CS 371 - Introduction to Artificial Intelligence
Course Syllabus |

Note: This syllabus is tentative and subject to change. You are responsible for all material covered in lecture and assigned in readings. Unless otherwise stated, readings are from Russell & Norvig, "Artificial Intelligence: a modern approach".

Date | Topics | Readings |

Tu 9/3 | Introduction, "What is AI?", agents, agent architectures, and environments | Ch. 2, Deep Blue article |

Th 9/5 | Search: search problem formulation and tradeoffs, uninformed search, breadth-first search, depth-first search, iterative-deepening | Ch. 3 |

Tu 9/10 | Search | |

Th 9/12 | Informed Search: heuristic functions, admissibility, monotonicity, best-first search, greedy search, A*, iterative-deepening A* (IDA*), epsilon-admissibility, epsilon-admissible IDA* | Sect. 4.1-4.3 (excluding SMA*) |

Tu 9/17 | Informed Search | |

Th 9/19 | Advanced Search Topics: node ordering, breadth limited search, iterative-broadening search, beam search, state-based discretization, action-based discretization, iterative-refinement search | Neller AAAI 2002 paper (optional) |

Tu 9/24 | Iterative Improvement Search: hill climbing / gradient descent, local maxima / minima, plateaux, ridges, random restarts, simulated annealing | Sect. 4.4 |

Th 10/10 | Game-tree Search: zero-sum games, utility functions, minimax | Sect. 5.1-5.3 |

Th 10/17 | Game-tree Search: alpha-beta pruning | Sect. 5.4 |

Tu 10/22 | Probabilistic Reasoning: qualification problem, probability theory, utility theory, decision theory, decision-theoretic agent, prior/unconditional probabilities, posterior/conditional probabilities, product rule, probability axioms, joint probability distribution, Bayes' rule, normalization | Ch. 14 |

Th 10/24 | Probabilistic Reasoning: belief (Bayesian) networks, noisy-OR relations, d-separation, diagnostic/causal/intercausal/mixed inference, stochastic simulation (Markov Chain Monte Carlo) | Ch. 15 (except 15.3 algorithm, 15.4, and 15.6), excerpt from Pearl, Judea. "Probabilistic Reasoning in Intelligent Systems: networks of plausible inference", section 4.4.3 (skipping "Concurrent Simulation with Distributed Control") |

Th 11/14 | Propositional Logic: knowledge-based agent, syntax, semantics, truth tables, interpretation, entailment, proof, inference procedure, soundness, completeness, models, validity, (un)satisfiability | Ch. 6 |

Tu 12/26 | Robotics: state machines, firmware, NQC | Lego robotics documentation (in lounge cabinet) |

Todd Neller