p.1 Philosophers have been trying for over two thousand years to understand and resolve two big questions
of the universe: how does a human mind work, and can non-humans have minds? However, these questions are
still unanswered.
p.4 To build an intelligent computer system, we have to capture, organise and use human expert knowledge
in some narrow area of expertise.
p.18 A machine is thought intelligent if it can achieve human-level performance in some cognitive task.
To build an intelligent machine, we have to capture, organise and use human expert knowledge in some problem area.
p.20 One of the main difficulties in building intelligent machines, or in other words in knowledge engineering,
is the "knowledge acquisition bottleneck" - extracting knowledge from human experts.
p.25 Knowledge is a theoretical or practical understanding of a subject or a domain. Knowledge
is also the sum of what is currently known... Those who possess knowledge are called experts... Anyone can
be considered a domain expert if he or she has deep knowledge (of both facts and rules) and strong practical experience in
a particular domain... most experts are capable of expressing their knowledge in the form of rules for problem solving.
p.35 In a rule-based expert system, the domain knowledge is represented by a set of IF-THEN production
rules and data is represented by a set of facts about the current situation. The inference engine compares each rule
stored in the knowledge base with facts contained in the database. When the IF (condition) part of the rule matches a fact,
the rule is fired and its THEN (action) part is executed... The matching of the rule IF parts to the facts produces inference
chains.
p.131 Knowledge in a computer can be represented through several techniques. In the previous chapters, we
considered rules. In this chapter, we will use frames for the knowledge representation... A frame
is a data structure with typical knowledge about a particular object or concept. Frames... are used to capture and
represent knowledge in a frame-based expert system.
p.165 "The computer hasn't proved anything yet", angry Garry Kasparov, the world chess champion, said after
his defeat in New York in May 1997, "If we were playing a real competitive match, I would tear down Deep Blue into pieces."
p.301 The process of building an intelligent system begins with gaining an understanding of the
problem domain. We first must assess the problem and determine what data are available and what is needed to solve
the problem. Once the problem is understood, we can choose an appropriate tool and develop the system with this tool.
The process of building intelligent knowledge-based systems is called knowledge engineering.
p.305 Quite often the experts are unaware of what knowledge they have and the problem-solving strategy
they use, or are unable to verbalize it... Understanding the problem domain is critical for building intelligent
systems. A classical example is given by Donald Mitchie (1982). A cheese factory had a very experienced cheese-tester who
was approaching retirement age. The factory manager decided to replace him with an "intelligent machine". The human tester
tested the cheese by sticking his finger into a sample and deciding if it "felt right". So it was assumed the machine had
to do the same - test for the right surface tension. But the machine was useless. Eventually, it turned out that the human
tester subconsciously relied on the cheese's smell rather than on its surface tension and used his finger just to break the
crust and let the aroma out.
p.305 We must not make a detailed analysis of the problem before [first creating and then] evaluating
the prototype. This involves creating an intelligent system - or, rather, a small version of it - and testing it
with a number of test cases... The main goal of the prototyping phase is to obtain a better understanding of the problem...
As soon as the prototype begins functioning satisfactorily, we can assess what is actually involved in developing a full-scale
system... The development of an intelligent system is, in fact, an evolutionary process.
p.349 Data is what we collect and store, and knowledge is what helps us to make informed decisions. The
extraction of knowledge from data is called data mining... The ultimate goal of data mining is to
discover knowledge.
p.352 The most popular tool used for data mining is a decision tree. A decision tree can be defined
as a map of the reasoning process... A decision tree consists of nodes, branches and leaves.
p.358 Decision trees are as good as the data they represent.
