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(3) based on distributed open software, hardware and network environment;
(4) constructed with expert system development tools and environments;
(5) achieve knowledge sharing and knowledge reuse.
3. Single-domain multi-paradigm intelligent system
Such systems contain knowledge of only a single domain, yet process problems
of multiple paradigms. Examples include compound intelligent system.
Generally, knowledge can be acquired through neural network training, and then
transformed into production rules to be used in problem solving by inference
engines.
Multiple mechanisms can be used to process a single problem in problem
solving. Take an illness diagnosis system as an example, both symbolic
reasoning and artificial neural networks can be used. Then, compare and
integrate the results of different methods processing the same problem, through
which correct results might be obtained and unilateralism can be avoided.
4. Multi-domain multi-paradigm intelligent system
Fig. 1.4 illustrates the sketch map of such systems, which contain knowledge of
multiple domains and process problems of different paradigms. Collective
intelligence in the figure means that when processing multiple paradigms,
different processing mechanisms work separately, accomplish different duties,
and cooperate with each other, so that to represent collective intelligent
behaviors.
Collective
Intuition Processor
Connection
…
Symbolic Reasoner
Knowledge Base 1
Knowledge Base 2
Knowledge Base n
…
Fig. 1.4. Multi-domain multi-paradigm intelligent system
Synthetical DSS and KBS belong to this category of intelligent systems. In
such systems, reasoning based abstract thought is based on symbolic processing,
