Computer Systems that Learn

Computer Systems that Learn

Juan A. Barceló (Universitat Autònoma de Barcelona, Spain)
Copyright: © 2009 |Pages: 69
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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Inverse Reasoning

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.

A naïve solution would be to list all possible consequences of the same cause. This universal knowledge base would contain all the knowledge needed to “guess” in a rational way the most probable cause of newly observed effects. This way of solving inverse problems implies a kind of instance-based learning, which represents knowledge in terms of specific cases or experiences and relies on flexible matching methods to retrieve these cases and apply them to new situations. This way of learning, usually called case-based learning, is claimed to be a paradigm of the human way of solving complex diagnostic problems in domains like archaeology. To act as a human expert, a computer system needs to make decisions based on its accumulated experience contained in successfully solved cases. Descriptions of past experiences, represented as cases, are stored in a knowledge base for later retrieval. When the computer sensor perceives a new case with similar parameters, the system searches for stored cases with problem characteristics similar to the new one, finds the closest fit, and applies the solutions of the old case to the new case. Successful solutions are tagged to the new case and both are stored together with the other cases in the knowledge base. Unsuccessful solutions also are appended to the case base along with explanations as to why the solutions did not work.

The suggestion that the intelligent machine should define causal events in terms of the observation of a repeated series of similar events typically relies on a kind of regularity assumption demanding that ‘similar problems have similar solutions’ (Hüllermeier, 2007; Kolodner, 1993). In other words, a learning machine can be broadly defined as any device whose actions are influenced by past experiences, so that learning procedures changes within an agent that over time enable it to perform more effectively within its environment (Arkin, 1998). The idea is that once a system has a rule that fits past data, if the future is similar to the past, the system will make correct predictions for novel instances (Alpaydin, 2004). This mechanism implies the search for maximal explanatory similarity between the situation being explained and some previously explained scenario (Falkenheimer, 1990).

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Table of Contents
Foreword
Jean-Claude Gardin
Acknowledgment
Chapter 1
Juan A. Barceló
The task of this automated archaeologist will be to assign to any artifact, represented by some features, visual or not, some meaning or explanatory... Sample PDF
"Automated" Archaeology: A Useless Endeavor, an Impossible Dream, or Reality?
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Chapter 2
Juan A. Barceló
When a specific goal is blocked, we have a problem. When we know ways round the block or how to remove it, we have less a problem. In our case, the... Sample PDF
Problem Solving in the Brain and by the Machine
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Chapter 3
Juan A. Barceló
Inverse problems are among the most challenging in computational and applied science and have been studied extensively (Bunge, 2006; Hensel, 1991;... Sample PDF
Computer Systems that Learn
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Chapter 4
Juan A. Barceló
Let’s build an automated archaeologist! It is not an easy task. We need a highly complex, nonlinear, and parallel information-processing “cognitive... Sample PDF
An Introduction to Neurocomputing
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Chapter 5
Juan A. Barceló
As we have discussed in previous chapters, an artificial neural network is an information-processing system that maps a descriptive feature vector... Sample PDF
Visual and Non-Visual Analysis in Archaeology
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Chapter 6
Juan A. Barceló
In order to be able to acquire visual information, our automated “observer” is equipped with range and intensity sensors. The former acquire range... Sample PDF
Shape Analysis in Archaeology
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Chapter 7
Juan A. Barceló
In this section, we will consider archaeological textures as the archaeological element’s surface attributes having either tactile or visual... Sample PDF
Texture and Compositional Analysis in Archaeology
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Chapter 8
Spatiotemporal Analysis  (pages 256-296)
Juan A. Barceló
As we have suggested many times throughout the book, the general form of an archaeological problem seems to be “why an archaeological site is the... Sample PDF
Spatiotemporal Analysis
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Chapter 9
Juan A. Barceló
It is obvious that answering the first question is a condition to solve the second. In the same way as human archaeologists, the automated... Sample PDF
An Automated Approach to Historical and Social Explanation
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Chapter 10
Juan A. Barceló
We have already argued that an automated archaeologist cannot understand past social actions by enumerating every possible outcome of every possible... Sample PDF
Beyond Science Fiction Tales
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