Artificial Intelligence and Soft Computing: Behavioral ... - Arteimi.info

Reasoning in the presence of imprecision of data and uncertainty of
knowledge is a complex problem. Various tools and techniques have been
devised for reasoning under incomplete data and knowledge. Some of these
techniques employ i) stochastic ii) fuzzy and iii) belief network models [16].
In a stochastic reasoning model, the system can have transition from one
given state to a number of states, such that the sum of the probability of
transition to the next states from the given state is strictly unity. In a fuzzy
reasoning system, on the other hand, the sum of the membership value of
transition from the given state to the next state may be greater than or equal to
one. The belief network model updates the stochastic / fuzzy belief assigned
to the facts embedded in the network until a condition of equilibrium is
reached, following which there would be no more change in beliefs. Recently,
fuzzy tools and techniques have been applied in a specialized belief network,
called a fuzzy Petri net, for handling both imprecision of data and
uncertainty of knowledge by a unified approach [14].
1.4.2 Applications of AI Techniques
Almost every branch of science and engineering currently shares the tools and
techniques available in the domain of AI. However, for the sake of the
convenience of the readers, we mention here a few typical applications, where
AI plays a significant and decisive role in engineering automation.
Expert Systems: In this example, we illustrate the reasoning process
involved in an expert system for a weather forecasting problem with special
emphasis to its architecture. An expert system consists of a knowledge base,
database and an inference engine for interpreting the database using the
knowledge supplied in the knowledge base. The reasoning process of a typical
illustrative expert system is described in Fig. 1.9. PR 1 in Fig. 1.9 represents
i-th production rule.
The inference engine attempts to match the antecedent clauses (IF parts)
of the rules with the data stored in the database. When all the antecedent
clauses of a rule are available in the database, the rule is fired, resulting in
new inferences. The resulting inferences are added to the database for
activating subsequent firing of other rules. In order to keep limited data in the
database, a few rules that contain an explicit consequent (THEN) clause to
delete specific data from the databases are employed in the knowledge base.
On firing of such rules, the unwanted data clauses as suggested by the rule are
deleted from the database.
Here PR1 fires as both of its antecedent clauses are present in the
database. On firing of PR1, the consequent clause “it-will-rain” will be added
to the database for subsequent firing of PR2.