Knowledge Representation | Issues in knowledge representation | Cognitive Science and AI

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knowledge representationKnowledge Representation | Issues in knowledge representation
Fourth and Fifth Semester | Tribhuvan University (TU)
Subject: Cognitive Science and AI | BSc.CSIT

Knowledge Representation
Knowledge representation (KR) is the study of how knowledge about the world can be represented and what kinds of reasoning can be done with that knowledge. Knowledge Representation is the method used to encode knowledge in Intelligent Systems.

Since knowledge is used to achieve intelligent behavior, the fundamental goal of knowledge representation is to represent knowledge in a manner as to facilitate inferencing (i.e. drawing conclusions) from knowledge. A successful representation of some knowledge must, then, be in a form that is understandable by humans, and must cause the system using the knowledge to behave as if it knows it.

Issues in knowledge representation from an AI perspective:

  • How do people represent knowledge?
  • What is the nature of knowledge and how do we represent it?
  • Should a representation scheme deal with a particular domain or should it be general purpose?
  • How expressive is a representation scheme or formal language?
  • Should the scheme be declarative or procedural?
    knowledge representation                                                 Fig: Two entities in knowledge representation

For example: English or natural language is an obvious way of representing and handling facts. Logic enables us to consider the following fact: spot is a dog as dog(spot) We could then infer that all dogs have tails with: ꓯx: dog(x)–> hasatail(x). We can then deduce:
hasatail(Spot)
Using an appropriate backward mapping function the English sentence Spot has a tail can be generated.

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