2.3 Knowledge Representation and Utilisation
In addition to knowledge acquisition, there are two more key issues we have to consider while building an expert system: knowledge representation and utilisation of the acquired knowledge. A number knowledge representa- tion schemes available to the knowledge engi- neer such as production rules, semantic network, frames, logic, and objected-oriented extensions of existing knowledge representation languages. Knowledge utilisation can also be viewed as knowledge maintenance. In another words, it re- quires an up to date knowledge base which can be used sensibly and consistently. The knowledge that human experts possess is dynamic. Normally, expert systems are built incrementally in the sense that 3 The Soft System Concept
Real life problems lie between two extreme ends, namely soft and hard. According to Checkland [3], a soft problem is:
“a problem, usually a real-world prob- lem, which cannot be formulated as a search for an efficient means of achiev- ing a defined end; a problem in which ends, goals, purpose are themselves problematic .”
Thus it is impractical to formulate definite procedures to solve soft problems. Checkland proposes a soft system methodology (SSM) [3] for handling this particular type of real-world problems. The soft systems concept provides a thinking framework to represent the problem en- vironment as a human activity system for visual- ising internal and external environment factors as an integrated entity. This concept also al- lows recognition of the proper place and func- tion of all associated elements. The systems within which the different parties must be in- volved are necessarily complex. The soft sys- tems concept is expected to help to dissolve some of the complexity, and it can also contribute to clarifying the complex problems and thereby op- erate within the perceived environment. The construction of a human activity system should consider the following elements:
0 objective
0 components
0 connectivity
0 transformation
0 subsystems and wider systems
0 measures of performance
2.3 Knowledge Representation and Utilisation In addition to knowledge acquisition, there are two more key issues we have to consider while building an expert system: knowledge representation and utilisation of the acquired knowledge. A number knowledge representa- tion schemes available to the knowledge engi- neer such as production rules, semantic network, frames, logic, and objected-oriented extensions of existing knowledge representation languages. Knowledge utilisation can also be viewed as knowledge maintenance. In another words, it re- quires an up to date knowledge base which can be used sensibly and consistently. The knowledge that human experts possess is dynamic. Normally, expert systems are built incrementally in the sense that 3 The Soft System Concept Real life problems lie between two extreme ends, namely soft and hard. According to Checkland [3], a soft problem is: “a problem, usually a real-world prob- lem, which cannot be formulated as a search for an efficient means of achiev- ing a defined end; a problem in which ends, goals, purpose are themselves problematic .” Thus it is impractical to formulate definite procedures to solve soft problems. Checkland proposes a soft system methodology (SSM) [3] for handling this particular type of real-world problems. The soft systems concept provides a thinking framework to represent the problem en- vironment as a human activity system for visual- ising internal and external environment factors as an integrated entity. This concept also al- lows recognition of the proper place and func- tion of all associated elements. The systems within which the different parties must be in- volved are necessarily complex. The soft sys- tems concept is expected to help to dissolve some of the complexity, and it can also contribute to clarifying the complex problems and thereby op- erate within the perceived environment. The construction of a human activity system should consider the following elements: 0 objective 0 components 0 connectivity 0 transformation 0 subsystems and wider systems 0 measures of performance
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