Abstract
This paper analyses the application of artificial intelligence techniques to various areas of archaeology and more specifically: (1) the use of software tools as a creative stimulus for the organization of exhibitions; the use of humanoid robots and holographic displays as guides that interact and involve museum visitors; (2) the analysis of methods for the classification of fragments found in archaeological excavations and for the reconstruction of ceramics, with the recomposition of the parts of text missing from historical documents and epigraphs; (3) the cataloging and study of human remains to understand the social and historical context of belonging with the demonstration of the effectiveness of the AI techniques used; (4) the detection of particularly difficult terrestrial archaeological sites with the analysis of the architectures of the Artificial Neural Networks most suitable for solving the problems presented by the site; the design of a study for the exploration of marine archaeological sites, located at depths that cannot be reached by man, through the construction of a freely explorable 3D version.


























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References
“Archaeology: Wikipedia.” [Online]. https://en.wikipedia.org/wiki/Archaeology. Accessed 16 Apr 2020.
INFORMATICA ARCHEOLOGICA in ‘Enciclopedia Italiana.’ [Online]. https://www.treccani.it/enciclopedia/informatica-archeologica_%28Enciclopedia-Italiana%29/. Accessed 16 Apr 2020.
Kuno Y, Sadazuka K, Kawashima M, Yamazaki K, Yamazaki A, Kuzuoka H. Museum guide robot based on sociological interaction analysis. In: Conference on human factors in computing systems—proceedings;ACM. San Jose. 2007. p. 1191–4.
Burgard W, et al. Experiences with an interactive museum tour-guide robot. Artif Intell. 1999;114(1–2):3–55. https://doi.org/10.1016/s0004-3702(99)00070-3.
Kuno Y et al. Museum guide robot with communicative head motion. In: The 15th IEEE international symposium on robot and human interactive communication (RO-MAN06); IEEE. Hatfield, UK, 2006. p. 33–8.
Amigoni F, Schiaffonati V, Somalvico M. Minerva: an artificial intelligent system for composition of museums, Milano. 2003.
Politecnico FA, Milano D, Amigoni F, Schiaffonati V. The Minerva multiagent system for supporting creativity the minerva multiagent system for supporting creativity in museums organization, Milano. 2003.
Amigoni F, Schiaffonati V. A New Version of Minerva for Organizing Archeological Museums, Milano. 2006.
Caggianese G, De Pietro G, Esposito M, Gallo L, Minutolo A, Neroni P. Discovering Leonardo with artificial intelligence and holograms: a user study. Pattern Recognit Lett. 2020;131:361–7.
Fukui K, Yamaguchi O. Facial feature point extraction method based on combination of shape extraction and pattern matching. Syst Comput Jpn. 1998;29(6):49–58. https://doi.org/10.1002/(SICI)1520-684X(19980615)29:6<49:AID-SCJ5>3.0.CO;2-L.
Frontoni E. Vision based mobile robotics: mobile robot localization using vision sensors. Lulu Com. 2012.
Fox D, Burgard W, Thrun S, Cremers AB. Position estimation for mobile robots in dynamic environments. In: Proceedings of the National Conference on Artificial Intelligence, 983–8, 1998.
Moravec HP. Sensor fusion in certainty grids for mobile robots. Sens Devices Syst Robot. 1989;9(2):253–76. https://doi.org/10.1007/978-3-642-74567-6_19.
Thrun S, Burgard W, Fox D. A probabilistic approach to concurrent mapping and localization for mobile robots. Auton Robots. 1998;5(3–4):253–71. https://doi.org/10.1023/A:1008806205438.
Fox D, Burgard W, Thrun S. The dynamic window approach to collision avoidance. Robot Autom Mag IEEE. 1997;4:23–33. https://doi.org/10.1109/100.580977.
Thrun S, Arno B, Fr T. Map learning and high-speed navigation in RHINO. AI-based mobile robots: case studies of successful robot systems. Bonn: MIT Press; 1999. p. 1–24.
Thomas D, Mark B. An analysis of time-dependent planning. Proc Seventh Natl Conf Artif Intell AAAI. 1988;88:49–544.
Richard B. Dynamic Programming, Sixth prin. Princeton: Princeton University Press; 1957.
Howard RA. Dynamic programming and Markov processes. Oxford: Wiley; 1960.
Levesque HJ, Reiter R, Lespérance Y, Lin F, Scherl RB. Golog: a logic programming language for dynamic domains. J Log Program. 1997;31(1–3):59–83. https://doi.org/10.1016/S0743-1066(96)00121-5.
Friedman-Hill E. Jess, the java expert system shell. Biosystems. 2nd ed. 2003.
Sprott JC. Physics demonstrations: a sourcebook for teachers of physics. Madison: University of Wisconsin Press; 2006.
Adams WY, Adams EW. Archaeological typology and practical reality: a dialectical approach to artifact classification and sorting. Cambridge: Cambridge University Press; 2008.
Barceló JA. Computational intelligence in archaeology. Comput Intell Archaeol. 2008;25(2003):1–418. https://doi.org/10.4018/978-1-59904-489-7.
Salazar A, Safont G, Vergara L, Vidal E. Pattern recognition techniques for provenance classification of archaeological ceramics using ultrasounds. Pattern Recognit Lett. 2020;135:441–50. https://doi.org/10.1016/j.patrec.2020.04.013.
Romanengo C, Biasotti S, Falcidieno B. Recognising decorations in archaeological finds through the analysis of characteristic curves on 3D models. Pattern Recognit Lett. 2020;131:405–12. https://doi.org/10.1016/j.patrec.2020.01.025.
Gualandi ML, et al. ArchAIDE-archaeological automatic interpretation and documentation of cEramics. Eurogr Work Graph Cult Herit. 2016;2:4–8.
Gattiglia G. Classificare le ceramiche: dai metodi tradizionali all’intelligenza artificiale L’esperienza del progetto europeo ArchAIDE Archeol. Archeol Quo Vadis? Riflessioni Metodol sul Futur di una Discip Atti. 2018;1:271–98.
Anichini F, Gattiglia G. Big archaeological data. The ArchAIDE project approach. In: GARR-Conf17-proceedings-03 Big; Associazione consortium GARR, Pisa. 2018. p. 3.
Ostertag C, Beurton-Aimar M. Matching ostraca fragments using a siamese neural network. Pattern Recognit Lett. 2020;131:336–40. https://doi.org/10.1016/j.patrec.2020.01.012.
Lecun Y, Bottou L, Bengio Y, Ha P. Gradient-based learning applied to document recognition. Proc IEEE. 1998;10(1109/5):726791.
Mayer-Schönberger V, Cukier K. Big data: a revolution that will transform how we live, work, and think. Boston: Houghton Mifflin Harcourt; 2013.
C. O’Neil, R. Schutt, Doing Data Science, 1st ed. O’Reilly, 2013.
Smith R. An overview of the Tesseract OCR engine. In: Ninth international conference document analysis recognition (ICDAR 2007), vol. 2; 2007. pp 629–633. https://doi.org/10.1007/978-3-642-22027-2_57.
Potenziani M, Callieri M, Dellepiane M, Corsini M, Scopigno R. 3DHOP: 3D heritage online presenter. Comput Graph. 2015. https://doi.org/10.1016/j.cag.2015.07.001.
De Smet P. Reconstruction of ripped-up documents using fragment stack analysis procedures. Forensic Sci Int. 2008;176(2–3):124–36. https://doi.org/10.1016/j.forsciint.2007.07.013.
Assael Y, Sommerschield T, Prag J. Restoring ancient text using deep learning: a case study on Greek epigraphy. 2019. https://doi.org/10.18653/v1/d19-1668.
Smith DA, Rydberg-Cox JA, Crane G. The Perseus Project: a digital library for the humanities. Lit Linguist Comput. 2000. https://doi.org/10.1093/llc/15.1.15.
Sutskever I, Vinyals O, Le QV. Sequence to sequence learning with neural networks. Adv Neural Inf Process Syst. 2014;4(January):3104–12.
Hochreiter S, Schmidhuber J. Long short-term memory. Neural Comput. 1997;9(8):1735–80. https://doi.org/10.1162/neco.1997.9.8.1735.
Luong M-T, Pham H, Manning CD. Effective approaches to attention-based neural machine translation. In: Conference Proceedings - EMNLP 2015: Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics (ACL), 17 Aug 2015; 2015. pp 1412–21.
“Studi Anatomici di Leonardo Da Vinci.” [Online]. https://www.leonardodavinci-italy.it/anatomia/anatomia-umana. Accessed 12 Mar 2020.
Bewes J, Low A, Morphett A, Pate FD, Henneberg M. Artificial intelligence for sex determination of skeletal remains: application of a deep learning artificial neural network to human skulls. J Forensic Leg Med. 2019;62:40–3. https://doi.org/10.1016/j.jflm.2019.01.004.
Szegedy C et al. Going deeper with convolutions. In: Proceedings of IEEE Computer Society Conference Computer Vision Pattern Recognition, Computer Vision Foundation, vol 07, 12-June; 2015. pp. 1–9. https://doi.org/10.1109/CVPR.2015.7298594.
“Pretrained GoogLeNet convolutional neural network - MATLAB googlenet - MathWorks Italia.” [Online]. https://it.mathworks.com/help/deeplearning/ref/googlenet.html;jsessionid=6e2403278b76bcd1b70f7c832bb6. Accessed 24 Feb 2020.
Thomas D. Methods of estimating height from parts of skeleton. Med Reconstr New York. 1884;46:293–6.
Pearson K. IV. Mathematical contributions to the theory of evolution.—V. On the reconstruction of the stature of prehistoric races. Philos Trans R Soc Ser. 1899;192:169–244.
Czibula G, Ionescu VS, Miholca DL, Mircea IG. Machine learning-based approaches for predicting stature from archaeological skeletal remains using long bone lengths. J Archaeol Sci. 2016;69:85–99. https://doi.org/10.1016/j.jas.2016.04.004.
Feldman J, Rojas R. Neural networks: a systematic introduction. Berlin: Springer; 2013.
Mitchell M. An introduction to genetic algorithms. Cambridge: Bradford Books; 1998.
Richard DGS, Duda O, Hart PE. Pattern Classification. 2nd ed. New York: Wiley; 2001.
Russell PNS. Artificial intelligence—a modern approach. Prentice Hall International Series in Artificial Intelligence. Upper Saddle River: Prentice Hall; 2003.
Mean absolute error: Wikipedia [Online]. https://en.wikipedia.org/wiki/Mean_absolute_error. Accessed 17 Feb 2020.
Standard error: Wikipedia. [Online]. https://en.wikipedia.org/wiki/Standard_error#Estimate. Accessed 17 Feb 2020.
Del Ser J, et al. Bio-inspired computation: where we stand and what’s next. Swarm Evol Comput. 2019;48(April):220–50. https://doi.org/10.1016/j.swevo.2019.04.008.
Singh B, Sohal HS. Estimation of stature from clavicle in Punjabis; a preliminary report. Indian J Med Res. 1952;40(1):67–71.
Tibbetts GL. Estimation of stature from the vertebral column in American Blacks. J Forensic Sci. 1981;26(4):11427J. https://doi.org/10.1520/jfs11427j.
Holland TD. Estimation of adult stature from fragmentary tibias. J Forensic Sci. 1992;37(5):13309J. https://doi.org/10.1520/jfs13309j.
Kozak J. Stature reconstruction from long bones. The estimation of the usefulness of some selected methods for skeletal populations. Var Evol. 1996;5:83–94.
Chiba M, Terazawa K. Estimation of stature from somatometry of skull. Forensic Sci Int. 1998;97(2–3):87–92. https://doi.org/10.1016/S0379-0738(98)00145-5.
Goldewijk GK, Jacobs J. The relation between stature and long bone length in the Roman Empire. 2013.
Jasuja OP, Singh G. Estimation of stature from hand and phalange length. J Indian Acad Forensic Med. 2004;26:1–24.
Ryan I, Bidmos M. Skeletal height reconstruction from measurements of the skull in Indigenous South Africans. Forensic Sci Int. 2007;167:16–211. https://doi.org/10.1016/j.forsciint.2006.06.003.
Raxter MH, Ruff CB, Azab A, Erfan M, Soliman M, El-Sawaf A. Stature estimation in ancient Egyptians: a new technique based on anatomical reconstruction of stature. Am J Phys Anthropol. 2008;136(2):147–55. https://doi.org/10.1002/ajpa.20790.
Champa Pal D, Datta AK. Estimation of stature from radius length in living adult Bengali males. Indian J Basic Appl Med Res. 2014;3(2):380–9.
Raxter M, Auerbach B, Ruff C. Revision of the fully technique for estimating statures. Am J Phys Anthropol. 2006;130:374–84. https://doi.org/10.1002/ajpa.20361.
Feldesman MR, Fountain RL. ‘Race’ specificity and the femur/stature ratio. Am J Phys Anthropol. 1996;100(2):207–24. https://doi.org/10.1002/(SICI)1096-8644(199606)100:2<207:AID-AJPA4>3.0.CO;2-U.
Boni G. Il metodo negli scavi archeologici, vol. IV, serie, no. XCIV. Arbor Sapientiae, 1901.
Ducke B, Brandenburg A. Archaeological predictive modelling in intelligent network structures. In: CAA The Digital Heritage of Archaeology. Proceedings of the 30th CAA conference held at Heraklion. In: Doerr M, Sarris A, editors. 2003 pp. 1–8.
Caspari G, Crespo P. Convolutional neural networks for archaeological site detection—finding ‘princely’ tombs. J Archaeol Sci. 2019. https://doi.org/10.1016/j.jas.2019.104998.
Verdonck L, Launaro A, Vermeulen F, Millett M. Ground-penetrating radar survey at Falerii Novi: a new approach to the study of Roman cities. Antiquity. 2020;94(375):705–23. https://doi.org/10.15184/aqy.2020.82.
Righetti G, Serafini S, Rueda FB, Church W, Garnero G. Sotto Nuvole, sotto la Foresta: applicazioni Tecnologiche Lidar e di Intelligenza Artificiale per Nuove prospettive nel Sito monumentale di Kuelap-Perù. Archeomatica; In Michele Fasolo editors. 2020;1:6–13.
Chapman P et al. VENUS, Virtual ExploratioN of Underwater Sites. In: The 7th International Symposium on Virtual Reality, Archaeology and Cultural Heritage VAST; 2006. pp. 1–8.
Jeansoulin R, Papini O. Underwater archaeological knowledge analysis and representation in the VENUS Project: a preliminary draft. In: XXI International CIPA Symposium; Greece. 2006. pp. 1–6.
Baum EB. On the capabilities of multilayer perceptrons. J Complex. 1988;4(3):193–21515. https://doi.org/10.1016/0885-064X(88)90020-9.
Mackay DJC. A practical Bayesian framework for backpropagation networks. Neural Comput. 1992;4:448–72.
Kohonen T. Self-organized formation of topologically correct feature maps. Biol Cybern. 1982;43(1):59–69. https://doi.org/10.1007/BF00337288.
Pascoal A, Silvestre C, Oliveira P. Vehicle and mission control of single and multiple autonomous marine robots. Adv Unmanned Mar Veh. 2005. https://doi.org/10.1049/PBCE069E_ch17.
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Mantovan, L., Nanni, L. The Computerization of Archaeology: Survey on Artificial Intelligence Techniques. SN COMPUT. SCI. 1, 267 (2020). https://doi.org/10.1007/s42979-020-00286-w
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DOI: https://doi.org/10.1007/s42979-020-00286-w