IE502014 Artificial Intelligence - Overview

The following plan is tentative, based on last year’s plan. There will be changes, but the rough outline will remain.

Session Topic Reading Keywords
1 Introduction 12 January 2023 R&N Ch. 1 What is intelligence? Is AI possible?
2 Rational Agents R&N Ch. 2 Instrumental reason, practical reason, etc.
3 Search R&N Ch. 3.1-3.4 Search and Problem Solving
4 Heuristic Search R&N Ch. 3.5-3.7 Heuristic Search
5 Complex Environments R&N Ch. 4 Local Search, Partional Information etc.
6 Adversarial Search R&N Ch. 6 Game Theory
- Midterm 20-24 February - own work
7 Optimisation and GA H&H Chapter 1-2 Genetic Algoritms
8 Continuous GA H&H Chapter 3-4 Genetic Algoritms
9 Game Theory Game Theory, Simulation, Genetic Algorithm
10 Machine Learning and Statistics R&N Chapter 19
11 Markov Decision Processes MDP R&N Chapter 16
- Easter 3-10 April - holiday
12 Reinforcement Learning R&N Chapter 23
13 Deep Q-Learning for Reinforcement Learning R&N Chapter 16/23
14 AI Ethics 24 April 2023 Implementation of ethical constraints

Considering

  • Limits of AI - a review of ChatGPT and github copilot
  • R&N Chapter 7: Logical Agents
  • R&N Chapter 12: Acting under Uncertainty

Key topics

  1. Ethics.
    1. What is intelligence?
      • what is artificial intelligence?
      • is artificial intelligence at all possible?
    2. Concerns about AI
    3. Machine Morality
  2. Intelligence Agents
    • State Machines and Graph Algorithms
    • Markov Decision Processes and Reinforcement Learning
  3. Optimisation and Evolutionary Algorithms

Note There are other modules on machine learning, so this remains out of scope.