Page 266 - DCAP310_INTRODUCTION_TO_ARTIFICIAL_INTELLIGENCE_AND_EXPERT_SYSTEMS

Introduction to Artificial Intelligence & Expert Systems

Notes 14.5 Learning by Induction

Inductive learning is essentially learning by example. The process itself ideally implies some
method for drawing conclusions about previously unseen examples once learning is complete.
More formally, one might state: Given a set of training examples, develop a hypothesis that is as
consistent as possible with the provided data. It is worthy of note that this is an imperfect
technique. As Chalmers points out, “an inductive inference with true premises [can] lead to false
conclusions”. The example set may be an incomplete representation of the true population, or
correct but inappropriate rules may be derived which apply only to the example set. A simple
demonstration of this type of learning is to consider the following set of bit-strings (each digit
can only take on the value 0 or 1), each noted as either a positive or negative.
In logic, we often refer to the two broad methods of reasoning as the deductive and inductive
approaches. Deductive reasoning works from the more general to the more specific. Sometimes
this is informally called a “top-down” approach. We might begin with thinking up a theory
about our topic of interest. We then narrow that down into more specific hypotheses that we can
test. We narrow down even further when we collect observations to address the hypotheses.
This ultimately leads us to be able to test the hypotheses with specific data – a confirmation (or
not) of our original theories.
Inductive reasoning works the other way, moving from specific observations to broader
generalizations and theories. Informally, we sometimes call this a “bottom up” approach. In
inductive reasoning, we begin with specific observations and measures, begin to detect patterns
and regularities, formulate some tentative hypotheses that we can explore, and finally end up
developing some general conclusions or theories.

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Caution Learning should be applied after refining the given task.

Task Create a learning chart for a car Driving.

Self Assessment

State whether the following statements are true or false:
9. Deductive reasoning works from the more general to the more specific.

10. Inductive learning is essentially learning by example.

14.6 Generalization and Specialization

Terms such as superclass, subclass, or inheritance come to mind when thinking about the object-
oriented approach. These concepts are very important when dealing with object-oriented
programming languages such as Java, Smalltalk, or C++. For modeling classes that illustrate
technical concepts they are secondary. The reason for this is that modeling relevant objects or
ideas from the real world gives little opportunity for using inheritance (compare the class
diagram of our case study). Nevertheless, we would like to further introduce these terms at this
point in Figure 14.3 shown below:

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