%0 Generic
%A Barceló, Juan A.
%C Bonn
%D 2008
%E Posluschny, A.
%E Lambers, K.
%E Herzog, I.
%F propylaeumdok:511
%P 413-417
%R 10.11588/propylaeumdok.00000511
%T Towards a true automatic archaeology: integrating technique and theory
%U https://archiv.ub.uni-heidelberg.de/propylaeumdok/511/
%X The question of whether it is possible to automate the scientific process is of both great theoretical interest and increasing practical importance because, in many scientific areas, data is being generated much faster than they can be effectively analyzed. I describe here a virtual robotic system which can be physically implemented that applies techniques from artificial intelligence to carry out cycles of scientific experimentation. I am exploring an analogy with the idea of “intelligent” machine, to understand the way we, archaeologists, think. If a computer can be programmed to perform human-like tasks it offers a “model” of the human activity that is less open to argument than the empirical explanations that are normal in philosophy. The purpose is to understand how intelligent behavior in archaeology is possible.

%0 Generic
%A Barceló, Juan A.
%A Maximiano, Alfredo
%C Bonn
%D 2008
%E Posluschny, A.
%E Lambers, K.
%E Herzog, I.
%F propylaeumdok:524
%R 10.11588/propylaeumdok.00000524
%T Some notes regarding distributional analysis of spatial data
%U https://archiv.ub.uni-heidelberg.de/propylaeumdok/524/
%X The purpose of geostatistics and other quantitative spatial analysis methods is the characterization of the processes having generated the spatial distribution of archaeological data. In this paper1 we investigate whether such methods can be used to distinguish the regularity or randomness of the social event or events having generated the observed spatial distribution. Our hypothesis is that only statistically significant deviations from spatial randomness can be interpreted as intentional clustering. Archaeological data distributions are best characterized in terms of spatial processes which are symmetrical around a central mean.