Introduction to Artificial Intelligence

Abstract

The chapter introduces natural and artificial intelligence characteristics such as non-algorithmic approach, use of heuristics, self-learning, and ability to handle partial inputs along with various types of artificial intelligence and application areas. Terms like weak AI, narrow AI, classical AI, modern AI, symbolic AI, machine learning, super AI, and general AI are outlined in this chapter. As the basic source of intelligence is knowledge, the systems which deal with artificial intelligence classically need to be knowledge-based. To demonstrate how such knowledge-based systems are working, it is necessary to know about various types of knowledge, knowledge acquisition processes, and related heuristics. Once a sufficient amount of variety of knowledge is collected from multiple domain experts, it needs to be effectively represented into the system. For this, various knowledge representation structures with their possible hybridization and comparative evaluation are illustrated in this chapter. Besides the knowledge acquisition and knowledge representation, there are many more components required for a traditional knowledge-based system. These components are the inference engine, self-learning, explanation, and user interface. These are known as components of symbolic AI or classical AI. Major popular symbolic AI systems, also known as the knowledge-based system are namely: (i) Expert systems, (ii) Linked based systems, (iii) Computer Aided Systems Engineering (CASE) based system, (iv) Knowledge-based tutoring systems, (v) Agent-based system, and (vi) Intelligent interface to data. All these systems are discussed with their general architecture and basic components. Once the knowledge-based system is developed, it must undergo testing and certification. To test such systems, various types of testing mechanisms are available. The Turing test, Chinese room test, Marcus test, Lovelace test 2.0, etc. are briefly discussed in this chapter. At the end, the chapter enlists the benefits, applications, and limitations of artificial intelligence. The chapter enlists more than 50 applications and possibilities of further research in the core, applied, and hybrid areas.