Knowledge representation models are one of the most important areas of research in the field of artificial intelligence. Why one of the most important? Yes, because without knowledge, artificial intelligence cannot exist in principle. Indeed, imagine a person who knows absolutely nothing. For example, he does not even know such elementary things as:
- in order not to die of hunger, it is necessary to periodically eat;
- It is not necessary to go from one side of the city to another if you can use public transport for this purpose.
There are many more such examples, but now you can easily answer the following question: “Can the behavior of such a person be considered reasonable?”. Of course not. That is why, when creating artificial intelligence systems, special attention is paid to knowledge representation models.
To date, has developed a sufficient number of models. Each of them has its own advantages and disadvantages, and therefore for each specific task it is necessary to choose exactly its own model. It will depend not so much on the effectiveness of the task as on how to solve it at all.
Note that knowledge representation models are related to the pragmatic direction of research in the field of artificial intelligence. This direction is based on the assumption that human mental activity is a “black box”. This approach does not raise the question of the adequacy of the knowledge representation models used in computers to the models used by people in similar situations, but only the final result of solving specific problems is considered.
Consider the three most commonly used and popular models of knowledge representation today:
- production models - models based on rules, allow us to represent knowledge in the form of sentences such as: "IF a condition, THEN action". Production models have the disadvantage that when a sufficiently large number of rules are accumulated, they begin to contradict each other;
- network models or semantic networks - as a rule, this is a graph that reflects the meaning of the whole image. The nodes of the graph correspond to the concepts and objects, and the arcs correspond to the relations between the objects;
- frame models - based on such a concept as a frame (eng. frame - frame, frame). A frame is a data structure for representing a conceptual object. Information relating to the frame is contained in its constituent slots. Slots can be terminal or be frames themselves, so forming a whole hierarchical network.
In more detail, the above models of knowledge representation are considered in the corresponding articles.
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Knowledge Representation Models
Terms: Knowledge Representation Models