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An Introduction to Artificial Intelligence. Introduction Getting machines to “think”. Imitation game and the Turing test. Chinese room test. Key processes.

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Presentation on theme: "An Introduction to Artificial Intelligence. Introduction Getting machines to “think”. Imitation game and the Turing test. Chinese room test. Key processes."— Presentation transcript:

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.


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