COMP 424 - Artificial Intelligence

• This was an Artificial Intelligence course that I took during my fifth year at McGill. Some of the topics we covered are listed below.
  • Uninformed Search - state space graph, search tree, BFS, DFS, uniform-cost search, depth-limited search, iterative deepening search
  • Informed Search - heuristics, best-first search, heuristic search, admissible heuristics, consistent heuristics, A* search, dominance
  • Local Search for Optimization - hill climbing, simulated annealing, parallel search, local beam search, genetic algorithms
  • Constraint Satisfaction Problems - constructive vs local search approaches, backtracking search, constraint graph, arc consistency, AC-3, forward checking
  • Searching Under Uncertainty - partial observability, unobservability, non-deterministic actions, conformant planning, AND-OR search, belief states
  • Game Playing - minimax search, evaluation functions, alpha-beta pruning, forward pruning, monte-carlo tree search (MCTS), upper confidence trees, exploration vs exploitation tradeoff
  • Knowledge Representation and Logic - propositional logic, propositional inference, model checking, normal forms (CNF, DNF, Horn), validity and satisfiability, forward chaining, backward chaining, first-order logic (FOL), quantifiers, FOL inference, FOL proofs, resolution, skolemization
  • Planning - STRIPS planning, progression/forward planning, regression/backward planning, planning as logic
  • Modelling Uncertainty - bayesian probability, probabilistic inference, naive bayes model, bayesian networks, inference in bayesian networks, variable elimination algorithm, bayes ball algorithm
  • Learning in Bayesian Networks - maximum likelihood estimation (MLE), laplace smoothing, latent variables, learning with missing data, expectation maximization (EM), soft vs hard EM, k-means clustering
  • Causal Graphical Models - causation vs correlation, do-operator, causal vs probabilistic conditioning, identifying causal effects, back-door paths
  • Utility and Decision Theory - axioms, lotteries, maximum expected utility (MEU), decision graphs, value of information
  • Bandits - bandit problems, greedy bandits, epsilon-greedy bandits, softmax action selection, optimistic initialization, upper-confidence bounds, contextual bandits
  • Markov Decision Processes (MDP) - sequential decision-making, MDP framework, policy iteration, policy evaluation, bellman equation, value iteration, reinforcement learning
• We had four written assignments throughout the course which were focused on class material. For our final project, we were required to build an intelligent agent for the board game Colosseum Survival. The project was done in pairs and my partner and I implemented our agent using Monte-Carlo Tree Search equipped with domain-specific heuristics. The project code and report can be found here and a demo of our agent beating a random agent is shown below.