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Knowledge level modeling is the process of theorizing over observations about a world and, to some extent, explaining the behavior of an agent as it interacts with its environment. Crucial to the understanding of knowledge level modeling are Allen Newell's notions of the knowledge level, ''operators'', and an agent's ''goal state''. *The ''knowledge level'' refers to the knowledge an agent has about its world. *''Operators'' are what can be applied to an agent to affect its state. *An agent's ''goal state'' is the status reached after the appropriate operators have been applied to transition from a previous, non-goal state. Essentially, knowledge level modeling involves evaluating an agent's world and all possible states and with that information constructing a model that depicts the interrelations and pathways between the various states. With this model, various problem solving methods (i.e. prediction, classification, explanation, tutoring, qualitative reasoning, planning, etc.) can be viewed in a uniform fashion. In (), Menzies proposes a new knowledge level modeling approach, called ''KL''''B'', which specifies that "a knowledge base should be divided into domain-specific facts and domain-independent abstract problem solving inference procedures." In his method, abductive reasoning is used to find assumptions which, when combined with theories, achieve the desired goals of the system. For a good example of abductive reasoning, look at logical reasoning. ==See also== *Knowledge level *Knowledge engineering 抄文引用元・出典: フリー百科事典『 ウィキペディア（Wikipedia）』 ■ウィキペディアで「Knowledge level modeling」の詳細全文を読む スポンサード リンク
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