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An Introduction to Artificial Intelligence 1 1 2 8 4 7 6 ... 3 4 5 6 1 2 7 4 5 6 2 6 State ... 3 1 2 3 8 6 4 7 ... 8 7 5 3 7 8 ... 1 ... 2 8 ... 3 4 5 7 ... ... 3 1 3 2 4 8 2 4 6 5 7 6 5 1 8 7 5 ... 1 2 3 8 4 6 5 ... ... 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. 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 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. . 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. . 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. . 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 . Modeling Human Performance • Systems that model some aspect of problem solving. • Examples: Intelligent tutoring systems that provide individualized instruction in a specific domain. . 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. Neural Networks • Aimed of low-level processing. • Are essentially mathematical models of the human brain. • A neuron: Dendrites Cell Body Axon . Synapse 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. . 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 . 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. . 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.