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
OnDemand PDF Download:
$30.00
List Price: $37.50

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).
Chapter Preview
Top

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

Search this Book:
Reset