VTU Notes | 18CS71 | ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING

VTU Module-2 | Knowledge representation issues, Predicate logic, Representaiton knowledge using rules

Module-2

4.9
2018 Scheme | CSE Department

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5 Modules

18CS71 | ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING | Module-2 VTU Notes

Knowledge representation is a fundamental aspect of artificial intelligence that involves capturing and organizing information in a format suitable for computational processing. This process is essential for creating intelligent systems capable of reasoning, problem-solving, and decision-making. In the realm of knowledge representation, several key issues arise, shaping the way information is stored and manipulated.

One prominent method of knowledge representation is Predicate Logic, a formal system for expressing relationships and properties using predicates, variables, and quantifiers. Predicate Logic provides a structured and unambiguous way to represent knowledge by employing symbols and rules to denote the relationships between entities. It plays a crucial role in defining the semantics of statements and propositions, enabling machines to understand and infer logical conclusions.

Representation of knowledge using rules is another significant aspect of knowledge representation. Rules serve as a mechanism to encode knowledge in the form of conditional statements or guidelines that govern how information should be processed. These rules can be simple or complex, allowing systems to make informed decisions based on the conditions specified in the knowledge base.

However, knowledge representation faces various challenges, including the need for expressiveness, efficiency, and the ability to handle uncertainty. Striking a balance between a representation's simplicity and its ability to capture complex real-world scenarios is an ongoing concern in the field of AI.

In summary, knowledge representation issues highlight the complexities involved in structuring information for computational purposes. Predicate logic provides a formal and systematic approach to expressing relationships, while the use of rules offers a way to encode knowledge for decision-making. Balancing expressiveness and efficiency remains a challenge in the pursuit of creating intelligent systems capable of effectively utilizing and reasoning with knowledge.

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