CPS 481/581: Advanced Artificial Intelligence
Spring Semester 2011 3 credits
Meets: T Th 4:30-5:45pm
203 Miriam Hall
|
Professor: Dr. Jennifer Seitzer
Office: 144 Anderson Hall Email: seitzer@udayton.edu Course Web Page:
http://homepages.udayton.edu/~jseitzer1/cps480 Phone: (937)
229-2197 *Office Hours:
|
Mailing Address: Dr. Jennifer Seitzer, Associate Professor Computer Science Department University of Dayton 300 College Park Dayton, OH 45469-2160 |
Description and Motivation
Artificial Intelligence
is the sub-discipline of computer science that attempts to endow intelligence
to computer systems. Although AI has
produced some very sophisticated computer systems, it is generally accepted
that the goal of systems achieving human-level intelligence is still elusive.
There are two general approaches to the pursuit of artificial
intelligence. The symbolic is
often referred to as "good old fashioned AI" (philosopher John Haugeland) and is abbreviated as: GOFAI.
This is based on the belief that thinking can be accomplished by a
physical symbol system -- that the manipulation of a physical symbol system provides
necessary and sufficient means for intelligent behavior [Newell and Simon
1976]. The subsymbolic
approach to AI employs techniques that mimic organisms in nature including
the human neuron as well collective and individual insect behavior. In fact, part of this camp of researchers believe that thinking can
only occur in machines made of proteins [Searle 1980, 1992].
In this course, we will
address some traditional areas of AI including default reasoning, fuzzy logic,
planning, and machine learning. We will
also broach a few topics spawned from the subsymbolic
approach including neural networks and perception.
Course
Objectives
·
To understand
some non-classical logics including nonmonotonic
reasoning, fuzzy logic, and probabilistic reasoning with uncertainty
·
To program an
expert system using a preexistent expert system shell and your own unique
knowledge rule base.
·
To grasp the
dominant areas of machine learning including version space, inductive logic
programming, genetic algorithms, clustering, and neural networks
·
To study some of
the fundamentals of natural language processing
·
To understand the
earliest algorithm of Swarm Programming:
shortest path mimicking ants
·
To program the
techniques and algorithms studied in class into working systems
Motivation:
Artificial Intelligence is the sub-discipline of computer science that attempts to endow intelligence to computer systems. Although AI has produced some very sophisticated computer systems, it is generally accepted that the goal of systems achieving human-level intelligence is still elusive.
There are two general approaches to the pursuit of artificial intelligence. The symbolic is often referred to as "good old fashioned AI" (philosopher John Haugeland) and is abbreviated as: GOFAI. This is based on the belief that thinking can be accomplished by a physical symbol system -- that the manipulation of a physical symbol system provides necessary and sufficient means for intelligent behavior [Newell and Simon 1976]. The subsymbolic approach to AI employs techniques that mimic organisms in nature including the human neuron as well collective and individual insect behavior. In fact, part of this camp of researchers believe that thinking can only occur in machines made of proteins [Searle 1980, 1992].
In this course, we will address some traditional areas of AI including default reasoning, fuzzy logic, planning, and machine learning. We will also broach a few topics spawned from the sub-symbolic approach including neural networks and perception.
Objectives:
· To understand some non-classical logics including nonmonotonic reasoning, fuzzy logic, and probabilistic reasoning with uncertainty
· To program an expert system using a preexistent expert system shell and your own unique knowledge rule base.
· To grasp the dominant areas of machine learning including version space, inductive logic programming, genetic algorithms, clustering, and neural networks
· To study some of the fundamentals of natural language processing
· To understand the earliest algorithm of Swarm Programming: shortest path mimicking ants
·
To program the techniques and algorithms studied in
class into working systems
Subject Matter (Tentative list
and schedule of coverage):
|
Week |
Date |
Topics |
Current Program |
|
1 |
1- Tues, 1/18/11 |
Course Introduction; Philosophical Foundations
of AI; Logic
Revisited; PROLOG
Revisited |
PROLOG
Basics |
|
2 -Thur, 1/20/11 |
Advanced PROLOG; The Logic of
Paradoxes; Prolog programming of
paradoxes |
(Individual) |
|
|
2 |
3- Tues, 1/25/11 |
More PROLOG; Nonmonotonic Logics; Review of JTMS / Stable Models |
PROLOG
Paradoxes |
|
4- Thur, 1/27/11 |
Ideas on Implementation of
JTMS; Demonstration of How
to Present a Paper: Paper on Nonmonotonic
Reasoning and Stable Models |
(Group) |
|
|
3 |
5- Tues, 2/1/11 Quiz |
Quiz Paper on PROLOG Fuzzy Sets and Fuzzy
Logics; |
Implementation of JTMS |
|
6- Thur, 2/3/11 |
Fuzzy Logic Systems; Expert Systems; Systems of Uncertainty |
(Group – Multiple Parts
and Due Dates) |
|
|
4 |
** Mon 2/7/11 |
Last day to
withdraw without record |
|
|
7- Tues, 2/8/11 |
Paper on Fuzzy Logic Systems Introduction to Planning |
|
|
|
8- Thur, 2/10/11 |
Paper on Systems of Uncertainty More Planning |
|
|
|
5 |
9 - Tues, 2/15/11 |
Paper on Planning – 1; Knowledge Discovery and
Data Mining |
|
|
10 - Thur, 2/17/11 |
Paper on Planning – 2; Machine Learning –
Inductive Logic Programming |
|
|
|
6 |
11- Tues, 2/22/11 |
Paper on Data Mining Machine Learning – Decision
Tree Learning |
|
|
12- Thur, 2/24/11 |
Review for Midterm Paper on Inductive Logic Programming Machine Learning –
Association Rule Mining |
|
|
|
7 |
13- Tues, 3/1/11 |
Midterm 1 |
Decision Tree Learner |
|
14- Thur, 3/3/11 No Class |
Midterm
Break – No Class |
(Group – Multiple Parts
and Due Dates) |
|
|
8 |
15- Tues, 3/8/11 |
Paper on Decision Tree Mining Paper on Association Rule Mining Introduction to
Evolutionary Computation; Genetic
Algorithms |
|
|
16- Thur,
3/10/11 |
Genetic Programming; Grammatical Evolution |
|
|
|
9 |
18- Tues, 3/15/11 |
Paper on Genetic Algorithms – 1 Paper on Genetic Algorithms - 2 Communication: The
Underpinnings of Natural Language Understanding (NLU)- |
|
|
19- Thur,
3/17/11 |
Paper on Genetic Programming Grammars in Natural Language Understanding (NLU)
---Chap 22 |
|
|
|
10 |
20- Tues, 3/22/11 |
Paper on Grammatical Evolution Semantics and Pragmatics in
NLU |
NLU System |
|
21- Thur,
3/24/11 |
Thought Chunking; Guest Speaker on an NLU application for the Hearing
Impaired |
(Individual) |
|
|
11 |
22- Tues, 3/29/11 |
Paper on Natural Language Understanding-1 Introduction to IR / Web Mining |
|
|
23- Thur,
3/31/11 |
Paper on Natural Language Understanding-2 Swarm Intelligence |
Lego Robot System |
|
|
12 |
** Mon, 4/4/11 |
Last day to
withdraw with grade of ‘W’ |
|
|
24- Tues, 4/5/11 |
Paper on Web Mining Vision and Perception |
(Group – Multiple Parts
and Due Dates) |
|
|
25- Thur,
4/7/11 |
Paper on Swarm Intelligence Introduction to Robots |
|
|
|
13 |
26- Tues, 4/12/11 |
Paper on Vision and Perception Paper on Robots More Robots |
|
|
27- Thur,
4/14/11 |
Paper on Web bots Additional Paper-1; Review for Midterm 2 |
|
|
|
14 |
28 - Tues, 4/19/11 Midterm 2 |
Test on material covered
since last exam |
|
|
29- Thur,
4/21/11 |
Easter Break – No Class |
|
|
|
15 |
30- Tues, 4/26/11 |
Additional Paper-2; Graduate Projects |
|
|
31- Thur,
4/28/11 |
Robot Talent Show! |
|
|
|
16 |
Tuesday, 5/2/11 4:30-6:20pm |
Cumulative AI Final Miriam Hall 203 |
|
|
Saturday, 5/7/11 |
Graduate
Student GRADUATION
12:45pm |
|
|
|
Sunday, 5/8/11 |
Undergraduate
Student GRADUATION
9:45am |
|
Suggested Text:
Artificial Intelligence 3rd Edition;
Elaine Rich, Kevin Knight, Shivashankar B. Nair
Suggested Additional Text:
Artificial Intelligence A Modern Approach
By, Stuart Russell and Peter Norvig
ISBN # 0-13-103805-2
Course
Facilitators