Approaches to intelligent control:
What
is Intelligent Control?
v Intelligent control is a class of control techniques
that use various AI computing approaches.
v Intelligent control can be divided into the following
major sub-domains:
•
Neural network control
•
Bayesian
control
•
Fuzzy
(logic) control
•
Neuro-fuzzy
control
•
Expert
Systems
•
Genetic
control
•
Intelligent
agents (Cognitive/Conscious control)
Expert
System:
ü An expert system is software that attempts to provide
an answer to a problem, or clarify uncertainties where normally one or more
human experts would need to be consulted.
ü Expert systems are most common in a specific problem
domain.
ü Methods for simulating the performance of the expert
are
1) The creation
of a so-called "knowledgebase" which uses some knowledge
representation formalism to capture the Subject Matter Expert's (SME) knowledge
2) A process of
gathering that knowledge from the SME and codifying it according to the
formalism, which is called knowledge engineering.
Intelligent
Agents (Cognitive / Conscious Control)
ü Intelligent agents may also learn or use knowledge to
achieve their goals.
ü Example: a reflex machine such as a thermostat is an
intelligent agent, as is a human being, as is a community of human beings
working together towards a goal.
ü It is an autonomous entity in AI which observes and
acts upon an environment (i.e. it is an agent) and directs its activity towards
achieving goals (i.e. it is rational).
APPROACHES
TO INTELLIGENT CONTROL
ü The field of artificial intelligence, or AI, attempts
to understand intelligent entities whose definitions as described by books
gives us four possible goals to pursue in artificial intelligence:
ü A human-centered approach must be an empirical
science, involving hypothesis and experimental confirmation.
ü A rationalist approach involves a combination of
mathematics and engineering.
1. Acting humanly: The Turing Test approach:
ü Proposed by Alan Turing (Turing, 1950)
ü Designed to provide a satisfactory operational
definition of intelligence.
ü Turing defined intelligent behavior as the ability to
achieve human-level performance in all cognitive tasks, sufficient to fool an
interrogator.
ü Roughly speaking, the test he proposed is that the
computer should be interrogated by a human via a teletype, and passes the test
if the interrogator cannot tell if there is a computer or a human at the other
end.
ü Avoided direct physical interaction between the
interrogator and the computer, because physical simulation of a person is
unnecessary for intelligence.
ü The issue of acting like a human comes up primarily
when AI programs have to interact with people, as when an expert system
explains how it came to its diagnosis, or a natural language processing system
has a dialogue with a user. These programs must behave according to certain
normal conventions of human interaction in order to make themselves understood.
2. Thinking humanly: The cognitive
modelling approach
ü If a given program thinks like a human, we need to
determine how humans think.
ü Two ways to do this:
ü through introspection--trying to catch our own
thoughts as they go by
ü through psychological experiments.
ü 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.
ü We will simply note that AI and cognitive science
continue to fertilize each other, especially in the areas of vision, natural
language, and learning.
3. Thinking rationally: The laws of thought
approach
ü These laws of thought were supposed to govern the
operation of the mind, and initiated the field of logic.
ü The development of formal logic provided a precise
notation for statements about all kinds of things in the world and the
relations between them.
ü 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. The so-called logicist tradition
within artificial intelligence hopes to build on such programs to create
intelligent systems.
4. Acting rationally: The rational agent
approach
ü Acting rationally means acting so as to achieve one's
goals, given one's beliefs.
ü An agent is just something that perceives and acts.
ü In this approach, AI is viewed as the study and
construction of rational agents.
