Page 74 - DCAP506_ARTIFICIAL_INTELLIGENCE

Artificial Intelligence

Notes 5.2 Approaches of Knowledge Representation

Knowledge representation is an area in artificial intelligence that is concerned with how to
formally “think”, that is, how to use a symbol system to represent “a domain of discourse” – that
which can be talked about, along with functions that may or may not be within the domain of
discourse that allow inference (formalized reasoning) about the objects within the domain of
discourse to occur. Generally speaking, some kind of logic is used both to supply a formal
semantics of how reasoning functions apply to symbols in the domain of discourse, as well as to
supply (depending on the particulars of the logic), operators such as quantifiers, modal operators,
etc. that, along with an interpretation theory, give meaning to the sentences in the logic.
When we design a knowledge representation (and a knowledge representation system to interpret
sentences in the logic in order to derive inferences from them) we have to make trades across a
number of design spaces, described in the following sections. The single most important decision
to be made, however is the expressivity of the KR. The more expressive, the easier (and more
compact) it is to “say something”. However, more expressive languages are harder to
automatically derive inferences from.

Example: An example of a less expressive KR would be propositional logic. An example
of a more expressive KR would be autoepistemic temporal modal logic.

Less expressive KRs may be both complete and consistent (formally less expressive than set
theory). More expressive KRs may be neither complete nor consistent.
The key problem is to find a KR (and a supporting reasoning system) that can make the inferences
your application needs in time, that is, within the resource constraints appropriate to the problem
at hand. This tension between the kinds of inferences an application “needs” and what counts as
“in time” along with the cost to generate the representation itself makes knowledge
representation engineering interesting. There are representation techniques such as frames,
rules and semantic networks which have originated from theories of human information
processing.

Did u know? The fundamental goal of knowledge representation is to represent knowledge
in a manner as to facilitate inferencing (i.e. drawing conclusions) from knowledge.

5.2.1 Frames

Frames were proposed by Marvin Minsky in his 1974 article “A Framework for Representing
Knowledge.” A frame is an artificial intelligence data structure used to divide knowledge into
substructures by representing “stereotyped situations.” Frames are connected together to form
a complete idea.

Frame Structure

The frame contains information on how to use the frame, what to expect next, and what to do
when these expectations are not met. Some information in the frame is generally unchanged
while other information, stored in “terminals,” usually change.

Did u know? Different frames may share the same terminals.

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