Page 274 - DCAP310_INTRODUCTION_TO_ARTIFICIAL_INTELLIGENCE_AND_EXPERT_SYSTEMS

Introduction to Artificial Intelligence & Expert Systems

Notes Basic Idea: Use results from one examples problem solving effort next time around. An EBL
accepts 4 kinds of input:

A training example – what the learning sees in the world.
A goal concept – a high level description of what the program is supposed to learn.
An operational criterion - a description of which concepts are usable.
A domain theory – a set of rules that describe relationships between objects and actions in
a domain.

From this EBL computes a generalization of the training example that is sufficient not
only to describe the goal concept but also satisfies the operational criterion. This has two
steps:

Explanation – the domain theory is used to prune away all unimportant aspects of the
training example with respect to the goal concept.
Generalization – the explanation is generalized as far possible while still describing the
goal concept.

Task Make a difference hart between learning and knowledge acquisition.

Self Assessment

State whether the following statements are true or false:
17. A domain theory is perfect or complete if it contains, in principle, all information needed
to decide any question about the domain.
18. EBL uses training examples to make searching for deductive consequences of a domain
theory efficient in practice.
14.10 Summary

Learning is acquiring new knowledge, behaviours, skills, values, preferences or
understanding, and may involve synthesizing different types of information.

Knowledge acquisition is the process of adding new knowledge to a knowledge-base and
refining or otherwise improving knowledge that was previously acquired.
Explanation-based learning (EBL) systems attempt to improve the performance of a
problem solver (PS), by first examining how PS solved previous problems, then modifying
PS to enable it to solve similar problems better (typically, more efficiently) in the future.
Inductive biases can be probabilistically correct or probabilistically incorrect, and if they
are correct, it is good to have as much of them as possible, and if they are incorrect, we are
left worse off than if you had no inductive bias at all.
‘Learning’ is a broad term covering a wide range of processes. We learn (memorize)
multiplication tables, learn (discover how) to walk, learn (build up an understanding of,
then an ability to synthesize) languages.
Performance measures are based on data, and tell a story about whether an agency or
activity is achieving its objectives and if progress is being made toward attaining policy
or organizational goals.

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