CPE/CSC 480
Artificial Intelligence
Fall 2009
CPE/CSC 480-F09 Artificial Intelligence Schedule
The following table provides an outline for the course schedule.
It lists the topic for a particular week, together with references
to the respective entries in the reading list, and to the assignments
with their due dates. Material will be made available as the course proceeds,
so some links will be broken initially.
Week
Date
Topic
Keywords
Description
Readings
Guest Speaker
Topic
Assignment
Lab Activity
Project
Due
Student Presentation
Student Commentators
1
Sep 22
Introduction
An overview of the course.
Intelligence in humans and machines: criteria, differences, problems.
Intelligent Agents: autonomy, behavior, structure and types of agents.
Systematic problem solving: Strategies, search methods
Games: adversarial search, minimax and alpha-beta pruning, chance.
Knowledge representation and reasoning: representation methods, logic, inference.
Learning: inductive learning, statistical methods, neural networks, reinforcement learning.
Conclusions: applications, social and ethical issues, future of AI.
AIMA 1,
AI @ Wikipedia ,
AI @ AAAI
Assignment 1: AI Nugget Presentation - topic selection Lab 1: Chatbots
Identify potential topics; team formation; brainstorming of ideas;
previous team projects
Sep 24
Project overview
Name/Topic:
Name/Topic:
2
Sep 29
Intelligent Agents
Structure and behavior of intelligent agents:
Rationality, performance measures, omniscience;
types and properties ofenvironments;
agent programs, agent types.
AIMA 2, Agents @ AAAI
Assignment 2: AI Competitions (Robocode, Prisoner's Dilemma)
Lab 2: Simple Agents
Select topic, team mates
Assignment 1: AI Nugget Presentation -
Oct 1
AIMA
Milestone Week 2: Requirements, Testing and Evaluation Plan; teams established; project definition
topic proposal and date selection
3
Oct 6
Problem Solving and Search Well-defined problems and solutions:
Problem formulation, performance assessment, systematic search as problem solving strategy
AIMA 3, Search @ AAAI ,
Uninformed Search @ Wikipedia
Lab 3: Breadth-First Search, Depth-First Search
Requirements definition, schedule
Oct 8
Uninformed Search Strategies
Search without domain knowledge: breadth-first and depth-first strategies; improvements for these strategies; limitations of uninformed search
AIMA 3, depth-first , breadth-first @ Wikipedia
4
Oct 13
Informed Search Search with domain knowledge: heuristics, greedy best-first search, A* search
AIMA 4.1, 4.2, Search @ AAAI ,
Informed Search @ Wikipedia
Assignment 3: Search Algorithms Lab 4: AI in Entertainment (e.g. Games, Movies) Milestone Week 4: Prototype 1 (alpha)
Oct 15
Local Search and Constraint Satisfaction
Local search algorithms: Hill-climbing, simulated annealing, local beam search, genetic algorithms;
Constraint satisfaction: Propagating information through constraints; suitable search methods.
AIMA 4.3, 4.4, 5
5
Oct 20
Games Games as Adversarial Search: Two-person, zero-sum games, search strategies, minimax, alpha-beta pruning, element of chance
AIMA 6, Games @ AAAI ,
Games in AI @ Wikipedia
Lab 5: AI in Real Life
Oct 22
Instructor's Furlough Day - No Class
6
Oct 27
Reasoning Knowledge-based agent: Limitations of search, deductive, inductive, and other methods of reasoning, syntax and semantics, validity and satisfiability
AIMA 7, 8, Reasoning @ AAAI ,
Games in AI @ Wikipedia
Assignment 4: Wumpus World Lab 6: Local Search: Constraint Satisfaction, Hill-Climbing
Milestone Week 6: Prototype 2 (beta)
Oct 29
Logic
propositional logic, predicate logic, inference methods, resolution, unification, forward and backward chaining
AIMA 7, 8, Logic @ AAAI ,
Games in AI @ Wikipedia
Assignment 3: Search Algorithms
7
Nov 3
Knowledge Representation Representation of knowledge in digital systems: categories and objects, mental vs. physical entities, actions, situations, and events; semantic networks, frame-based systems; ontologies; logic and knowledge
AIMA 10, (Knowledge) Representation @ AAAI ,
Knowledge representation @ Wikipedia
Lab 7: Wumpus World Agent
Nov 5
8
Nov 10
Learning Improving agent performance through learning: Forms of learning; inductive learning, decision trees; computational learning theory;
AIMA 18, 19, (Machine) Learning @ AAAI ,
(Machine) Learning @ Wikipedia
Lab 8: Logical Wumpus World Agent Milestone Week 8: Final Version
Nov 12
Assignment 4: Wumpus World
9
Nov 17
Learning
explanation-based learning and rule extraction; statistical learning, Bayesion networks, hidden Markov models, neural networks;
reinforcement learning
AIMA 20, 21
Lab 9: Learning
Nov 19
10
Nov 24
Instructor's Furlough Day - No Class
Lab 10: Something Funny
Nov 26
Thanksgiving Break - No Class
11
Dec 1
Applications of AI and Conclusions; Team Project Presentations
Examples of the use of AI methods in various domains; ethical and social issues in AI
Ethics of AI @ AAAI ,
Applications of AI @ AAAI ,
Ethics of AI @ Wikipedia
Project Presentations Feedback and Evaluation forms
Project Presentations Project Presentations
Dec 3
Future of AI; Team Project Presentations
Recent developments and trends in AI; e.g. autonomous robots, consciousness, singularity, Science Fiction and AI
Science Fiction and AI @ AAAI ,
Future of AI @ AAAI
Project Presentations Feedback and Evaluation forms
Project Presentations
A note about the links for additional reading: The Wikipedia links I have included above under "Readings" contained reasonable and useful additional information on the respective topics when I last checked them (in Sep. 09). The contents may change, however, so you should probably not use it as your only source of information.
Some other links refer to a wiki maintained by the Association for the Advancement of Artificial Intelligence (AAAI). These articles are typically written by experts in the specific area, but may also be "under construction".