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Sai Chetan
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Asked: 2022-04-03T22:29:05+05:30 In:

What are the issues in knowledge representation in artificial intelligence?

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Q1) What are the issues in knowledge representation in artificial intelligence?

Q2) Discuss Forward Versus Backward Reasoning.

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    2 Answers

    2. I'M ADMIN

      Q2) Discuss Forward Versus Backward Reasoning.

      ANSWER:

      1. The forward reasoning is data-driven approach while backward reasoning is a goal driven.
      2. The process starts with new data and facts in the forward reasoning. Conversely, backward reasoning begins with the results.
      3. Forward reasoning aims to determine the result followed by some sequences. On the other hand, backward reasoning emphasis on the acts that support the conclusion.
      4. The forward reasoning is an opportunistic approach because it could produce different results. As against, in backward reasoning, a specific goal can only have certain predetermined initial data which makes it restricted.
      5. The flow of the forward reasoning is from the antecedent to consequent while backward reasoning works in reverse order in which it starts from conclusion to incipient.

       

    3. I'M ADMIN

      Q1) What are the issues in knowledge representation in artificial intelligence?

       

      knowledge representation:

      • Artificial intelligence is a system that is concerned with the study of understanding, designing and implementing the ways,  associated with knowledge representation to computers.
      • In any intelligent system, representing the knowledge is supposed to be an important technique to encode the knowledge.
      • The main objective of AI system is to design the programs that provide information to the computer, which can be helpful to interact with humans and  solve problems in various fields which require human intelligence.

      Issues in knowledge representation:

      The main objective of knowledge representation is to draw the conclusions from the knowledge, but there are many issues associated with the use of knowledge representation techniques.

      Refer to the above diagram to refer to the following issues.

      Choosing the granularity of representation:
      While deciding the granularity of representation, it is necessary to know the following:

      i. What are the primitives and at what level should the knowledge be represented?

      ii. What should be the number (small or large) of low-level primitives or high-level facts?

      High-level facts may be insufficient to draw the conclusion while Low-level primitives may require a lot of storage.

      For example: Suppose that we are interested in following facts:
      John spotted Alex.

      Now, this could be represented as “Spotted (agent(John), object (Alex))”

      Such a representation can make it easy to answer questions such as: Who spotted Alex?

      Suppose we want to know : “Did John see Sue?”
      Given only one fact, user cannot discover that answer.

      Hence, the user can add other facts, such as “Spotted (x, y) → saw (x, y)”

      Representing sets of objects.
      There are some properties of objects which satisfy the condition of a set together but not as individual;

      Example: Consider the assertion made in the sentences:
      “There are more sheep than people in Australia”, and “English speakers can be found all over the world.”
      These facts can be described by including an assertion to the sets representing people, sheep, and English.

       

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