Computational Intelligence in Archaeology

As some of you may know, my masters topic was “A Computational Intelligence Approach to Clustering Temporal Archaeological Data”. As Masters sometimes do, that topic ended up having to change, removing the archaeology šŸ˜¦ and resulting in just “A Computational Intelligence Approach to Clustering Temporal Data”.

Removing the archaeology also meant removing everything from the literature study relevant to archaeology, except maybe some examples I managed to keep in. This post contains theĀ literature that had to be removed. The post begins giving a short overview of archaeology, explaining the role of computational intelligence in archaeologyĀ and concluding with a variety of examples where computational intelligence has beenĀ used in archaeology.

Overview of Archaeology

Archaeology [1] is a field in humanities which studies the material remains of former societies. The field also aims to preserve and reconstruct these remains in order to find out more about the past. The preservation of the remains allows for the knowledge arising from the discovery to be passed to future generations, as well as for more complete interpretations of the past to be documented.

The archaeological process takes place in five stages [2]. These stages are discovery and investigation, excavation, post-excavation analysis and lab work, dating discoveries and, lastly, reporting and interpretation. An archaeologist will work with large amounts of data during each step, either by using previous datasets or creating new ones. This data often becomes so vast it may lose manageability and introduce difficulties in the interpretation process. For this reason, computers began to be used by archaeologists not only for storing data and as statistical tools, but also for managing and analysing the data [3].

Technology inspired by various fields is widely used by archaeologists today. Ground penetrating radar (GPR) [4] and metal detectors [5] are devices used to map out and detect certain objects buried underground.Ā  Virtual reality [6] and Laser Scanning [7] areĀ  used to document and generate virtual models of archaeological discoveries.Ā  Applications such as Google Sketchup [8] and Photomodeller [6] are also used to model artefacts and structures. Geographical information systems (GIS) [9] andĀ  geographical positioning systems (GPS) [10] are used to handle spatial data. Tablets, such as iPads [11] have previously been used by Pompeii archaeologists to ease the documentation process. Satellite images [12] are used to detect possible site locations. X-ray diffraction [13] and Raman spectrometry [14] are used for analysing the physical composition of an artefact. All these technologies are used with the intention of easing the archaeological process and centralising storage.


The Role of Computational Intelligence in Archaeology

Computational Intelligence (CI) techniques can be used in archaeology to aid the analysis and interpretation of data. The more common use of CI in archaeology is classification [15], which involves categorizing objects and predicting typology according to the attributes of the data.

Archaeologists need to often group similar objects together, such as artefacts manufactured or designed in similar manners [16]. An automated classification of such objects saves the archaeologist time and results in a more accurate and unbiased classification of the data. Certain CI algorithms need to be trained [17] by the archaeologist, using known classifications which may be based on prior research. In the case where prior classifications are unavailable, algorithms which require no prior training in order to perform classification can be used, such as data clustering algorithms.

Object recognition is another growing archaeological interest where CI techniques can be used [15]. The CI technique chosen for this purpose must deal with data that is not purely numeric or textual. For example, the input to the algorithm may be an image and the output may be the position of an object with specific characteristics. In order for the algorithm to be able to process this kind of data, the data must either be converted to a format that can be understood by the algorithm, or the algorithm needs to be adapted to accept such an input format.

CI may also be used in archaeology as a data mining technique in order to determine interesting patterns among archaeological entities [18]. Techniques that can be used for this purpose include artificial neural networks (ANNs), data clustering algorithms and genetic algorithms (GAs) [19]. Visualisations may be performed before, during, or after the use of CI techniques in order to gain a more general and compact view of the datasets being analysed [19].

CI can also be used in robotics as robots can be programmed to be ā€œintelligentā€ using CI algorithms [20], requiring less or no user interaction. Marine archaeologists often use robots which are built to withstand harsh environments [21]. These harsh environments include depths the human body cannot withstand [22] or dangerous sites with limited access. Robots aid the archaeologists in exploring archaeological sites which would otherwise be hazardous to a diver.

In order for the use of CI to be of advantage to archaeologists, the input and output of the CI algorithms chosen must be considered. Archaeologists use various types of data daily, including spatial data [23], temporal data [24], numerical data [25], visual data [26], textual data [26], categorical data [26] and 3D models [27]. The CI techniques used by the archaeologist must be implemented to handle these different types of data and the output must be interpretable by the archaeologist. An algorithm that cannot handle the data that has to be processed is useless, as is output that cannot be understood by a human.


Computational Intelligence for the Analysis of Archaeological Data

Although the discussed algorithms have not previously been used to analyse archaeological data, a variety of other computational methods have been analysed for this purpose. Hodson [28] discusses various examples of cluster analysis methods for the classification of fibulae. The research mentions the use of single-link cluster analysis, average-link cluster analysis, double-link cluster analysis and the K-means clustering approach. Single-link cluster analysis [28] involves joining the two most similar data patterns first, followed by the next two most similar data patterns until all data patterns are joined.Ā  Already linked data patterns merge their entire group with similar data patterns or groups, eventually resulting in a set of N clusters representing the classification of the data.Ā  Double-link cluster analysis [29] is an adaptation of single-link cluster analysis where clusters can overlap by a certain number of data patterns before merging to become one cluster. Average-link cluster analysis [30] allows for a data pattern to join an existing cluster only if the average similarity between all its data patterns is at a predefined level. Lastly, K-means [31] involves selecting an optimal number K of cluster centers, assigning each data pattern to the closest cluster center, calculating the average distance between data patterns in a cluster, and setting the cluster center to this average value.

Van den Dries [32] describes an expert system and an ANN developed for the purpose of use-wear analysis of prehistoric flint tools. The expert system, called WAVES, is a menu based expert system designed to be used by people with limited computer knowledge by having a user friendly and intuitive interface. The system consists of a set of rules, which can be added or removed. The ANN implemented was a multi-layer feed-forward ANN with back-propagation learning. After testing both WAVES and the ANN, van den Dries [32] concluded that the systems would be useful to archaeologists as they may save time, reveal possible analysis deficiencies and be usable by non-experts.

Van den Dries [32] also built a table containing names and descriptions of previous artificial intelligence systems built for the purpose of archaeology. This table included, among others, the following examples:

  • An expert system developed for the purpose of identifying and classifying ceramic beakers.
  • An expert system developed for the interpretation of ancient iconography.
  • A system developed for the purpose of classifying bronze age axes.
  • An expert system for identification of artefacts found in excavations.
  • An expert system for the purpose of analysing bird bones.
  • A rule-based system for the purpose of dating Japanese keyhole tombs.
  • A hybrid \gls{ANN} for the purpose of ageing of artefacts.
  • A pattern-recognition system for classification of Phoenician pottery.

ANNs have also been used extensively in archaeology. Toyota et al [33], Lopez-Molinero et al [34] as well as Tanevska et al [35] have all published research done on the use of a self-organising maps (SOMs) in archaeology. SOMs [36] are unsupervised ANNs where neurons form neighbourhoods and a response across all neurons takes place for each input. Toyota et al [33] used these SOMs to evaluate Marajora ceramic composition, while Lopez-Molinero et al [37] used SOMs to classify ancient Roman glazed ceramics and Tanevska et al [35] to determine the origin of the terra cota icons found in the Vinicia Fortress. Alternatively, Barcelo and Fauna [38] used a standard ANN to determine the correlation between the quantity of pottery found in an area and time. This was also done by Ma et al [39], who classified Chinese pottery from the neolithic age using an ANN.

An area not yet mentioned is the use of evolutionary algorithms in archaeology. Although evolutionary algorithms have been used for this purpose, they are not as popular as ANNs. Kontogiorgos and Leontitsis [40] used a genetic algorithm to estimate the weight of microartefacts in order to aid archaeological interpretation of a Greek neolithic site. Maiza and Gaildrat [41] also used a genetic algorithm to determine the best position of a pottery fragment based on a previously calculated distance measure. Coevolution, has been used by IbaƱez et al [42] to handle landmark location uncertainty in skull-face overlay. A fuzzy evolutionary method was applied to the same problem and the two methods were compared.



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2 comments on “Computational Intelligence in Archaeology

  1. this is very interesting! i am wondering, as an undergraduate myself, what was your undergraduate education in?

    • Hi. My undergraduate degree was computer science but I find most computational archaeologists study archaeology and learn the computer science later. Computer science teaches you to think in a particular manner, so if you’re interested in CI and archaeology I would recommend studying computer science and specialising in CI. However if you maybe just like the idea of a neural network, for example, coursera does offer beginners and advanced courses in such topics. So it all depends on what you want to specialise in. I think I made the right decision for myself by studying computer science.

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