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In conventional programming approach programming is sequential task that is performed in three sequential steps, first one is designing, then coding/programming and then debugging. On the other hand, Knowledge engineering, we are designing the Expert System that involve different activities like assessment, knowledge acquisition, design, testing, documentations and maintenance. In Conventional programming we only focuses on solution of the problem while in Expert System ES programming focuses in problem. In Knowledge Engineering main philosophy on right knowledge base, because solution will be derived from the generic reasoning machine.
In machine-learning problem space can be represented through concept space, instance space version space and hypothesis space. These problem spaces used the conjunctive space and is very restrictive one and also in the above-mentioned representations of problem spaces, it is not sure that the true concept lies within conjunctive space.
Discuss the case if we have a bigger search space and want to overcome the restrictive nature of conjunctive space, then how can we represent our problem space. Secondly in a given scenario which algorithm is used for our problem space to represent the learning problem.