An Introduction to AI

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Presentation Transcript

An Introduction to Artificial Intelligence.: 

<![CDATA[ An Introduction to Artificial Intelligence. What is intelligence? the ability to learn or understand or to deal with new … situations … the ability to apply knowledge … or to think abstractly … Merriam-Webster Online Dictionary. ]]>

Slide2: 

<![CDATA[ Artificial Intelligence (AI) is the study of how to make computers behave intelligently. These are some examples of tasks performed by AI systems: machine translation medical diagnosis game playing Bonjour]]>

The Turing Test: 

<![CDATA[ The Turing Test Proposed by Alan Turing in 1950. Q A Q A Q A]]>

Engineering or Science: 

<![CDATA[ Engineering or Science Engineering building systems that behave intelligently in order to solve practical problems focus on constructing a piece of software Science building systems that behave intelligently in order to better understand intelligence focus on understanding intelligence]]>

Strong A.I. and Weak A.I.: 

<![CDATA[ Strong A.I. and Weak A.I. Strong AI A computer running suitable software actually is intelligent. Weak AI Intelligence is something that only biological systems can possess. The best that can be accomplished with a machine is to simulate intelligence. Distinction first made by John Searle.]]>

Neighbouring Disciplines: 

<![CDATA[ Neighbouring Disciplines Philosophy Logic: valid reasoning How does the mind relate to the brain? Mathematics Logic: valid reasoning What can/can’t be computed? Psychology How do humans think? What are the processes that underlie intelligence? Computer engineering How can we build an efficient computer? How can we construct effective computer software?]]>

Early History of A.I.: 

<![CDATA[ Early History of A.I. 1956: AI is born at the Dartmouth workshop. 1956: Newell & Simon present Logic Theorist at Dartmouth Workshop. 1958: McCarthy defined the Lisp programming language. 1965: Robinson described resolution principal. 1972: Winograd constructed SHRDLU natural language understanding system. 1972: First implementation of Prolog.]]>

Expectation and Reality: 

<![CDATA[ Expectation and Reality Expectation computer systems with robust and flexible intelligent behaviour the ability to match, or improve on, human performance AI systems in widespread use Reality approaches that were successful in micro-worlds failed to scale up to more complex domains AI still not moved out of the laboratory]]>

Knowledge-Based Systems: 

<![CDATA[ Knowledge-Based Systems The lessons of early AI work indicated that successful systems could be built for limited domains. They need two things: a domain specific knowledge base a general purpose reasoning system The KBS approach emphasises domain specific knowledge rather than general purpose reasoning. This was found to be the best way to scale-up an AI system.]]>

This Module: 

<![CDATA[ This Module Prolog programming language a general purpose programming language a general purpose reasoning system a knowledge representation language Knowledge-based systems knowledge representation reasoning]]>

Exercises: 

<![CDATA[ Exercises What can you deduce about Tweety from the following statements? All birds can fly Tweety is a bird Explain your reasoning.]]>

Exercises: 

<![CDATA[ Exercises What can you deduce about Thelonious? All dudes are cool, Thelonious is a dude What can you deduce about Nina? All flerps can flimble, Nina is a flerp]]>

Exercises: 

<![CDATA[ Exercises On the basis of the following, we can deduce that Pingu can fly. All birds can fly, All penguins are birds Pingu is a penguin This appears to conflict with the real world. Where does the problem lie?]]>

Acknowledgements: 

<![CDATA[ Acknowledgements Russell & Norvig. Artificial Intelligence: A Modern Approach (2nd Edition). Chapter 1.]]>