STRUCTURED REPRESNTATION OF KNOWLEDGE
Representing knowledge using logical formalism, like predicate logic, has several advantages. They can be combined with powerful inference mechanisms like resolution, which makes reasoning with facts easy. But using logical formalism complex structures of the world, objects and their relationships, events, sequences of events etc. can not be described easily.
A good system for the representation of structured knowledge in a particular domain should posses the following four properties:
Representational Adequacy:- The ability to represent all kinds of knowledge that are needed in that domain.
Inferential Adequacy :- The ability to manipulate the represented structure and infer new structures.
Inferential Efficiency:- The ability to incorporate additional information into the knowledge structure that will aid the inference mechanisms.
Acquisitional Efficiency :- The ability to acquire new information easily, either by direct insertion or by program control.
The techniques that have been developed in AI systems to accomplish these objectives fall under two categories:
Declarative Methods:- In these knowledge is represented as static collection of facts which are manipulated by general procedures. Here the facts need to be stored only one and they can be used in any number of ways. Facts can be easily added to declarative systems without changing the general procedures.
Procedural Method:- In these knowledge is represented as procedures. Default reasoning and probabilistic reasoning are examples of procedural methods. In these, heuristic knowledge of
“How to do things efficiently “can be easily represented.
In practice most of the knowledge representation employ a combination of both. Most of the knowledge representation structures have been developed to handle programs that handle natural language input. One of the reasons that knowledge structures are so important is that they provide a way to represent information about commonly occurring patterns of things . such descriptions are some times called schema. One definition of schema is
“Schema refers to an active organization of the past reactions, or of past experience, which must always be supposed to be operating in any well adapted organic response”.
By using schemas, people as well as programs can exploit the fact that the real world is not random. There are several types of schemas that have proved useful in AI programs. They include
Frames:- Used to describe a collection of attributes that a given object possesses (eg: description of a chair).
Scripts:- Used to describe common sequence of events
(eg:- a restaurant scene).
Stereotypes :- Used to described characteristics of people.
Rule models:- Used to describe common features shared among a set of rules in a production system.
Frames and scripts are used very extensively in a variety of AI programs. Before selecting any specific knowledge representation structure, the following issues have to be considered.
The basis properties of objects , if any, which are common to every problem domain must be identified and handled appropriately.
The entire knowledge should be represented as a good set of primitives.
Mechanisms must be devised to access relevant parts in a large knowledge base.
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