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Problem Solving in the Brain and by the Machine
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Author(s): Juan A. Barceló (Universitat Autònoma de Barcelona, Spain)
Copyright: 2009
Pages: 40
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.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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