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2101INT – Principles of Intelligence Systems
Lecture 1
About Principles of Intelligent Systems
This course introduces the fundamental practice and underlying principles involved in the study of intelligent systems.
The emphasis of the course is on a practical approach to problem solving and will involve AI techniques.
Staff
Convenor: Dr Michael Blumenstein– Email: [email protected]– Room: G23_2.15– Phone: x28271
Lecturer/Tutor: Stuart Bain– Email: [email protected]– Room: G23_2.31– Phone: x28728
Class Hours
Lectures – 2 hours per week– Weeks 1 – 13– 10AM-12PM Tuesdays, G02_1.26F
Tutorials – 2 hours per week– Weeks 2 – 13– 12PM-2PM Tuesdays, G09_1.33
Course Materials
Website with course materials – news, lectures, tutorials, assignments:
http://stuart.multics.org/principles
Textbook
Russell and Norvig (2002) “Artificial Intelligence: A Modern Approach”, Prentice Hall
On campus: $???.?? Co-op Bookshop: $110 Amazon: ~$110
Topic Outline
Introduction to Intelligent Systems
1. Course Introduction and History of Artificial Intelligence
2. More History of Artificial Intelligence and Philosophical Foundations
Search
3. Informed and Uninformed Search
4. Constraint Satisfaction
5. Local Search
6. Complete Search
Knowledge Representation and Reasoning
7. Planning
8. Knowledge Representation 1
9. Knowledge Representation 2
Topic Outline cont.
Biologically Inspired AI
10. Neural Networks
11. Evolutionary Computing
Natural Language Processing
12. Natural Language Processing
Conclusion
13. Future of AI and Revision
Assessment
Individual Assignment – 15% – Due Week 7– Will be given in Week 4
Individual/Pair Assignment – 35% – Due Week 12– Will be given in Week 6
Final Exam – 50% – In Exam Week
Why Study AI?
AI makes computers more useful Studying AI leads to insights about human intelligence Increases in computing power allow more difficult
problems to be addressed AI is a bridge between computer science and other
disciplines
AI is fun!– Seriously, AI was cited as “the field I would most like to be in”
by scientists in other fields
Applications of AI
Handwriting recognition for tablet PCs Computer games GPS route planning for cars Speech recognition – Telstra Natural Language Processing/Translation Computer Vision
Links between AI and other disciplines
Philosophy– Logic, methods of reasoning, mind as a physical system,
foundations of learning, language and rationality
Mathematics– Formal representation and proof, algorithms, computation,
un/decidability, in/tractability
Psychology– Adaptation, perception and motor control
Economics– Formal theory of rational decisions
Linguistics– Formal grammars, language processing and translation
5 pp
AI in the News – July 2005
21st – Artificial Brains to be used in robots for Mars 19th – Computer bots play statistically perfect poker 18th – Electronic Brain Helps Cut Credit Card Fraud 13th – Soccer robots given even odds to beat human
team by 2050 9th – Webcrawler can complete crossword puzzles 8th – Robots paint and do oragami in Venice 2nd – Robot cleans pool without poles or hoses
27
What is AI?
– “The conjecture that every aspect of learning or any other feature of intelligence can in principle be so precisely described that a machine can be made to simulate it”
McCarthy et al. (1955) “A Proposal for the Dartmouth Summer Research Project on Artificial Intelligence”
– “AI is attempting to build artificial systems that will perform better on tasks that humans currently do better”
Wray et al. (1994) “A Survey of Cognitive and Agent Architectures”
1
Different Types of AI
Systems that think like humans“machines with minds, in the full and literal sense” [Haugeland, 1985]
Systems that think rationally“studying computations making it possible to perceive, reason and act”
[Winston, 1992]
Systems that act like humansThe Turing test – “The Imitation Game” [Turing, 1950]
Systems that act rationally“AI is concerned with the automation of intelligent behaviour” [Luger and
Stubblefield, 1993]
2
History of AI - Ancient
Galatea – 2000BC– In Greek mythology, an ivory statue sculpted by Man but later
brought to life by the goddess Aphrodite.
Barbiton – 13th C.– The unofficial version: a speaking, thinking automaton,
complete with a soul, created by Albertus Magnus with the help of “angels of the underworld”.
– Officially, Barbiton was a mechanical doll and little more than a complex musical instrument.
The Golem – 16th C.– A statue sculpted of clay that could be ordered to work through
religious incantations.
What do all of these have in common?
History of AI – Renaissance
Descartes proposed that the bodies of animals are nothing more than complex machines. He also proposed the mind-matter dualism, which fundamentally disallows machine consciousness.
Pascal invents the digital adding machine (1642). Leibniz extends it to perform multiplication and division,
and envisages a universal calculus of reasoning by which arguments could be decided mechanically.
Frankenstein’s Monster – Dr Frankenstein created a creature from body parts and brought it to life. Unlike previous automatons, science has replaced faith.
6
History of AI – Early 20th Century
Russell and Whitehead publish Principia Mathematica, revolutionising formal logic.
Karel Capek’s play R.U.R., “Rossum’s Universal Robots” is performed in London. (1923)
Rosenblueth, Wiener & Bigelow coin the term cybernetics in their 1943 paper.
Alan Turing publishes “Computing Machinery and Intelligence” in 1950. This marks the introduction of the Turing test of intelligence.
Claude Shannon details how chess is a problem of search (1950)
Asimov publishes his three laws of robotics (1950)
14pp
1956 – The Birth of AI
The term “Artificial Intelligence” was coined for a summer workshop at Dartmouth University
Organisers were John McCarthy, Marvin Minsky, Claude Shannon and Nathaniel Rochester
Other attendees included Arthur Samuel (IBM) and Newell and Simon (CMU)
17
The 60’s and 70’s
Pioneering work on planning and natural language processing centred around the “blocks-world” scenario
There were significant advances in neural networks, with Rosenblatt’s work on perceptrons.
Unfortunately, many attempts to extend the systems discussed to larger real world problems were doomed to failure.
21
Expert Systems
To overcome the difficulties of complex domains research returned to specific, restricted domains
DENDRAL: could infer the molecular structure of organic molecules
MYCIN: able to diagnose blood diseases. Significant in part because of its clean separation of the rules of inference from the knowledge (in this case, of blood diseases)
There were many commercial successful expert systems
22pp
And more recently…
Further work on ANNs provided support for “connectionist” models of intelligence
Other biologically inspired techniques such as Genetic Algorithms and Genetic Programming solve problems or learn by mimicking biological evolution
Significant work in AI now focuses on reasoning with uncertainty
Agents have become the latest “buzzword” of AI
25pp
Readings for Week 1
1. Russell and Norvig, Chapter 1
25pp
