overview, page 1 csi 4106, winter 2005 csi 4106 introduction to artificial intelligence winter 2005

Overview, page 1CSI 4106, Winter 2005

CSI 4106Introduction to

Artificial IntelligenceWinter 2005


Some Information (1)

• Instructor: Dr. Nathalie Japkowicz

• Office: STE 5-029

• Phone Number: 562-5800 x 6693 (don’t rely on it!)

• E-mail: [email protected] (best way to contact me!)

• Office Hours: Monday, Wednesday 1:00pm-2:00pm

or by appointment


Some Information (2)

• Textbook: Luger, George, F.: Artificial Intelligence, Structures and Strategies for Complex Problem Solving , Addison Wesley, Fifth Edition, 2005.

• Course Requirements: 3 Assignments…………………. 30%

Project Report/Presentation …..15%

Midterm Exam……………….……20%

Final Exam………………35%


Assignments

• Assignments must be handed in at the beginning of classes, the day they are due. There are no make-up assignments. The three assignments will have to be handed in on the following days. They will be posted two weeks before their due-date.

• Assignment #1 (LISP/Search) -----

Due Date: Wednesday, February 6, 2008 • Assignment #2 (PROLOG/Logic) ------

Due Date: Wednesday, March 5, 2008 • Assignment #3 (WEKA/Learning) ------

Due Date: Wednesday, April 2, 2008


Project

• Computer Games (A very popular topic, in general !)

• Expert Systems • Robotics • Planning

• Natural Language Processing• Machine Learning/Data Mining • Neural Networks• Genetic Algorithms • AI and Psychology

Students, in teams of two, will do a project on the practical applications of Artificial Intelligence. This will involve carrying out research on the topic of the team’s choice, submitting a report on this research, and giving an in-class presentation of 15 or so minutes, during which both team members will have to speak. You can choose a topic from one of the following areas of application:


TopicsOverviewKnowledge and SearchSearch

Basic Search Methods

Heuristic Search

GamesKnowledge Representation

Logic

Rules

UncertaintyNatural Language Processing

Basic Facts about English

Syntax

SemanticsPlanningMachine Learning


Definitions, overview, historyPoints

Definitions of AI:systems that

think like humans

act like humans

think rationally

act rationally

Physical-symbol systems

Sources and areas of AI

Bits of history


Definitions of Artificial Intelligence

A general classification of AI systems, due to Russell and Norvig (1995, 2003):

systems thatthink like humans

systems thatthink rationally

systems thatact like humans

systems thatact rationally


The Turing test

Assessing intelligence by observation is biased: the experimenter is guided by guesses rather than measurable properties. This is a blind test.


Systems that think like humans

AI systems of this type would try to recreate the human mind and its innate (precoded?) cognition mechanisms.

This is very difficult, because it requires a thorough understanding of psychology, neurophysiology, and philosophy.

Such systems would belong to Cognitive Science rather than Artificial Intelligence.


Systems that act like humans

E. Rich & K. Knight (1991)

AI is the study of how to make computers do things which, at the moment, people do better:

• perception,• communication,• reasoning,• learning,• planning.


... act like humans (2)

We do not even consider social behaviour, sense of humour, appreciation of arts and other talents that so far only Science Fiction gives to machines.

Even an approximation of these faculties requires vast amounts of knowledge (to represent explicitly cultural background, common sense and so on).

People also rely on experience -- perhaps on memory patterns that we do not yet know how to recreate in computer systems.



Things at which computers will soon be appreciably better: advice, diagnosis, fault detection, forecasting...

Those would be systems where specific technical knowledge plays a central role.

Measurable success will come when we solve the problems of organizing and acquiring vast knowledge.

We also need experience, rules-of-thumb, and the ability to reason in the absence of full information.



Things at which computers are already better, or nearly so:

formalized games such as chess, chequers, backgammon, Othello;

formal inference (but not creativity and invention).

They do require good heuristics -- shortcuts -- of the kind that skilled people apply, sometimes even without conscious reflection.



Neat: it is easy to verify the success of all these tasks (after all, we are better). The tasks are challenging, and can hardly be solved by algorithmic means.

Ugly: the amount of necessary knowledge is overwhelming; too many tasks end up solved in a toy form. Heuristics are fallible, and AI systems are not trusted as they perhaps deserve to be.


Systems that think rationally

E. Charniak & D. McDermott (1985)

AI is the study of mental faculties through the use of computational models.Mental faculties (reasoning, learning, perception) are studied more or less as in psychology, except that working with programs is easier and more objective, more measurable.

On the other hand, programs require full and explicitly stated knowledge.


... think rationally (2)

Does "computational" imply computing? Do brains work like computers? No, but:

what brain does may be thought of as a kind of computation.

INPUT

Vision

Language

INTERNALS

Deduction and search

Planning

Explanation

Learning

OUTPUT

Robotics

Speech



P. H. Winston (1992)

AI is the study of the computations that make it possible to perceive, reason, and act.

These are the hallmarks of intelligence, and they can be measured more or less objectively.

Now, if we could agree that this is what intelligence is about...



AI can be indirectly characterized by (some of) its goals:

•make computers more useful,

•understand the principles that make intelligence possible.

The contribution of AI methods and techniques to the classical study of intelligence:

•computational metaphors for mental processes,

•precision of the data and structures (that is, knowledge),

•establishing practical limits for "intelligent" programs,

•repeatability of experiments -- and no ethical problems.


Systems that act rationally

G. F. Luger & W. F. Stubblefield (1993),G. F. Luger (2005)

AI is the branch of computer science concerned with the automation of intelligent behaviour.

This means seeing AI as part of computer science that grows out of the same basic principles.


...act rationally (2)

Once more, we ask what is intelligence (if it can be defined, so can AI):

is intelligence innate or acquired?

what is the essence of learning, creativity, intuition?

can we observe intelligence without knowing the internal mechanisms (memory, search)?

can psychology, neurology and other related fields help build AI systems? is it possible to have intelligence without a host (body)?

These questions show how much is yet unknown. Practical AI (building systems in the absence of a philosophical foundation) is more like a blind search for answers.


Physical-symbol systems

A physical-symbol system is collection ofexpressions built of elementary symbols (without inherent meaning), andprocesses that create and modify such expressions

that exist in the context of the "real world". Symbols can be mapped into real-world entities, and processes into real-world events.A physical-symbol system is what we may call a model of the real world.


Physical-symbol systems (2)

The physical-symbol system hypothesis:we can model intelligence.

M. Ginsberg (1993)AI is the enterprise of constructing a physical-symbol system that can reliably pass the Turing test.

G. F. Luger (2005) [revised definition]

AI is the study of the mechanisms underlying intelligent behaviour through the construction and evaluation of artifacts designed to enact those mechanisms.


The sources of Artificial Intelligence

• Philosophy (ontology, epistemology, ...)

• Mathematics (logic, geometry, probability, decision theory, ...)

• Psychology

• Linguistics, psycholinguistics

• Computing (theory; engineering practice)


The areas of Artificial Intelligence

• Search (blind, informed, adversarial)• Knowledge representation (logic, semantic

networks, frames, rules, neural networks)• Planning• Machine Learning (symbolic, statistical;

data mining)• Natural Language Processing (symbolic,

statistical; text mining)• Perception (vision, speech)• Robotics


Elements of the history of Artificial Intelligence

The forerunners of AI:

information theory,

cybernetics (the study of communication and control processes in biological, mechanical, and electronic systems; comparison of these processes in biological and artificial systems).

Simple neural network computers (!) were also built in 1940s and early 1950s.


... history of AI (2)

The first, very ambitious, tasks that computing science set itself included Machine Translation and Chess Playing (Shannon 1950). Artificial Intelligence was not in the cards yet...

These have not been too successful: machine translation is still more craft than science, and computer chess has only recently become truly competitive, thanks to specialized or superfast hardware.



The term "Artificial Intelligence" has been coined in mid-1950s by John McCarthy (later the inventor of Lisp).

The first period of growth -- and funding -- came in the 1960s. General Problem Solver (Newell & Simon 1972): Aristotelian (!) means-ends analysis.

Other early applications: analogy discovery; simple question-answering systems in toy domains.



There followed a disillusionment and the withdrawal of funds.

Renewed interest in the late 1970s brought large funding (particularly from the military). In this period: more and more subtle knowledge representation methods, first of all standard logic and various advanced logics.

AI is sometimes seen as "applied logic" (Nilsson, early 1970s).



Programming languages best suited to AI tasks are Lisp (1960) and Prolog (1972). There also have been specialized knowledge representation systems and languages, used to develop knowledge bases and knowledge-based systems. This includes expert systems, in which probability and beliefs play an important role. Commercialization of some expert systems is one the signs of the growing maturity of AI.



First textbooks appeared late (1971, then 1984). No theory of AI exists in spite of the massive publication rate and the bandwagon effect (Genesereth & Nilsson 1987 is a rare textbook devoted to the foundations of AI).

Fads and trends: expert systems, genetic algorithms, neural networks, data mining. Successes have been rare and sometimes bizarre: are intelligent warheads a success?


That’s it.

We will now turn to methods, tools and techniques (but we will occasionally look at a bit of theory).

overview, page 1 csi 4106, winter 2005 csi 4106 introduction to artificial intelligence winter 2005

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