ARTIFICIAL INTELLIGENCE TECHNIQUES IN PROGRAMMING AND NATURAL LANGUAGES

MODULE 3 TOPIC 1

Knowledge representation is the process of representing knowledge in a way that can be understood and processed by a computer system. It involves creating a structured model of knowledge that can be used to reason about the world and make decisions. The goal of knowledge representation is to enable a computer system to understand and reason about the world in the same way that humans do. This involves creating a formal language for representing knowledge, as well as developing algorithms and techniques for manipulating and reasoning with that knowledge. Knowledge representation is a key component of artificial intelligence and is used in a wide range of applications, including natural language processing, expert systems, and robotics.

The history of knowledge representation and reasoning can be traced back to ancient Greece, where the development of logic began. However, the field of artificial intelligence (AI) dedicated to representing information about the world, known as knowledge representation and reasoning (KRR), emerged in the 1970s. In 1971, the heuristic dendral program was developed for explaining empirical data, and in 1978, the politics automated ideological reasoning program was created. Since then, KRR has become a core component of AI and has been used in a wide range of applications, including natural language processing, expert systems, and robotics. Today, KRR continues to evolve and advance, with ongoing research and development in the field.

Domain modeling is the field in which the application of KR to specific domains is studied and performed.

The amorphous shape labelled Domain Knowledge refers to the knowledge possessed by the domain expert that must be encoded in some fashion. This knowledge is not well defined and is fairly difficult for others to access. The box labelled Meta-Model refers to the KR formalism, typically a KR language that will be used as the symbol level [Newell, 1982] for the machine representation of this knowledge. The box labelled instantiation refers to the process of taking the domain knowledge and physically representing it using the meta-model, this process is sometimes referred to as knowledge acquisition [Schoen, 1991]. The box labelled domain model refers to the knowledge-base that results from the instantiation, and the operational goals are typically not represented formally, but refer to the reason the domain model was built and what it will be used for.

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