AI lijiaqi

Artificial Intelligence : 

Artificial Intelligence

What is AI? : 

What is AI? Various definitions: Building intelligent entities. Getting computers to do tasks which require human intelligence. But what is “intelligence”? Simple things turn out to be the hardest to automate: Recognising a face. Navigating a busy street. Understanding what someone says. All tasks require reasoning on knowledge.

Why do AI? : 

Why do AI? Two main goals of AI: To understand human intelligence better. We test theories of human intelligence by writing programs which emulate it. To create useful “smart” programs able to do tasks that would normally require a human expert.

Who does AI? : 

Who does AI? Many disciplines contribute to goal of creating/modelling intelligent entities: Computer Science Psychology (human reasoning) Philosophy (nature of belief, rationality, etc) Linguistics (structure and meaning of language) Human Biology (how brain works) Subject draws on ideas from each discipline.

Typical AI Problems : 

Typical AI Problems Intelligent entities (or “agents”) need to be able to do both “mundane” and “expert” tasks: Mundane tasks - consider going shopping: Planning a route, and sequence of shops to visit! Recognising (through vision) buses, people. Communicating (through natural language). Navigating round obstacles on the street, and manipulating objects for purchase. Expert tasks are things like: medical diagnosis. equipment repair. Often “mundane” tasks are the hardest.

About this Module : 

About this Module Natural Language: How can a system communicate in a natural language such as English. Machine learning and neural networks: How can a system learn from experience, or from past case data. Agents: How can we develop and use practical “intelligent agents”. Knowledge Engineering: How do we elicit the human expertise required to build intelligent applications.

Labs and Coursework : 

Labs and Coursework Weekly lab, starting Wed 16th April! Labs give you experience of two AI programming languages: Prolog and NetLogo. Weeks 1-4: Exercises on AI Programming in Prolog. Some of these must be “ticked off” by Lab demonstrators and will contribute to your coursework mark. Weeks 5-8: NetLogo with assessed exercise.

Books etc. : 

Books etc. “Essence of Artificial Intelligence” by Alison Cawsey, Prentice Hall. Review: “I missed most of the lectures but thanks to this short and sweet book I passed my first year introduction to AI course. If you are a slack student taking an AI course - buy this book. “ Artificial Intelligence: A Modern Approach (second edition), Russell & Norvig, Prentice Hall. 2003 Artificial Intelligence: Structures and Strategies for Complex Problem Solving, Luger, Benjamin Cummings. Slides, lab exercises etc for weeks 1-4 on www.macs.hw.ac.uk/~alison/ai3/