Editing Computational archaeology (section)

== Origins and objectives ==
In recent years, it has become clear that [[archaeologists]] will only be able to harvest the full potential of [[Quantitative research|quantitative]] methods and computer technology if they become aware of the specific pitfalls and potentials inherent in the archaeological data and research process. AI science is an emerging discipline that attempts to uncover, quantitatively represent and explore specific properties and patterns of archaeological information. [[Basic research|Fundamental research]] on data and methods for a self-sufficient archaeological approach to [[Data processing|information processing]] produces quantitative methods and computer [[software]] specifically geared towards archaeological problem solving and understanding.

AI science is capable of complementing and enhancing almost any area of [[scientific]] archaeological research. It incorporates a large part of the methods and theories developed in [[Processual archaeology|quantitative archaeology]] since the 1960s but goes beyond former attempts at quantifying archaeology by exploring ways to represent general archaeological information and problem structures as computer [[algorithms]] and [[data structures]]. This opens archaeological analysis to a wide range of computer-based information processing methods fit to solve problems of great complexity. It also promotes a formalized understanding of the discipline's research objects and creates links between archaeology and other quantitative disciplines, both in methods and software technology. Its agenda can be split up in two major research themes that complement each other:

#Fundamental research (theoretical AI science) on the structure, properties and possibilities of archaeological data, [[inference]] and [[knowledge building]]. This includes modeling and managing [[Fuzzy measure theory|fuzziness]] and [[uncertainty]] in archaeological data, scale effects, optimal [[Sampling (statistics)|sampling]] strategies and spatio-temporal effects.
#Development of computer algorithms and software (applied AI science) that make this theoretical knowledge available to the user.

There is already a large body of literature on the use of quantitative methods and computer-based analysis in archaeology. The development of methods and applications is best reflected in the annual publications of the [[Computer Applications and Quantitative Methods in Archaeology|CAA conference]] (see external links section at bottom). At least two journals, the Italian ''Archeologia e Calcolatori'' and the British ''Archaeological Computing Newsletter'', are dedicated to archaeological computing methods. AI Science contributes to many fundamental research topics, including but not limited to:

* advanced [[statistics]] in archaeology, spatial and temporal archaeological data analysis
* [[bayesian analysis]] and advanced [[probability]] models, [[Fuzzy measure theory|fuzziness]] and [[uncertainty]] in archaeological data
* scale-related phenomena and scale transgressions
* [[GIS in archaeology|intrasite analysis]] (representations of [[stratigraphy]], 3D analysis, [[Artifact (archaeology)|artefact]] distributions)
* landscape analysis (territorial modeling, [[visibility analysis]])
* optimal [[archaeological field survey|survey]] and sampling strategies
* [[Process (science)|process-based]] modeling and [[simulation]] models
* archaeological [[predictive modeling]] and [[heritage management]] applications
* supervised and unsupervised [[Scientific classification|classification]] and typology, [[artificial intelligence]] applications
* digital [[Excavation (archaeology)|excavations]] and [[virtual reality]]
* computational reproducibility of archaeological research
* archaeological software development, electronic [[data sharing]] and publishing

AI science advocates a formalized approach to archaeological inference and knowledge building. It is [[interdisciplinary]] in nature, borrowing, adapting and enhancing method and theory from numerous other disciplines such as [[computer science]] (e.g. algorithm and software design, [[database]] design and theory), [[geoinformation]] science ([[Geostatistics|spatial statistics]] and modeling, [[geographic information systems]]), [[artificial intelligence]] research (supervised classification, [[fuzzy logic]]), [[ecology]] (point pattern analysis), [[applied mathematics]] ([[graph theory]], [[probability theory]]) and [[statistics]].