Computational Intelligence in Archaeology

Libro de Barceló (2009)

Section I
From Natural Archaeology to “Artificial” Intelligence

Chapter I “Automatic” Archaeology: A Useless Endeavor, an Impossible Dream, or Reality?

Automata: The Awful Truth about Humans and Machines
Archaeology as a Problem Solving Task
Why Archaeological Observables are the Way They Are? The Mechanical Nature of
Archaeological Recognition
The Sciences of the Artificial
Directions for Further Research

Chapter II Problem Solving in the Brain and by the Machine

Looking for Solutions
Expert Systems
“Deconstructing” Archaeology
An Automated Archaeologist, which Discovers the Function of Lithic Tools
An Automated Archaeologist, which Reconstructs Incomplete Data
An Automated Archaeologist, which Understands What an Archaeological Site Was
An Automated Archaeologist, which Explains Ancient Societies
An Automated Archaeologist, which Understands Everything
Is a “Rational” Automated Archaeologist an Impossible Dream?
Directions for Further Research

Section II
Learning and Experimentation in Historical Sciences

Chapter III Computer Systems that Learn

Inverse Reasoning
Inverse Reasoning as a Predictive Task
An Introduction to Machine Learning Algorithms
Simple Rule Induction Methods
Inducing Decision Trees
Classification and Clustering
Predicting Complex Relationships
Some Limitations of Supervised Learning
A Biological Metaphor: Adaptive and Genetic Algorithms
Learning in Uncertain Contexts
Directed Graphs and Probabilistic Networks
Directions for Further Research

Chapter IV An Introduction to Neurocomputing

Simulating the Brain
How a Neural Network Works
How a Neural Network Learns
The Backpropagation Learning Algorithm
How Good Are Neural Network Answers?
When the Neural Network Does Not Learn
Alternative Supervised Learning Algorithms: Radial Basis Functions
Unsupervised Learning Algorithms: Self-Organized Maps
Recurrent Networks
Directions for Further Research

Section III
Practical Examples of Automated Archaeology

Chapter V Visual and Non-Visual Analysis in Archaeology

From Observable Effects to Unobservable Causes
Identification-Based Analysis
An Automated Archaeologist, which Understands Scientific Texts
The Archaeological Analysis of Visual Marks
Directions for Further Research


Chapter VI Shape Analysis

Why Archaeological Evidence has “Shape”?
Direct Shape Recognition
Advanced Methods of Shape Analysis and Interpretation
Decomposing Shape
Limitations in Shape Analysis and Recognition
Directions for Further Research

Chapter VII Texture and Compositional Analysis in Archaeology

Texture
Composition
Directions for Further Research

Chapter VIII Spatiotemporal Analysis

The Analysis of Spatial Frequencies
Neural Networks for Solving the Spatial Interpolation Problem
Interpreting Remote Sensing Data: An Example of Spatial Interpolation
Neurocomputational Spatial Models in Archaeology and the Spatial Sciences
The Analysis of Temporal Series and Chronological Data
Understanding the Future: Towards Historical Prediction
Directions for Further Research

Chapter IX An Automated Approach to Historical and Social Explanation

Neuroclassification as Social Explanation
Towards a Neurocomputational Approach to Social Dynamics
Beyond the “Neural” Analogy: Building an Artificial Society
Directions for Further Research

Section IV
Conclusions: The Computational Philosophy of Archaeology

Chapter X Beyond Science Fiction Tales

The Automated Archaeologist as a Time Machine
“Seeing” the Past in the Present
“Conceptualizing” the Past in the Present
“Understanding” the Past in the Present
“Simulating” the Past in the Present
A Final Comment on Automated Explanation
Towards a “Computational Philosophy of Science”
Directions for Further Research