Knowledge Acquisition
Question b
Introduction
The notion of `knowledge level ' was introduced by Allen Newell more than a decade ago . Ever since it has provided a common perspective for researchers in Artificial Intelligence (AI) and in knowledge systems in particular. Its impact has been tremendous. Newell managed to make explicit what had become common practice in AI, namely talking about intelligent systems in a language of `knowing ' and `wanting '. Moreover, he gave this language a role in systems engineering by postulating the knowledge level as a computer systems level to be studied in line with other levels such as the register-transfer level or the symbol level. It is no surprise then that Newell 's treatment of knowledge was particularly attractive to the system oriented mind of computer scientists who are, after all, still the majority of AI researchers.
The notion of knowledge level has been used most visibly within the so called modelling approaches toward knowledge systems. In these approaches developing a knowledge based system is viewed as the construction of a series of models related to some (problem solving) behaviour. In particular the knowledge level model is a model in terms of the knowledge that rationalises that behaviour. It has become `en vogue ' to assimilate the knowledge level idea in any encompassing treatment of knowledge systems. It ties together and to some extend unifies different approaches toward the theory and practice of knowledge systems
No doubt taking a knowledge level perspective has greatly improved our understanding of what knowledge systems are and how we can build them. For example, it has provoked a profound shift in knowledge acquisition: rather than extracting knowledge from an expert the aim of knowledge acquisition is to build a consolidated model of an expert 's problem solving behaviour in terms of knowledge. Nevertheless the knowledge level is not beyond critique and several authors have pointed out problems with it. Some of
Introduction
The notion of `knowledge level ' was introduced by Allen Newell more than a decade ago . Ever since it has provided a common perspective for researchers in Artificial Intelligence (AI) and in knowledge systems in particular. Its impact has been tremendous. Newell managed to make explicit what had become common practice in AI, namely talking about intelligent systems in a language of `knowing ' and `wanting '. Moreover, he gave this language a role in systems engineering by postulating the knowledge level as a computer systems level to be studied in line with other levels such as the register-transfer level or the symbol level. It is no surprise then that Newell 's treatment of knowledge was particularly attractive to the system oriented mind of computer scientists who are, after all, still the majority of AI researchers.
The notion of knowledge level has been used most visibly within the so called modelling approaches toward knowledge systems. In these approaches developing a knowledge based system is viewed as the construction of a series of models related to some (problem solving) behaviour. In particular the knowledge level model is a model in terms of the knowledge that rationalises that behaviour. It has become `en vogue ' to assimilate the knowledge level idea in any encompassing treatment of knowledge systems. It ties together and to some extend unifies different approaches toward the theory and practice of knowledge systems
No doubt taking a knowledge level perspective has greatly improved our understanding of what knowledge systems are and how we can build them. For example, it has provoked a profound shift in knowledge acquisition: rather than extracting knowledge from an expert the aim of knowledge acquisition is to build a consolidated model of an expert 's problem solving behaviour in terms of knowledge. Nevertheless the knowledge level is not beyond critique and several authors have pointed out problems with it. Some of
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