Intelligent Systems by Universal Teacher Publications

Miscellaneous Questions

RULE-BASED SYSTEM ARCHITECTURES

Q. Give the advantages of expert system architectures based on decision trees over those of production rules. What are the main disadvantages? (June 98)

Ans. The most common form of architecture used in expert and other types of knowledge- based systems is the production system, also called the rule-based system. This type of system uses knowledge encoded in the form of production rules, that is, if ...then rules. For example,

IF: Condition-1 and Condition-2 and Condition-3
THEN: Take Action-4

IF: The temperature is greater than 200 degrees, and

The water level is low
THEN: Open the safety valve.

A & B & C & D à E & F

Each rule represents a small chunk of knowledge relating to the given domain of expertise. A number of related rules collectively may correspond to a chain of inferences, which lead from some initially known facts to some useful conclusions. When the known facts support the conditions in the rule's left side, the conclusion or action part of the rule is then accepted as known (or at least known with some degree of certainty). The main components of a typical expert system are depicted in the following figure.

Inference in these systems is accomplished by a process of chaining through the rules recursively, either in a forward or backward direction, until a conclusion is reached or until failure occurs.

Decision Tree Architectures

When knowledge can be structured in a top-to-bottom manner, it may be stored in the form of a decision tree. For example, the identification of objects (equipment faults, physical objects, diseases, and the like) can be made through a decision tree structure. Initial and intermediate nodes in the tree correspond to object attributes, and terminal nodes correspond to the identities of objects. Attribute values for an object determine a path to a leaf node in the tree, which contains the object's identification. Each object attribute corresponds to a nonterminal node in the tree and each branch of the decision tree corresponds to an attribute value or set of values.

A segment of a decision tree knowledge structure taken from an expert system used to identify objects such as liquid chemical waste products is illustrated in the following figure.

Each node in the tree corresponds to an identifying attribute such as molecular weight, boiling point, burn test color, or solubility test results. Each branch emanating from a node corresponds to a value or range of values for the attribute such as 20-37 degrees C, yellow, or non soluble in sulfuric acid.

An identification is made by traversing a path through the tree (or network) until the path leads to a unique leaf node which corresponds to the unknown object's identity.

Advantages of Decision Tree Architecture

New nodes and branches can be added to the tree when additional attributes are needed to further discriminate among new objects. As the system gains experience, the values associated with the branches can be modified for more accurate results.

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