Artificial Intelligence and Soft Computing: Behavioral ... - Arteimi.info

Analogical reasoning has been successfully realized in many systems.
Winston’s analogical system [14] was designed to demonstrate that the
relationship between acts and actors in one story can explain the same in
another story. Carbonell’s [1] transformational analogy system employs a new
approach to problem solving. It solves new problems by modifying existing
solutions to problems until they may be applied to new problem instances.
13.3 Unsupervised Learning
In supervised learning the input and the output problem instances are
supplied and the learner has to construct a mapping function that generates
the correct output for a given input pattern. Unsupervised learning, however,
employs no trainer. So, the learner has to construct concepts by experimenting
on the environment. The environment responds but does not identify which
ones are rewarding and which ones are punishable activities. This is because
of the fact that the goals or the outputs of the training instances are unknown;
so the environment cannot measure the status of the activities of the learner
with respect to the goals. One of the simplest ways to construct concept by
unsupervised learning is through experiments. For example, suppose a child
throws a ball to the wall; the ball bounces and returns to the child. After
performing this experiment a number of times, the child learns the ‘principle
of bouncing’. This, of course, is an example of unsupervised learning. Most of
the laws of science were developed through unsupervised learning. For
instance, suppose we want to learn Ohm’s law. What should we do? We
construct a simple circuit with one cell, one potentiometer, one voltmeter and
one ammeter. Each time we set the potentiometer to a position and measure
the reading of the voltmeter and ammeter. After taking around 100 readings,
suppose we plot ‘the current’ against the ‘voltage drop across the
potentiometer’. Then we find that the voltage across the potentiometer is
proportional to the current passing through it, what all of us know to be the
standard Ohm’s law. It is to be noted that we do not perform experiments to
learn a specific law. Rather the experimental results reveal a new concept/law.
Let us take another example to illustrate the principles of concept formation
by unsupervised learning. Suppose, we want to classify animals based on their
speed and height/weight ratio. We, for example, take sample animals and
measure the above features and then plot these on a two dimensional frame.
What result should we derive? It may be found from fig.13.7 that cows,
dogs, tigers and foxes form different classes. Further, there is an overlap in the
classes of foxes and dogs.
Now, if we are given a measured value of the speed and height/ weight
ratio of an unknown animal, we can easily classify it, unless it does not
coincide with the overlapping classes. An overlapped region cannot correctly