An Introduction to Artificial Intelligence

1. An Introduction to Artificial Intelligence

2. Introduction • Getting machines to “think” . • Imitation game and the Turing test. • Chinese room test. • Key processes of AI: • Search, e.g. breadth first search, depth first search, heuristic searches. • Knowledge representation, e.g. predicate logic, rule-based systems, semantic networks.

3. Areas of AI • Game playing • Theorem proving • Expert systems • Natural language processing • Modeling human performance • Planning and Robotics • Neural-networks • Evolutionary algorithms and other biologically inspired methods • Agent-based technology

4. Game Playing • Getting the computer to play certain board games that require “intelligence”, e.g. chess, checkers, 15-puzzle. • A state space of the game is developed and a search applied to the space to look ahead. • Example: Deep blue vs. Kasparov. .

5. Theory Proving • Automatic theorem proving. • Generate proofs for simple theorems. • Mathematical logic forms the basis of these systems. • The “General Problem Solver” is one of the first systems. .

6. Expert Systems • Performs the task of a human expert, e.g. a doctor, a psychologist. • Knowledge from an expert is stored in a knowledge base. • Examples: ELIZA, MYCIN, EMYCIN • Suitable for specialized fields with a clearly defined domain. .

7. Natural Language Processing • Develop systems that are able to “understand” a natural language such as English. • Voice input systems, e.g. Dragon. • Systems that “converse” in a particular language. • Examples: SHRDLU and ELIZA .

8. Modeling Human Performance • Systems that model some aspect of problem solving. • Examples: Intelligent tutoring systems that provide individualized instruction in a specific domain. .

9. Planning and Robotics • Involves designing flexible and responsive robots. • Lists of actions to be performed are generated. • Aimed at high-level tasks, e.g. moving a box across the room. • Has led to agent-oriented problem solving.

10. Neural Networks • Aimed of low-level processing. • Are essentially mathematical models of the human brain. • A neuron: .

11. Evolutionary Algorithms & Other Nature-Inspired Algorithms • Based on Darwin’s theory of evolution. • An initial population of randomly created individuals is iteratively refined until a solution is found. • Examples: genetic algorithms, genetic programming, memetic algorithms • Other methodologies: ant colonization, swarm intelligence. .

12. Uncertainty Reasoning • Uncertain terms may need to be presented. • Example: representing terms such as “big” or “small”. • Methods for this purpose: • Fuzzy logic • Bayesian reasoning and networks .

13. Agent-based Technology • Intelligent agents, also called “softbots”, are used to perform mundane tasks or solve problems. • In a multi-agent system agents communicate using an agent communication language. .

14. Artificial Intelligence Languages • Programming paradigms • Artificial intelligence languages – Prolog and Lisp • Prolog (Programming Logic) – declarative – predicate logic • Lisp (List Processing) – functional – code takes the form of recursive functions. • More recently AI systems have been developed in a number of languages including Smalltalk, C, C++ and Java.