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

trainer, who supplies the input-output training instances. The learning system
adapts its parameters by some algorithms to generate the desired output
patterns from a given input pattern. In absence of trainers, the desired output
for a given input instance is not known, and consequently the learner has to
adapt its parameters autonomously. Such type of learning is termed
‘unsupervised learning’. The third type called the reinforcement learning
bridges a gap between supervised and unsupervised categories. In
reinforcement learning, the learner does not explicitly know the input-output
instances, but it receives some form of feedback from its environment. The
feedback signals help the learner to decide whether its action on the
environment is rewarding or punishable. The learner thus adapts its
parameters based on the states (rewarding / punishable) of its actions. Among
the supervised learning techniques, the most common are inductive and
analogical learning. The inductive learning technique, presented in the
chapter, includes decision tree and version space based learning. Analogical
learning is briefly introduced through illustrative examples. The principle of
unsupervised learning is illustrated here with a clustering problem. The
section on reinforcement learning includes Q-learning and temporal difference
learning. A fourth category of learning, which has emerged recently from the
disciplines of knowledge engineering, is called ‘inductive logic
programming’. The principles of inductive logic programming have also been
briefly introduced in this chapter. The chapter ends with a brief discussion on
the ‘computational theory of learning’. With the background of this theory,
one can measure the performance of the learning behavior of a machine from
the training instances and their count.
13.2 Supervised Learning
As already mentioned, in supervised learning a trainer submits the inputoutput
exemplary patterns and the learner has to adjust the parameters of the
system autonomously, so that it can yield the correct output pattern when
excited with one of the given input patterns. We shall cover two important
types of supervised learning in this section. These are i) inductive learning and
ii) analogical learning. A number of other supervised learning techniques
using neural nets will be covered in the next chapter.
13.2.1 Inductive Learning
In supervised learning we have a set of {xi, f (xi)} for 1≤ i ≤ n, and our aim is
to determine ‘f’ by some adaptive algorithm. The inductive learning [7-12] is
a special class of the supervised learning techniques, where given a set of {xi,
f(xi)} pairs, we determine a hypothesis h(xi) such that h(xi ) ≈ f(xi), ∀i. A
natural question that may be raised is how to compare the hypothesis h that
approximates f. For instance, there could be more than one h(xi) where all of