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Computer Systems that Learn
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Author(s): Juan A. Barceló (Universitat Autònoma de Barcelona, Spain)
Copyright: 2009
Pages: 69
Source title:
Computational Intelligence in Archaeology
Source Author(s)/Editor(s): Juan A. Barcelo (Universidad Autonoma de Barcelona, Spain)
DOI: 10.4018/978-1-59904-489-7.ch003
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Abstract
Inverse problems are among the most challenging in computational and applied science and have been studied extensively (Bunge, 2006; Hensel, 1991; Kaipio & Somersalo, 2004; Kirsch, 1996; Pizlo, 2001; Sabatier, 2000; Tarantola, 2005; Woodbury, 2002). Although there is no precise definition, the term refers to a wide range of problems that are generally described by saying that their answer is known, but not the question. An obvious example would be “Guessing the intentions of a person from her/his behavior.” In our case: “Guessing a past event from its vestiges.” In archaeology, the main source for inverse problems lies in the fact that archaeologists generally do not know why archaeological observables have the shape, size, texture, composition, and spatiotemporal location they have. Instead, we have sparse and noisy observations or measurements of perceptual properties, and an incomplete knowledge of relational contexts and possible causal processes. From this information, an inverse engineering approach should be used to interpret adequately archaeological observables as the material consequence of some social actions.
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