CPSC 444 Schedule

Week 1: 1/16-1/20

Topics: course introduction; reactive agents: boids and steering behaviors

• slides (course introduction)

• C. Reynolds, Steering Behaviors for Autonomous Characters

• boids (extensive list of other resources related to boids)

• slides (reactive agents, boids, steering behaviors)

Week 2: 1/23-1/27

Topics: boids and steering behaviors; combining behaviors; decision making

• slides (combining behaviors)
• slides (action selection - decision trees, FSMs)

• On Finite State Machines and Reusability
• The Gist of Hierarchical FSM
• Choosing a Hierarchical FSM or a Hierarchy of Nested FSMs?

• slides (implementing decision trees and FSMs; behavior trees)

• implementing FSMs
  • implementing a hardcoded FSM
  • Common Ways to Implement Finite State Machines in Games
• behavior trees
  • Understanding Behavior Trees
  • The Power of Sequences for Hierarchical Behaviors
  • The Flexibility of Selectors for Hierarchical Logic
  • Using Decorators to Improve Behaviors
  • Enabling Concurrency in Your Behavior Hierarchy
  • Popular Approaches to Behavior Tree Design
• implementing behavior trees
  • Termination Status for Behaviors
  • How to Build Hierarchical Behaviors
  • How to Build Concurrent Behaviors
  • A Low-Level Task Class
  • A Scheduler for Tasks
  • Observing the Scheduler

Week 3: 1/30-2/3

Topics: problem solving as search; uninformed search; informed search

• slides (problem solving via search: representation, uninformed search)

• slides (comments on hw 1)

• slides (comments on hw 3)
• slides (search: uninformed and informed)

Week 4: 2/6-2/10

Topics: work on project

Week 5: 2/13-2/17

Topics: informed search, adversarial search

• slides (informed search - IDA*, RBFS, SMA*)

• slides (heuristic functions)

Week 6: 2/20-2/24

Topics: adversarial search, pathfinding

• slides (adversarial search - minimax, alpha-beta pruning, move ordering)

Slides:

• slides (adversarial search - additional optimizations and variations)

• slides (pathfinding)

• slides (pathfinding - waypoint generation, navigation meshes, path smoothing, hierarchical pathfinding)

Week 7: 2/27-3/3

Topics: pathfinding, classical planning

• slides (pathfinding - end of hierarchical pathfinding, open goal pathfinding, dynamic pathfinding, continuous time pathfinding)

• slides (pathfinding - movement planning; classical planning)

• slides (about hw 3, 4, 6)

Week 8: 3/6-3/10

Topics: classical planning

• slides (classical planning - forward and backward search, generating heuristics, planning graphs)

• review information

• slides (comments on hw 7)
• slides (using a planning graph as a heuristic)

• slides (heuristics involving mutex links, using a planning graph directly, beyond classical planning)
• slides (a few more comments on hw 7, issues and considerations in deciding on representations for planning)

Spring Break: 3/11-3/29

Week 9: 3/20-3/24

Topics: genetic algorithms

• slides (genetic algorithms - building blocks, simple genetic algorithms, challenges)

• The Evolution of Code (genetic algorithms)
• Genetic Algorithms in Plain English

• slides (genetic algorithms - challenges, alternatives for representation and selection)

• slides (genetic algorithms - crossover, mutation, steady state and survivor selection, strengths and limitations)

Week 10: 3/27-3/31

Topics: genetic algorithms; machine learning

• slides (genetic programming)

• eaters (genetic algorithm involving a cellular automaton)

• A Field Guide to Genetic Programming
• Competition, Coevolution and the Game of Tag
• Evolution of Corridor Following Behavior in a Noisy World

• slides (genetic programming - applications)
• slides (machine learning - introduction, rote learning)

• slides (machine learning - rote learning, backwards induction, temporal difference)

Week 11: 4/3-4/7

Topics: machine learning

• project 1 comments
• slides (machine learning - Q-learning, SARSA)

• slides (reinforcement learning wrapup)

• project 2 comments
• slides (neural networks - introduction, network structure)

Week 12: 4/10-4/14

Topics: machine learning

• slides (neural networks - training; learning recap)

• slides (machine learning - decision trees)

• review information

• slides (machine learning - classifiers)

Week 13: 4/17-4/21

Topics: machine learning

• slides (hw11)
• slides (machine learning - classifiers)

Week 14: 4/24-4/28

Topics: machine learning; state of the art; intelligence, philosophical, and ethical issues

• slides (hw13)
• slides (machine learning wrapup; intelligence)
• Vehicles excerpt

• slides (intelligence; state of the art)
• Meet the People Who Train the Robots (to Do Their Own Jobs) (NY Times)
• The Great A.I. Awakening (NY Times)
• Will AI Destroy Humanity?

Read all three articles for Monday if you can; start with the two shorter ones (the first and last ones) if you are short on time. The NYT articles are behind a paywall; printed copies of all three articles are available outside my office if you can't access them.

Week 15: 5/1-5/2

Topics: wrapup

• see materials posted for last Friday's class

Slides:

• slides (wrapup - philosophical issues, limitations and dangers, possibilities)

Reading Period: 5/3-5/5

Slides:

• project 3 comments
• project 4 comments

Exams: 5/6-5/9

• review information

last updated: --Fri May 5 22:38:50 EDT 2017--
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