Information Technology Reference
In-Depth Information
Language is the starting point for the study of human's thinking in the logic,
as well as for the simulation of human's thinking in the computer science. Topics
related to language are important issues that run through the domain of computer
science. Many subjects of the computer science, such as programming languages
and their formal semantics, knowledge representation and reasoning, and the
natural language processing, are all related to language. Generally speaking,
representation and reasoning are two basic topics in the computer science and the
artificial intelligence. Majority of the intelligent behavior relies on a direct
representation of knowledge, for which the formal logic provides an important
approach.
Knowledge, especially the so-called common knowledge, is the foundation of
intelligent behavior. Intelligent behavior such as analyzing, conjecturing,
forecasting and deciding are all based on the utilization of knowledge.
Accordingly, in order to simulate with computer the intelligent behavior, one
should firstly make knowledge represented in the computer, and then enable the
computer to utilize and reason about the knowledge. Representation and
reasoning are two basic topics on knowledge in the study of artificial intelligence.
They are entirely coincident with the two topics focused by the study of natural
language, i.e., the accurate structure and reasoning of natural languages.
Therefore, the methods and results obtained in logic are also useful for the study
of knowledge in the artificial intelligence. The ability of representation and the
performance of reasoning are a pair of contradictions for any logic system
applied to intelligent systems. A trade-off between such a pair is often necessary.
The logic applied in majority of logic-based intelligent systems is first order
logic or its extensions. The representation ability of first order logic is so strong
that many experts believe that all the knowledge representation problems arising
in the research of artificial intelligence can be carried out within the framework
of first order logic. First order logic is suitable for representing knowledge with
uncertainty. For example, the expression ∃x P(x) states that there exists an object
for which the property P holds, while it is not pointed out that which one is such
an object. For another example, the expression P ∨ Q states that at least one of P
and Q holds, but it is not determined whether P (or Q) really holds. Furthermore,
first order logic is equipted with a complete axiom system, which can be treated
as a standard of reference in the designing of strategies and algorithms on
reasoning. Although first order logic is capable for representing majority of
knowledge, it is not convenient and concise for many applications. Driven by
