| Week | Date | Topic/Read Before Class | Notes | Homework |
| 1 | M, 09/04 | Labor day: no class | ||
| W, 09/06 | Ch. 01. Introduction | Ch. 01 - Introduction
full page pdf, ps, 4 per page ps |
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| F, 09/08 | Ch. 02 - Intelligent Agents | Ch. 02 - Intelligent Agents
full page pdf, ps, 4 per page ps |
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| 2 | M, 09/11 | Ch. 02 - Intelligent Agents (cont'd) | ||
| W, 09/13 | Ch. 03 - Problem Solving by Searching
goal-driven agents formulating search problems formulating search problems basic search algorithm blind search |
Ch. 03 - Problem Solving by Searching full page pdf, ps, 4 per page ps |
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| F, 09/15 | Ch. 03 - Problem Solving by Searching
basic search algorithm blind search breadth-first search |
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| 3 | M, 09/18 | Ch. 03 - Problem Solving by Searching
basic search algorithm blind search breadth-first search uniform cost search depth-first-search depth-limited-search iterative deepening search iterative broadening search |
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| W, 09/20 | Ch. 04 - Informed search and exploration
Best first search Greedy search A* search Admissible heuristics A* is optimal |
Ch. 04 - Informed Search and Exploration full page pdf, ps, 4 per page ps |
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| F, 09/22 | Ch. 04 - Informed search and exploration
Properties of heuristics Admissible heuristics Consistent heuristics Heuristic dominance IDA* search IDA* is optimal Performance of IDA* |
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| 4 | M, 9/25 | Ch. 04 - Informed search and exploration
Designing heuristics Relaxed problems Minimum Spanning tree heuristic for TSP Iterative improvement algorithms Hill climbing Simulated annealing Tabu search, stochastic hill climbing, genetic algorithms, ant colony optimization Assign and explain homework 1 |
hw1: search
assigned |
|
| W, 09/27 |
Ch. 05 - Constraint Satisfaction Problems
Definition, example Backtracking search Heuristics for backtracking search Arc consistency |
Ch. 05 - CSP
full page pdf, ps, 4 per page ps |
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| F, 09/29 |
Ch. 05 - Constraint Satisfaction Problems
Arc consistency |
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| 5 | M, 10/02 |
Ch. 05 - Constraint Satisfaction Problems
Tree structured CSPs |
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| W, 10/04 |
Roland's tips for homework 1 Java (thanks!)
Ch. 05 - Constraint Satisfaction Problems Iterative algorithms for CSPs |
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| F, 10/06 |
Temporal Constraint Networks
(Addition to Chapter 5) The interval algebra (IA) Definition, examples Path consistency in CSPs Path consistency in IA Qualitative Path Consistency (QPC) algorithm |
Ch. 05b- Temporal CSP
full page pdf, ps, 4 per page ps |
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| 6 | M, 10/09 |
Temporal Constraint Networks
(Addition to Chapter 5) The interval algebra (IA) Path consistency in IA Qualitative Path Consistency (QPC) algorithm The point algebra (PA) Definition, examples Composition in PA Properties of PA algorithms |
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| W, 10/11 |
Temporal Constraint Networks
(Addition to Chapter 5) Simple Temporal Networks (STNs) Definition, examples Floyd-Warshall Algorithm |
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| F, 10/13 |
Temporal Constraint Networks
(Addition to Chapter 5) Simple Temporal Networks (STNs) Floyd-Warshall Algorithm Properties of STN/TCSP algorithms |
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| 7 | M,10/16 |
Ch. 11 - Planning
Planning vs. search State space search Operator description |
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| W, 10/18 | ||||
| F, 10/20 | No class due to the midterm exam (take home)
Handed out: Monday, 10/23/06 at 11:55am Due: Wednesday, 10/25/06 at 11:05am |
Good luck! | ||
| 8 | M, 10/23 | |||
| W, 10/25 | ||||
| F, 10/27 | ||||
| 9 | M, 10/30 |
Ch. 11 - Planning
The Satplan algorithm Boolean satisfiability (SAT) Satplan architecture Simplifying CNF formulas Building CNF formulas for planning problems Frame axioms (full) |
Ch. 11c - Satplan
full page pdf, ps, 4 per page ps |
|
| W, 11/01 |
Return and discuss exam 1
Ch. 11 - Planning The Satplan algorithm Frame axioms (full, explanatory) Properties of frame axioms Symbol splitting |
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| F, 11/03 |
Ch. 11 - Planning
The Satplan algorithm SAT solving algorithms (DPLL, WALKSAT, GSAT) Ch. 16 - Making Simple Decisions Maximizing expected utility Influence diagrams |
Ch. 16 - decision making
full page pdf, ps, 4 per page ps |
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| 10 | M, 11/06 |
Ch. 16 - Making Simple Decisions
Influence diagrams Coming up with utility numbers Stochastic dominance |
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| W, 11/08 |
Ch. 17 - Making Complex Decisions
MDP problems Bellman updates Solving MDPs using value iteration Solving MDPs using policy iteration Mr. Li Li taught this class. |
Ch. 17 - MDPs
full page pdf, ps, 4 per page ps |
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| F, 11/10 |
Ch. 16 - Making Simple Decisions
Value of information Ch. 17 - Making Complex Decisions Policy and value iteration |
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| 11 | M, 11/13 | Ch. 17 - Making Complex Decisions
The RTDP algorithm Ch. 13 - Uncertainty Probability basics Random variables Prior probability Conditional probability |
Ch. 13 - uncertainty
full page pdf, 6 per page pdf |
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| W, 11/15 |
Ch. 13 - Uncertainty
Inference by enumeration Normalization, the α notation Independence Conditional independence |
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| F, 11/17 | Return and discuss homework 1
Ch. 13 - Uncertainty Conditional independence Bayes' rule The Wumpus world |
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| -- | M, 11/20 | Thanksgiving recess | ||
| W, 11/22 | Thanksgiving recess | |||
| F, 11/24 | Thanksgiving recess | |||
| 12 | M, 11/27 | Ch. 13 - Uncertainty
Finish up the wumpus world Ch. 14 - Probabilistic Reasoning Section 14.1 Representing Knowledge in an Uncertain Domain Section 14.2 The Semantics of Bayesian Networks Section 14.3 Efficient Representation of Conditional Distributions |
Ch. 14a - Sections 14.1 - 14.3
full page pdf, 6 per page pdf |
|
| W, 11/29 |
Ch. 14 - Probabilistic Reasoning
Section 14.4 Exact Inference in Bayesian Networks |
Ch. 14b - Sections 14.4 - 14.5
full page pdf, 6 per page pdf | hw3: planning | |
| W, 11/09 |
Ch. 14 - Probabilistic Reasoning
Section 14.5 Approximate Inference in Bayesian Networks |
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| F, 12/01 | ||||
| 13 | M, 12/04 | Final exam (take home)
Handed out: Monday, 12/04/06 at 11:55am Due: Wednesday, 12/06/06 at 11:05am |
Good luck! | |
| W, 12/06 | Student presentations:
Chris' presentation |
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| F, 12/08 | Student presentations:
Roland's presentation Ray's presentation |
hw5: about AI
assigned |
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| 14 | M, 12/11 | Student presentations:
Brian's presentation Mike's presentation |
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| W, 12/13 | Student presentations:
Lei's presentation Mingsong's presentation |
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| F, 12/15 | Student presentations:
Alicia's presentation Bob's presentation |
Homework 5 (About AI) collected | ||
| Finals | No exam during finals week |