CSci 4511w: Class Notes Spring 2020
Intro to AI
Material will be added to this page during the semester, as a
topic is being covered.
General resources on AI and Computer Science
AI Topics library from AAAI offers a great collection of pages with
information about AI and various subjects within AI.
This is an excellent place to visit for people who
want to get a good start at any of the subfields within AI.
ACM Digital Library has a huge searchable collection of papers.
If the link does not work, you can get to it from the UofM library page
ACM guide to computing literature
IEEE has also a large collection of papers in
To read the papers you have to access them from an on-campus
computer or authenticate through the UofM Library
Indexes and Database page.
Look under A for ACM Digital Library
and under I for IEEE Explore.
- Google Scholar is another
good source for papers.
Applets and demo programs
Intro to AI
- The term Artificial Intelligence was first used in 1955
by John McCarthy in the
Proposal for the Dartmouth Summer Research Project on Artificial
Intelligence to the Rockfeller Foundation, by
John McCarthy, Marvin Minsky, Nathaniel Rochester, and Claude Shannon.
The proposal starts like this:
"We propose that a two-month, ten man study of artificial intelligence be
carried out during the summer of 1956 at Dartmouth College in Hanover, New
Hampshire. The study is to proceed on the basis of 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
An attempt will be made to find how to make machines use language, form
abstractions and concepts, solve kinds of problems now reserved for
humans, and improve themselves. We think that a significant advance can
be made in one or more of these problems if a carefully selected group of
scientists work on it together for a summer.
[Additional attendees were Trenchard More, Arthur Samuel, Oliver Selfridge,
Ray Solomonoff, Alen Newell, and Herbert Simon.]
The original paper by A. M. Turing,
Computing machinery and intelligence.
Mind, 59, pp 433-460, 1950. This is the paper where he described
the Turing test and more.
For a short introduction to different meanings of the term artificial
read the first pages of
Natural and Artificial Intelligence by Robert Sokolowski.
Look at What is
Artificial Intelligence?, a paper from John McCarthy.
For another view, look at
What is Artificial Intelligence?, an on-line philosophical
introduction to AI by Jack Copeland. The article includes some history
of AI, the Turing test, and more. Easy to read.
Artificial Intelligence is an attempt to prove the Physical-Symbol
System Hypothesis (PSSH).
From Allen Newell and Herbert Simon, Turing Award winners,
as Empirical Inquiry: Symbols and Search,"
Communications of the ACM, March 1976, pp 113-126.
"A physical symbol system consists of a set of entities, called symbols,
which are physical patterns that can occur as components of another type
of entity called an expression (or symbol structure). Thus, a symbol
structure is composed of a number of instances (or tokens) of symbols
related in some physical way (such as one token being next to another).
At any instant of time the system will contain a collection of these
symbol structures. Besides these structures, the system also contains a
collection of processes that operate on expressions to produce other
expressions: processes of creation, modification, reproduction and
In the paper they state the Physical Symbol System Hypothesis:
"A physical symbol system has the necessary and sufficient means for
general intelligent action.
By necessary we mean that any system that exhibits intelligence will
prove upon analysis to be a physical symbol system.
By sufficient we mean that any physical symbol system of sufficient
size can be organized further to exhibit general intelligence.
By general intelligent action we wish to indicate the same scope of
intelligence as we see in human action."
The Knowledge Navigator
Video, a video made by Apple in 1987 to show their vision for the future of
computer use. The video shows a simulated personal agent which interacts with
the user by video chat, with linked databases and integrated multimedia and
hypertext. The date on the calendar of the user is September 16, 2011. Apple
announced SIRI on October 4, 2011.
- Notes on search algorithms. The
description of the algorithms in these notes is taken from J. Pearl,
"Heuristics", Addison-Wesley, 1984.
Genetic algorithms: principles of natural selection applied to computation
by Stephanie Forrest, Science, Vol 261, No. 5123, August 1993, pp 872--878,
for an introduction to genetic algorithms and to the random key encoding
applied to the Traveling Salesman Problem.
- For a nice overview of algorithms for the Traveling Salesman Problem
(TSP) look at
- Minimax and alpha-beta pruning
- Monte Carlo Tree Search (MCTS) methods
- Look at an extensive survey of MCTS in games
Browne, Powley, Whitehouse, Lucas, Cowling, Rohlfshagen, Tavener, Perez, Samothrakis, and Colton
A Survey of Monte Carlo Tree Search Methods
IEEE Trans on Computational Intelligence and AI in Games,
VoL. 4, No. 1, March 2012.
AlphaGo Zero is the program that learned to play Go from
scratch. It is described in
Mastering the game of Go without human knowledge
by David Silver et al, Nature 550, pages 354-359
(19 October 2017).
short story on Libratus, the program from CMU that defeated
professional poker players. A
longer paper describes some of the methods used.
- Constraint propagation
- Do you want to know how many colors you need to color a planar map
so that no two adjacent regions (that share more than a common point)
have the same color?
The problem is called the map coloring problem. It is solved by the
four color theorem.
- An example from Peter Norvig of how to use constraints to solve Sudoku Puzzles using Python.
- Labelling edges in drawings of blocks
is another example of propagation of constraints, where the constraints
are the physically realizable labels for the lines in the drawings.
A description of how to use the labels from the catalogue to label a drawing
is in Winston's AI textbook at
- A short paper comparing
Methods for Constraint Propagation.
- Practical applications of search algorithms
- A complex search problem: searching for airfares. ITAsoftware
was the leader in producing software used to search for airfares,
schedule, and availability of flights. To get a glimpse at the
complexity, think that there are 10,000 paths from San Franscisco to
Boston, and for each path about 10,000 ways of pricing the itinerary.
For a general description look at
Carl de Marcken: Inside Orbitz, a short story of how Lisp and other
decisions helped solving the problem and obtain a great commercial
- Notes on the STRIPS language, assumptions
- Try the applet at
It lets you use the STRIPS representation for actions and states,
and runs a planning algorithm to produce a plan. It works only in the
blocks world, but has a nice graphical interface that let's you follow
what happens step-by-step while constructing the plan and let's you
execute the plan step by step. Very cute!
- Look at Shakey. This is the
earliest robotics project at SRI where planning and STRIPS were used.
The site has a video and links to all the historical papers.
- A short description of Graphplan mutexes and an example.
- The original paper on Graphplan by Avrim Blum and Merrick Furst
"Fast planning through planning graph analysis,"
Artificial Intelligence, vol 90, pp 281-300, 1997
is available from
http://www.cs.cmu.edu/~avrim/graphplan.html, the Graphplan home page.
- The earlier paper by D. Weld,
"An introduction to least commitment planning",
AI Magazine, Winter 1994, pp 27-61
is a well written tutorial on least commitment
planning algorithms, which includes detailed examples.
- To learn about and to use a planner based on SAT, look at
This planner combines Graphplan with Satplan. On the page you'll
find links to papers such as
Henry Kautz, David McAllester, and Bart Selman,
"Encoding Plans in Propositional Logic", Proc. KR-96.
Neural Networks and Deep Learning is a free online book that
covers in a gentle but precise way the theory and practice of neural
networks and gives a limited introduction to deep learning. Good to
build up full understanding of what makes neural nets to work.
chapter, and the
ANSI Common Lisp by Paul Graham are available on the Web.
Copyright: © 2010-2020 by the Regents of the University
Department of Computer Science and
Engineering. All rights reserved.
Comments to: Maria Gini
Changes and corrections are in red.