Knowledge :
Knowledge is the collection of facts, inference rules etc. which can be used for a particular purpose. Knowledge requires the use of data and information. It combines relationships, correlations, dependencies with data and information.
The basic components of knowledge are:
• A set of collected data
• A form of belief or hypothesis
• A kind of information.
Knowledge is different from data. Data is the collection of raw materials whereas knowledge is the collection of some well specified inference rules and facts. Knowledge is also different from belief and hypothesis. Belief is any meaningful and coherent expression that can be represented. Belief may be true or false.
A hypothesis is a justified belief that is not known to be true. A hypothesis is a belief which is backed up with some supporting evidence but it may still be false. So knowledge can be defined as true justified knowledge
It becomes clear that particular knowledg are presentation models allow for morespecific more powerful problem solving mechanisms that operate on them. Examine specific techniques that can be used for representing & manipulating knowledge within programs.
Representation And Mappings
Facts :- truths in some relevant world
These are the things we want to represent.Representations of facts in some chosen formalism. Things we are actually manipulating.
Structuring these entities is as two levels.The knowledge level, at which facts concluding each agents behavior & current goals are described.The Symbol Level: At which representations of objects at the knowledge level are defined in terms of symbols that can be manipulated by programs.
Representation mappings:
• Forward Representation mappings: It maps from facts to representations.
• Backward Representation mappings: It maps from representations to facts.
Knowledge Based Systems:
Knowledge based systems get their power from the expert knowledge that has been coded into facts, rules, heuristics and procedures. The knowledge is stored in a knowledge base separate from the control and
inferencing components. Knowledge is important and essential for knowledge based intelligent.
•Any choice of representation will depend on the type of problem to be solved and the inference methods available.
• Knowledge may be vague, contradictory or incomplete.
• Thus, knowledge is information about objects, concepts and relationships
that are assumed to exist in a particular area of interest.
Approaches to knowledge Representation:
• Representational adequacy the ability to represent all of the kinds of knowledge that are needed in that domain.
• Inferential Adequacy: – the ability to manipulate the representation structures in such a way as to derive new structures corresponding to new knowledge inferred from ol.
• Inferential Efficiency: – the ability to incorporate into the knowledge structure
additional information that can be used to focus the attention of the inference
mechanism in the most promising directions.
• Acquisitioned Efficiency: – the ability to acquire new information easily. The simplest
case involves direct insertion by a person of new knowledge into the database.
