Page 262 - DCAP310_INTRODUCTION_TO_ARTIFICIAL_INTELLIGENCE_AND_EXPERT_SYSTEMS

Introduction to Artificial Intelligence & Expert Systems

Notes General learning model is depicted in figure 14.1 where the environment has been included as
a part of the overall learner system. The environment may be regarded as either a form of nature
which produces random stimuli or as a more organized training source such as a teacher which
provides carefully selected training examples for the learner component. The actual form of
environment used will depend on the particular learning paradigm. In any case, some
representation language must be assumed for communication between the environment and
the learner. The language may be the same representation scheme as that used in the knowledge
base (such as a form of predicate calculus). When they are chosen to be the same, we say the
single representation trick is being used. This usually results in a simpler implementation since
it is not necessary to transform between two or more different representations. For some systems
the environment may be a user working at a keyboard. Other systems will use program modules
to simulate a particular environment.

Notes In even more realistic cases the system will have real physical sensors which
interface with some world environment. Inputs to the learner component may be physical
stimuli of some type or descriptive, symbolic training examples.

The information conveyed to the learner component is used to create and modify knowledge
structures in the knowledge base. This same knowledge is used by the performance component
to carry out some tasks, such as solving a problem playing a game or classifying instances of
some concept given a task, the performance component produces a response describing its
action in performing the task. The critic module then evaluates this response relative to an
optimal response. Feedback, indicating whether or not the performance was acceptable, is then
sent by the critic module to the learner component for its subsequent use in modifying the
structures in the knowledge base. If proper learning was accomplished, the system’s performance
will have improved with the changes made to the knowledge base.

The cycle described above may be repeated a number of times until the performance of the
system has reached some acceptable level, until a known learning goal has been reached, or
until changes ceases to occur in the knowledge base after some chosen number of training
examples have been observed. There are several important factors which influence a system’s
ability to learn in addition to the form of representation used. They include the types of training
provided, the form and extent of any initial background knowledge, the type of feedback
provided, and the learning algorithms used. The type of training used in a system can have a
strong effect on performance, much the same as it does for humans. Training may consist of
randomly selected instance or examples that have been carefully selected and ordered for
presentation. The instances may be positive examples of some concept or task a being learned,
they may be negative, or they may be mixture of both positive and negative. The instances may
be well focused using only relevant information, or they may contain a variety of facts and
details including irrelevant data. Domain-specific learning theories of development hold that
we have many independent, specialized knowledge structures, rather than one cohesive
knowledge structure. Thus, training in one domain may not impact another independent domain.
For example, core knowledge theorists believe we have highly specialized functions that are
independent of one another. Jean Piaget’s theory of development, on the other hand, believed
that knowledge is internalized into a cohesive knowledge structure, favoring the domain-
general learning model.
In this model, the purpose of a learning machine is to be able to infer certain facts of some data
X from a training set selected from X.

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