Computational Intelligence In Archaeology

Provides analytical theories offered by innovative artificial intelligence computing methods in the archaeological domain.

This book describes a novel machine-learning based approach to answer some traditional archaeological problems, relating to archaeological site detection and site locational preferences. Institutional data collected from six Swiss regions (Zurich, Aargau, Grisons, Vaud, Geneva and Fribourg) have been analyzed with an original conceptual framework based on the Random Forest algorithm. It is shown how the algorithm can assist in the modelling process in connection with heterogeneous, incomplete archaeological datasets and related cultural heritage information. Moreover, an in-depth review of past and more recent works of quantitative methods for archaeological predictive modelling is provided. The book guides the readers to set up their own protocol for: i) dealing with uncertain data, ii) predicting archaeological site location, iii) establishing environmental features importance, iv) and suggest a model validation procedure. It addresses both academics and professionals in archaeology and cultural heritage management, and offers a source of inspiration for future research directions in the field of digital humanities and computational archaeology.

The use of computation in archaeology is a kind of magic, a way of heightening the archaeological imagination. Agent-based modelling allows archaeologists to test the ‘just-so’ stories they tell about the past. It requires a formalization of the story so that it can be represented as a simulation; researchers are then able to explore the unintended consequences or emergent outcomes of stories about the past. Agent-based models are one end of a spectrum that, at the opposite side, ends with video games. This volume explores this spectrum in the context of Roman archaeology, addressing the strengths, weaknesses, and opportunities of a formalized approach to computation and archaeogaming.

Tools of data comparison and analysis are critical in the field of archaeology, and the integration of technological advancements such as geographic information systems, intelligent systems, and virtual reality reconstructions with the teaching of archaeology is crucial to the effective utilization of resources in the field. E-Learning Methodologies and Computer Applications in Archaeology presents innovative instructional approaches for archaeological e-learning based on networked technologies, providing researchers, scholars, and professionals a comprehensive global perspective on the resources, development, application, and implications of information communication technology in multimedia-based educational products and services in archaeology.

Traditional methods of making archaeological data available are becoming increasingly inadequate. Thanks to improved techniques for examining data from multiple viewpoints, archaeologists are now in a position to record different kinds of data, and to explore that data more fully than ever before. The growing availablility of computer networks and other technologies means that communication should become increasingly available to international archaeologists. Will this result in the democratisation of archaeological knowledge on a global basis? Contributors from Western and Eastern Europe, the Far East, Africa and the Americas seek to answer this and other questions about the way in which modern technology is revolutionising archaeological knowledge.

Computer science—especially pattern recognition, signal processing and mathematical algorithms—can offer important information about archaeological finds, information that is otherwise undetectable by the human senses and traditional archaeological approaches. Pattern Recognition and Signal Processing in Archaeometry: Mathematical and Computational Solutions for Archaeology offers state of the art research in computational pattern recognition and digital archaeometry. Computer science researchers in pattern recognition and machine intelligence will find innovative research methodologies combined to create novel and efficient computational systems, offering robust, exact, and reliable performance and results. Archaeologists, conservators, and historians will discover reliable automated methods for quickly reconstructing archaeological materials and benefit from the application of non-destructive, automated processing of archaeological finds.

If machine learning transforms the nature of knowledge, does it also transform the practice of critical thought? Machine learning—programming computers to learn from data—has spread across scientific disciplines, media, entertainment, and government. Medical research, autonomous vehicles, credit transaction processing, computer gaming, recommendation systems, finance, surveillance, and robotics use machine learning. Machine learning devices (sometimes understood as scientific models, sometimes as operational algorithms) anchor the field of data science. They have also become mundane mechanisms deeply embedded in a variety of systems and gadgets. In contexts from the everyday to the esoteric, machine learning is said to transform the nature of knowledge. In this book, Adrian Mackenzie investigates whether machine learning also transforms the practice of critical thinking. Mackenzie focuses on machine learners—either humans and machines or human-machine relations—situated among settings, data, and devices. The settings range from fMRI to Facebook; the data anything from cat images to DNA sequences; the devices include neural networks, support vector machines, and decision trees. He examines specific learning algorithms—writing code and writing about code—and develops an archaeology of operations that, following Foucault, views machine learning as a form of knowledge production and a strategy of power. Exploring layers of abstraction, data infrastructures, coding practices, diagrams, mathematical formalisms, and the social organization of machine learning, Mackenzie traces the mostly invisible architecture of one of the central zones of contemporary technological cultures. Mackenzie's account of machine learning locates places in which a sense of agency can take root. His archaeology of the operational formation of machine learning does not unearth the footprint of a strategic monolith but reveals the local tributaries of force that feed into the generalization and plurality of the field.