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(none)-(none) (none) 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.
1
9/20
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; Ertel 1;
AI @ AAAI
AI @ Wikipedia
AI Nugget Presentation - topic selection L1: Chatbots, Hunch
Topics, teams;
old projects
9/22
Project overview
Name/Topic:
Name/Topic:
2
9/27
Intelligent Agents
Structure and behavior of intelligent agents:
Rationality, performance measures, omniscience;
types and properties ofenvironments;
agent programs, agent types.
AIMA 2, Ertel 1.3,Agents @ AAAI
A3: AI Competitions
L2: Simple Agents
Select topic, team mates
AI Nugget Presentation topic
9/29
see above
Due: AI Nugget topic & presentation date
MS Week 2: Features, Requirements, Schedule
topic proposal and date selection
3
10/4
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
A1: Search Algorithms L3: Breadth-First Search, Depth-First Search
10/6
Uninformed Search Strategies
Search without domain knowledge: breadth-first and depth-first strategies; improvements for these strategies; limitations of uninformed search
AIMA 3, Ertel 6.1, 6.2, depth-first breadth-first @ Wikipedia
4
10/11
Informed Search Search with domain knowledge: heuristics, greedy best-first search, A* search
AIMA 4.1, 4.2, Search @ AAAI
Informed Search @ Wikipedia
L4: AI in Entertainment (e.g. Games, Movies)
10/13
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, Ertel 6.3
A2: Wumpus World
MS Week 4: Prototype 1 (alpha)
5
10/18
Games Games as Adversarial Search: Two-person, zero-sum games, search strategies, minimax, alpha-beta pruning, element of chance
AIMA 6, Ertel 6.4Games @ AAAI
Games in AI @ Wikipedia
L5: AI in Real Life
10/20
see above
A1 due
A1: Search Algorithms
6
10/25
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
L6: Local Search: Constraint Satisfaction, Hill-Climbing
10/27
Logic
propositional logic, predicate logic, inference methods, resolution, unification, forward and backward chaining
AIMA 7, 8, Ertel 2, 3Logic @ AAAI
Games in AI @ Wikipedia
A2 Part 1 due
MS Week 6: Prototype 2 (beta)
7
11/1
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, Ertel 4, 5 (Knowledge) Representation @ AAAI
Knowledge representation @ Wikipedia
L7: Wumpus World Agent
11/3
see above
A2 Part 2 due
8
11/8
Learning Improving agent performance through learning: Forms of learning; inductive learning, decision trees; computational learning theory;
AIMA 18, 19, Ertel 8(Machine) Learning @ AAAI
(Machine) Learning @ Wikipedia
L8: Logical Wumpus World Agent
11/10
A2 Part 2 due
MS Week 8: Final Version
9
11/15
Learning
explanation-based learning and rule extraction; statistical learning, Bayesion networks, hidden Markov models, neural networks;
reinforcement learning
AIMA 20, 21, Ertel 9, 10
L9: Learning
11/17
see above
10
11/22
Applications of AI
Examples of the use of AI methods in various domains;
L10: Something Funny
11/24
Thanksgiving Break - No Class
11
11/29
Ethical and social issues in AI; Team Project Presentations
Delegation of important decisions to computers; augmentation and replacement of human capabilities; "singularity"
Ethics of AI @ AAAI
Applications of AI @ AAAI
Ethics of AI @ Wikipedia
A3 Competitions
Project Presentations Feedback and Evaluation forms
Project Presentations Project Presentations
12/1
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".
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