Page 49 - DCAP310_INTRODUCTION_TO_ARTIFICIAL_INTELLIGENCE_AND_EXPERT_SYSTEMS

Unit 3: Representation of Knowledge

judge engages in a natural language conversation with a human and a machine designed to Notes
generate performance indistinguishable from that of a human being. All participants are separated
from one another. If the judge cannot reliably tell the machine from the human, the machine is
said to have passed the test. The test does not check the ability to give the correct answer; it
checks how closely the answer resembles typical human answers. The conversation is limited to
a text-only channel such as a computer keyboard and screen so that the result is not dependent
on the machine’s ability to render words into audio. The test was introduced by Alan Turing in
his 1950 paper “Computing Machinery and Intelligence,” which opens with the words: “I propose
to consider the question, ‘Can machines think?’” Since “thinking” is difficult to define, Turing
chooses to “replace the question by another, which is closely related to it and is expressed in
relatively unambiguous words.” Turing’s new question is: “Are there imaginable digital
computers which would do well in the imitation game?” This question, Turing believed, is one
that can actually be answered. In the remainder of the paper, he argued against all the major
objections to the proposition that “machines can think”.
Self Assessment

State whether the following statements are true or false:
7. KQML, is a language and protocol for communication among software agents and
knowledge-based systems.
8. The manipulations are the computational equivalent of reasoning.
9. The searching and matching operations consume least amount of computation time in AI
systems.
3.4 Knowledge Acquisition

Knowledge acquisition is the process of extracting, structuring and organizing knowledge from
one source, usually human experts, so it can be used in software such as an ES. This is often the
major obstacle in building an Expert System (ES).
There are three main topic areas central to knowledge acquisition that require consideration in
all ES projects. Firstly, the domain must be evaluated to determine if the type of knowledge in
the domain is suitable for an ES. Secondly, the source of expertise must be identified and evaluated
to ensure that the specific level of knowledge required by the project is provided. Thirdly, if the
major source of expertise is a person, the specific knowledge acquisition techniques and
participants need to be identified. An ES attempts to replicate in software the reasoning/pattern-
recognition abilities of human experts who are distinctive because of their particular knowledge
and specialized intelligence. ES should be heuristic and readily distinguishable from algorithmic
programs and databases. Further, ES should be based on expert knowledge, not just competent
or skillful behavior.

Domains

Several domain features are frequently listed for consideration in determining whether an ES is
appropriate for a particular problem domain. Several of these caveats relate directly to knowledge
acquisition. Firstly, bona fide experts, people with generally acknowledge expertise in the
domain, must exist. Secondly, there must be general consensus among experts about the accuracy
of solutions in a domain. Thirdly, experts in the domain must be able to communicate the details
of their problem solving methods. Fourthly, the domain should be narrow and well defined and
solutions within the domain must not require common sense.

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