Page 245 - DCAP208_Management Support Systems

Management Support Systems

Notes Wang (1960)), who were concerned with getting the right answers regardless of how humans
might do it. The interdisciplinary field of cognitive science brings together computer models
from AI and experimental techniques from psychology to try to construct precise and testable
theories of the workings of the human mind.

Thinking Rationally: The Laws of Thought Approach

The Greek philosopher Aristotle was one of the first to attempt to codify “right thinking,” that
is, irrefutable reasoning processes. His famous syllogisms provided patterns for argument
structures that always gave correct conclusions given correct premises.

Example: “Socrates is a man; all men are mortal; therefore Socrates is mortal.” These
laws of thought were supposed to govern the operation of the mind, and initiated the field of
logic.
The development of formal logic in the late nineteenth and early twentieth centuries, provided
a precise notation for statements about all kinds of things in the world and the relations between
them. (Contrast this with ordinary arithmetic notation, which provides mainly for equality and
inequality statements about numbers.) By 1965, programs existed that could, given enough time
and memory, take a description of a problem in logical notation and find the solution to the
problem, if one exists. (If there is no solution, the program might never stop looking for it.) The
so-called logicist tradition within artificial intelligence hopes to build on such programs to
create intelligent systems.
There are two main obstacles to this approach. First, it is not easy to take informal knowledge
and state it in the formal terms required by logical notation, particularly when the knowledge
is less than 100% certain. Second, there is a big difference between being able to solve a problem
‘‘in principle” and doing so in practice. Even problems with just a few dozen facts can exhaust the
computational resources of any computer unless it has some guidance as to which reasoning
steps to try first. Although both of these obstacles apply to any attempt to build computational
reasoning systems, they appeared first in the logicist tradition because the power of the
representation and reasoning systems are well-defined and fairly well understood.
Acting Rationally: The Rational Agent Approach

Acting rationally means acting so as to achieve one’s goals, given one’s beliefs. An agent is just
something that perceives and acts. In this approach, AI is viewed as the study and construction
of rational agents.

In the “laws of thought” approach to AI, the whole emphasis was on correct inferences. Making
correct inferences is sometimes part of being a rational agent, because one way to act rationally
is to reason logically to the conclusion that a given action will achieve one’s goals, and then to
act on that conclusion. On the other hand, correct inference is not all of rationality, because there
are often situations where there is no provably correct thing to do, yet something must still be
done. There are also ways of acting rationally that cannot be reasonably said to involve inference.

Example: Pulling one’s hand off of a hot stove is a reflex action that is more successful
than a slower action taken after careful deliberation.
All the “cognitive skills” needed for the Turing Test are there to allow rational actions. Thus, we
need the ability to represent knowledge and reason with it because this enables us to reach good
decisions in a wide variety of situations. We need to be able to generate comprehensible sentences
in natural language because saying those sentences helps us get by in a complex society.

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