Week 0 - 21/7

A

No lecture A in week 0

B

Re-schedule classes to try and accommodate everyone.

Lab

No labs week 0

Week 1 - 28/7

Lecture1A

Course Overview
Agent/Planning systems

Lecture1B

rUNSWift overview
  Aperios
  Our Objects/systems
    Low/High level vision
    Tracking
    Behaviours
    Wireless
    ActuatorControl
  Flow Control between objects
  Interfaces between objects

Lab (Level 3)

Intro to rUNSWift agent
  Subversion
  Compiling
  Running
  Base Station

Week 2 - 4/8

Lecture2A

Robot Vision
  Colour segmentation
  Blobbing
  High level concepts

Lecture2B

Motion (low level)
  Head Control
  Forward and Inverse Kinematics  (Head and Legs)
  Point Projection
  Non-Commutable Rotations
  Recorded motions

Lab (Level 3)

Ball tracking
Assignment 1 out at end of lab (Tracking & Life Saver)
Assignment 2 out at end of lab (Grab Ball - get to other half of field)
Assignment 3 out at end of lab (1x1 penalty shooter)

Week 3 - 11/8

Lecture3A

Tracking
  Bayesian Localization I
    Tabular representation

Lecture3B

Tracking
  Bayesian Localization II
    Particle Filters
    Kalman-Bucy Filters

Lab (Level 3)

Go To Point
  Experiments with tracking (odom/obs, bad mean/var)

Week 4 - 18/8

Lecture4A

Finish Tracking
Information form Kalman Filters
SLAM
  Expectation Maximisation if time

Lecture4B

Motion (Walking)
  Walking
    Normal Walk
    Odomentry Calibration
    Parameter Optimisation

Lab (Level 3)

Work on Assignment 1

Week 5 - 25/8

Lecture5A

Wireless
  UDP vs TCP
Behaviours
  Hysteresis
  Hierarchies
  Software Engineering and State

Lecture5B

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 - 1/9

Lecture6A

Introduction to Search
  BFS/DFS
  A*
  Islands

Lecture6B

Further search - heuristics
  Mixing Search and Heuristics (Korf)
  Heuristics as simplifications/relaxations

Lab (Level 3)

Assignment 1 graded in lab

Week 7 - 8/9

Lecture7A

Reinforcemnt Learning
  Style of problem
  Optimality criteria
  Exploration/Exploitation trade-off

Lecture7B

Planning
  State Space search

Lab (Computer lab)

A*/Islands/Heuristics

Week 8 - 15/9

Lecture8A

Markov Decision Processes
  Formal definition
  Model based updates
  Certainty equivalence
  Prioritized Sweeping
  Model free updates
    Q-learning
    Adaptive Heuristic Critic

Lecture8B

Planning
  Plan space planning

Lab (Level 3)

Work on Assignment 3

Week 9 22/9

Lecture9A

Applying discrete search to continuous problems
  Obstacle expansion
  Different representations

Lecture9B

Rapidly-Exploring Random Trees
Introduction to Optimization
  1D Golden Section Search

Assignment 2 Due at start of lecture.

Lab (Computer lab)

Planning

Mid Semester Break - 29/9

Week 10 - 6/10

Lecture10A

Gradient descent
  Simplex search
  Powell's Method

Lecture10B

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 - 13/10

Lecture11A

Partially Observable Markov Decision Processes
  Belief state Tracking based methods
  History methods
  Explicit memory methods

  Piecewise linear value function

Lecture11B

Genetic Algorithms
  Shock

Lab

No Lab - use time for review if needed.

Week 12 - 20/10

Lecture12A

Adversarial Agents
  Minimax search
  Stochastic Policies
  Best Response Learning

Lecture12B

Review

Lab (Level 3)

Assignment 3 graded in lab