Problem Solving in the Brain and by the Machine

Problem Solving in the Brain and by the Machine

Juan A. Barceló (Universitat Autònoma de Barcelona, Spain)
Copyright: © 2009 |Pages: 40
DOI: 10.4018/978-1-59904-489-7.ch002
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Abstract

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 automated archaeologist wants to know the cause of the observed material outcomes of social action. What blocks this goal is a lack of knowledge: it does not know the particular mechanism that caused in the past what it sees in the present. To remove this obstacle it must learn some specific knowledge: how a causal process or processes generated the specific measurable properties determining the observed evidence. To the automated archaeologist, problem solving has the task of devising some causal mechanism that may mediate between the observation and its cause or causes. Consequently, explanatory mechanisms taken in pursuit of that goal can be regarded as problem solving. In other words, explanation is a kind of problem solving where the facts to be explained are treated as goals to be reached, and hypotheses can be generated to provide the desired explanations (Thagard, 1988).
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Looking For Solutions

What does an “intelligent” human being when she tries to solve a problem? In general, she uses the word “problem” to mean different things:

  • As a question to be answered,

  • As a series of circumstances that hinder the attainment of an objective,

  • As a proposition directed to verify the way some results are known.

Research in cognitive sciences suggests “Problem solving is any goal-directed sequence of cognitive operations” (Anderson, 1980, p. 257). According to Sloman (1987) “to have a goal” is to use a symbolic structure represented in some formalism to describe a state of affairs to be produced, preserved or prevented. Then, any rational agent, be artificial or natural, has a “problem” when an intention or goal cannot be achieved directly. Jackson (1983) summarizes this type of approach as:

PROBLEM= GOAL+OBSTACLE

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 automated archaeologist wants to know the cause of the observed material outcomes of social action. What blocks this goal is a lack of knowledge: it does not know the particular mechanism that caused in the past what it sees in the present. To remove this obstacle it must learn some specific knowledge: how a causal process or processes generated the specific measurable properties determining the observed evidence. To the automated archaeologist, problem solving has the task of devising some causal mechanism that may mediate between the observation and its cause or causes. Consequently, explanatory mechanisms taken in pursuit of that goal can be regarded as problem solving. In other words, explanation is a kind of problem solving where the facts to be explained are treated as goals to be reached, and hypotheses can be generated to provide the desired explanations (Thagard, 1988).

Problem solving has been defined as the successive addition of knowledge until the obstacle, which prevented goal achievement, is surmounted (Newell & Simon, 1972). A cognitive machine will solve a problem just by adding knowledge to a situation where it identifies some lack of knowledge. Therefore, a foundation prescriptive rule, one that is so obvious that we always forget it in real life: if you want to solve problems effectively in a given complex domain, you should have as much knowledge or information as you can about that domain.

We cannot use any bit of knowledge we wish, because there is only a finite set of right answers to a problem. Looking for the needed knowledge constitutes part of the procedure. The less knowledge available, the more “problematic,” and troublesome is the solution and the more difficult will be to produce a result. In this sense “problematic” means “poor in knowledge.” This is true for archaeology as for any other scientific discipline. It is true for both humans and for robots!

When there is insufficient knowledge, a problem cannot be solved. The robot needs specific knowledge for specifying what it knows and what it wants to do (goal). Acquiring this knowledge implies solving a previous problem (sub-goal). Each of the new sub-goals defines a problem that can be attacked independently. Problem decomposition constitutes, at the same time, a problem. Finding a solution to each sub-goal will require fewer steps than solving the overall compound goal. The idea is:

TO DECOMPOSE THE PROBLEM
If you want to reach the objective G,
And, it is not fulfilled using the 
previous condition C,
Then, 
Look for sub-goal C
Once C has been attained, 
Then, 
Proceed until G.

Complete Chapter List

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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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