Dates for Semester 2, 2010.
See also the course calendar.
Week 1 - 19/7
Introduction
Course Overview
Agent/Planning systems
Introduction to Bayesian statistics
BayesianPrediction
What is a (Bayesian) probability?
What is Bayes' Rule?
How can you use Bayes' Rule to do sequence prediction?
Week 2 - 26/7
Utility Theory
?Bayesian Agents
Representation
Simple probability distributions
Observability assumptions
Lab (Level 3)
Intro to rUNSWift agent
Subversion
Compiling
Running
Base Station
AIBO-Overview
rUNSWift overview
Aperios
Our Objects/systems
Low/High level vision
Tracking
Behaviours
Wireless
ActuatorControl
Flow Control between objects
Interfaces between objects
RobotVision
Robot Vision
Colour segmentation
Blobbing
High level concepts
Kinematics
Motion (low level)
Head Control
Forward and Inverse Kinematics (Head and Legs)
Point Projection
Non-Commutable Rotations
Recorded motions
Week 3 - 2/8
BayesianLocalisationI
Tracking
Bayesian Localization I
Tabular representation
BayesianLocalisationII
Tracking
Bayesian Localization II
Particle Filters
Kalman-Bucy Filters
Lab (Level 3)
Ball tracking
Assignment 1 out at end of lab (Tracking & Life Saver)
Assignment 3 out at end of lab (Grab Ball - get to other half of field)
Week 4 - 9/8
BayesianLocalisationIII
Finish Tracking
Information form Kalman Filters
SLAM
Expectation Maximisation if time
Walking
Motion (Walking)
Walking
Normal Walk
Odomentry Calibration
Parameter Optimisation
Lab (Level 3)
Go To Point
Experiments with tracking (odom/obs, bad mean/var)
Week 5 - 16/8
AIBO Behaviour and Comms
Wireless
UDP vs TCP
Behaviours
Hysteresis
Hierarchies
Software Engineering and State
Agent Architectures
Agent Architectures
Engineered (subsumption)
Logic based (Three layer, planning, golog)
Cognitive (ACT-R, not Golog in this sense)
Reinforcement Learning
Lab (Level 3)
Work on Assignment 1
Week 6 - 23/8
SearchI
Introduction to Search
BFS/DFS
A*
Islands
SearchII
Further search - heuristics
Mixing Search and Heuristics (Korf)
Heuristics as simplifications/relaxations
Lab (Level 3)
Assignment 1 graded in lab
Week 7 - 30/8
Overview and Models
Reinforcemnt Learning
Style of problem
Optimality criteria
Exploration/Exploitation trade-off
?PlaningI
Planning
State Space search
Lab (Computer lab)
A*/Islands/Heuristics
Mid Semester Break - 6/9
Week 8 - 13/9
MDP
Markov Decision Processes
Formal definition
Model based updates
Certainty equivalence
Prioritized Sweeping
Model free updates
Q-learning
Adaptive Heuristic Critic
PlanningII
Planning
Plan space planning
Lab (Level 3)
Work on Assignment 3
Week 9 20/9
Discretization
Applying discrete search to continuous problems
Obstacle expansion
Different representations
OptimizationI
Rapidly-Exploring Random Trees
Introduction to Optimization
1D Golden Section Search
Lab (Computer lab)
Planning
Week 10 - 27/9
OptimizationII
Gradient descent
Simplex search
Powell's Method
Examples
Gradient Descent
Conjugate Gradient
Quasi-Newton methods
Gradient estimation
Handling noise
Oversampling
Pegasus and pseudo-randomness
Function fitting
Meta-search
Examples
Walk learning as application of optimization
Gradient descent over a stochastic policy in gridworld
Lab (Computer Lab)
Optimization
Week 11 - 4/10
POMDPs
Partially Observable Markov Decision Processes
Belief state Tracking based methods
History methods
Explicit memory methods
Piecewise linear value function
Genetics
Genetic Algorithms
Shock
Lab
No Lab - use time for review if needed.
Week 12 - 11/10
Adversarial
Assignment 2 Due at start of lecture.
Adversarial Agents
Minimax search
Stochastic Policies
Best Response Learning
Function Approximation
Review
Week 13 - 18/10
Lab (Level 3)
Assignment 3 graded in lab