Mona Institute of Applied Sciences
MACS6319 - Artificial Intellignce
Summer 2010
Description
Decisions, decisions, decisions.
Solving a problem requires making decisions, and making the right decisions.
The ability to make the right decisions, and hence solve a problem, is
a fundamental measure of intelligence.
Lots of wrong decisions are made all the time - just watch the world go by -
intelligence doesn't come easy.
Artificial Intelligence is the study of computer agents that make the right
decisions (with more than random chance), and hence exhibit intelligence.
There are five cornerstones to the construction of artificially intelligent
agents:
- Powerful input processing, to obtain an
adequate description of the problem to be solved, and the environment
within which the problem exists.
Examples are natural language processing, image recognition, and tactile
sensor processing.
- Problem representation, to maintain an
adequate representation of the problem, as the decisions made transform
it from the initial description to a final solution.
Examples are logics, semantic nets, and Bayesian networks.
- Search strategies, to investigate alternative
decisions and to evaluate the quality of each.
Examples are A* search, iterative deepening, and intersection search.
- Contextual knowledge, to provide domain specific
information that can be used to guide the search for a solution.
The same data structures used for problem representation are
useful here.
- Powerful output processing, to present the solution.
Examples are robotic arms, speech synthesizers, and digital interfaces.
If all the above sounds kinda different to other things you've learned
in Computer Science, you're right!
Many traditional Computer Science techniques (algorithms, data structures,
etc.) are invented and refined by intelligent computer scientists, but their
execution on a computer exhibits no intelligence at all.
Artificial Intelligence aims to build computer agents that exhibit
intelligence themselves.
This course teaches advanced concepts in Artificial Intelligence.
It also introduces the basic AI search techniques, knowledge representation,
and some applications of AI.
Some of the topics covered are:
- History and foundations
- Classical logic and reasoning
- Prolog programming
- Features of knowledge
- Reasoning with uncertainty and imprecision
- State space search
- Knowledge representation
- Machine learning
Learning Objectives
- Give an introduction to AI, e.g., like intelligent agents, agent
environment etc.
- Understand the basic AI searching techniques
- Understand the principles of knowledge representation
- Understand the learning principles of AI, neural networks
- Program in a AI programming language (Prolog this time round)
- Appreciate some applications of AI
Instructor
Dr Geoff Sutcliffe.
Contact details are on the WWW at
http://www.cs.miami.edu/~geoff.
There will be office hours at a time we agree on, and
students are encouraged to ask questions by email at all times.
Contact Hours
Each week there are three 3 hours lectures:
- Monday, Tuesday, Thursday 6:00-9:00pm, in Chemistry Lecture Theatre 3
Everyone must email me ASAP so I can get all your email addresses -
please make the email subject "CS63S", and put your name in the email.
I will use those email addresses to send out course announcements.
Students are also required to consult the subject WWW page regularly.
Resource materials
There is no required text - all will be revealed in the classes.
The recommended text, which covers most of the material of this course, is:
A list of
reference texts,
lecture slides,
and
assignments will be available
on the WWW.
Assessment
| 30%
| Assignments
|
| 10%
| Test
|
| 60%
| Final Exam
|
In order to obtain a particular grade, you may be required to attain
that grade in all items of assessment.
Assignments will be placed on the WWW.
The submission requirements for each assignment are given with each
assignment.
Late submissions will not be accepted.
Extensions of the due date will be granted if supporting documentary
evidence is supplied (e.g., a doctor's certificate).
Application for an extension must be made to the instructor before
the due date (if possible).
Assessment items must be completed individually.
While general interaction between students is encouraged, plagiarism
is a breach of the Honor code. It is ok to talk to other
students about general solution techniques for assignments,
but it is not ok to copy solutions in part or as a whole.
Plagiarism will result in a loss of marks for all guilty students
involved.