Networks
9.7 Discussion
-- 
10 Relational Learning in Neural Networks
10.1 An Example
10.2 Variable Representation
10.3 Relation Representation
10.4 Relational Learning
10.5 Relational Reasoning
10.6 Experimental Results
10.7 Discussion
-- 
11 Argumentation Frameworks as Neural Networks
11.1 Value-Based Argumentation Frameworks
11.2 Argumentation Neural Networks
11.3 Argument Computation and Learning
11.3.1 Circular Argumentation
11.3.2 Argument Learning
11.3.3 Cumulative (Accrual) Argumentation
11.4 Fibring Applied to Argumentation
11.5 Discussion
-- 
12 Reasoning about Probabilities in Neural Networks
12.1 Representing Uncertainty
12.2 An Algorithm for Reasoning about Uncertainty
12.3 The Monty Hall Puzzle
12.4 Discussion
-- 
13 Conclusions
13.1 Neural-Symbolic Learning Systems
13.2 Connectionist Nonclassical Reasoning
13.2.1 Connectionist Modal Reasoning
13.2.2 Connectionist Temporal Reasoning
13.3 Fibring Neural Networks
13.4 Concluding Remarks
-- 
References
Index
http://www.springer.com/978-3-540-73245-7