Internal/Teaching AI

Project

Keep Java server, strongly recommend they write players in Python - test the connectivity

Setup network environment to run the server. Can it be r2s server or a virtual machine on the wiki server (talk with Nicholas)?

Labs

• Lab1:
  • reflex agent, using rule-based reasoning engine
    • let's try pyke
    • create 2-3 simple examples
  • simple mobile robot controller
    • let's try Webots http://www.cyberbotics.com/overview
    • we either create our own world, or we use one of the competitions: the rat maze or the savanna world
    • the trick is to have individual decisions be complex enough (we don't want to have too much memory yet)
    • make sure Webots is easy to integrate with pyke!
  • simple poker player
    • demonstrate how to stratify the knowledge (levels of hand quality, evaluate opponents, etc.)
    • how easy is it to calculate winning percentage of any given hand in pyke?

• Lab 2:
  • search algorighms: A*
  • mobile robot path planning
    • can we make it a car instead of omnidirectional robot?
    • talk to Jennifer/Gaurav about ideas?
    • do it in an abstract manner
    • but maybe we could connect to Webots later for visualisation?
  • poker bidding against known hand & strategy
    • we need to come up with sufficiently complicated strategy!
    • has to have a number of local minima
    • and a number of reasonable heuristics

• Lab3
  • logic
    • students write knowledge base in FOL
    • the idea is to "do machine learning by hand" - come up with some rules, see which examples are covered, iterate
  • human behaviour analysis and anomaly detection
    • based on data from Jens
    • generated by his simulator
    • KB should explain as many examples as possible
  • poker: deduce opponent's hand
    • based on known cards
    • and description of their strategy

• Lab 4
  • Bayesian networks
  • healthcare and medical diagnosis
    • match symptoms with causes
    • talk to Anita and Nicholas
  • poker: opponent recognition
    • given non-deterministic strategies, match play history to an opponent

• Lab 5
  • Learning
    • SVM, neural networks and/or decision trees
  • fingerprint classification (?)
    • talk with Anna (?)
    • maybe face / digit recognition (talk with Stefan K)
  • poker: learn opponent's strategy
    • train on past games
    • try to predict next bids
    • how to deal with hidden information (current hand)?