Information Technology Reference
In-Depth Information
Expert systems apply domain-specific knowledge and reasoning methods to
solve complex and specific problems usually settled only by human experts, so
that to construct intelligent computer programs with similar problem solving
capabilities as experts. They can make explanations about decision making
procedure and learn to acquire related problem solving knowledge. The first
generation of expert systems (such as DENDRAL, MACSYMA, etc.) had highly
professional and specific problem solving capabilities, yet they lacked
completeness and portability in architecture, and were weak in problem solving.
The second generation of expert systems (such as MYCIN, CASNET,
PROSPECTOR, HEARSAY, etc.) was subject-specific professional application
system. They were complete in architecture with better portability, and were
improved in aspects such as human-machine interface, explanation mechanisms,
knowledge acquisition, uncertain reasoning, enhanced expert system knowledge
representation, heuristics and generality of reasoning, etc.
2. Multi-domain single-paradigm intelligent system
Such systems contain knowledge about multiple domains, yet only process
problems of a certain paradigm. Examples include most distributed problem
solving system and multi-expert system. Generally, expert system development
tools and environments are used to construct such large-scale synthetical
intelligent systems.
Since intelligent systems are widely applied to various domains such as
engineering technology, social economics, national defense affairs and ecological
environment, several requirements are put forward for intelligent systems. To
solve the many real-world problems such as medical diagnosis, economic
planning, military commanding, financial projects, crop planting and
environment protection, expert knowledge and experience of multiple domains
might be involved. Many existing expert systems are single-subject, specific
micro expert systems, which might not satisfy the users' practical demands. To
construct multi-domain single-paradigm intelligent systems might be an
approach to meet the users' requirements in certain degrees. Characteristics of
such systems include:
(1) solve the user's real-world complex problems;
(2) adopt
knowledge
and
experience
of
multiple
domains,
disciplines
and
professionals for cooperative problem solving;
