Revision

University of Essex - CE213 Introduction to Artificial Intelligence

Learning Outcomes

  • Explain and criticize arguments for and against the possibility of artificial intelligence.

  • Implement standard blind and heuristic search procedures; understand their strengths and weaknesses.

  • Explain operation of production system interpreters and their pros and cons.

  • Understand established machine learning procedures and appropriate problem types.

  • Understand agent-oriented AI and build multi-agent systems using subsumption architecture.

Basic AI Concepts and Methods

Key Terms

  • AI Machine Learning: Knowledge Representation, Search

  • Critical Concepts: Generate and Evaluate, State Space Representation

  • Search Strategies: Blind Search, Heuristic Search, Game Playing

Methods

  • Importance of Knowledge Production System

    • Forward Chaining

    • Backward Chaining

  • MYCIN as a production system example

Problem Solving Skills - Checklist

  • Formalize problems into state space representation.

  • Analyze properties of search strategies (4 criteria).

  • Find optimal routes using search strategies and heuristics.

  • Minimax search for game trees using heuristic values.

  • Update game trees through MCTS iterations.

  • Identify rule firing and conclusions using forward chaining.

  • Determine certainty of conclusions using backward chaining and MYCIN’s system.

  • Calculate information and information gain using Shannon’s formula.

  • Induce decision trees based on information gain.

Sample Questions and Revision Material

  • Review CE213 exam rubric and sample questions on Moodle.

  • Past CE213 exam papers available for practice.

  • Note: Exam format is Open Book (Restricted), no bookwork questions.

  • Typical applications: puzzles, game playing, robot control, prediction, data mining.

Useful Equations

  1. CF_combined = CFr1 + (1 - CFr1) x CFr2

  2. P(X|Y) = P(Y|X) x P(X) / P(Y)

  3. Information = -3p5log8(p5)

Academic Support

  • Academic Support Hours for CE213 revision: Thursdays 4-6pm on Zoom.

  • CE213 Help Forum on Moodle for queries: Post discussion topics for assistance.

Lecture Summaries

Lecture 1: What is AI?

  • AI defined as science and engineering of making intelligent machines.

  • Discussion on the feasibility of AI including Lady Lovelace's objection, Turing Test, and Searle's Chinese Room.

Lecture 2: Problem Solving

  • Example: Corn, Goose and Fox Puzzle - essence of problem abstraction and systematic search.

Lecture 3: Search Strategies

  • Types: Breadth-First Search, Depth-First Search, Iterative Deepening, Uniform Cost Search.

  • Comparison based on completeness, optimality, time, and space complexity.

Lecture 4: Advanced Search Strategies

  • Greedy Search and A* Search - optimal and efficient but need good heuristics.

  • Overview of Hill Climbing.

Lectures 5 & 6: Minimaxing & Adversarial Search

  • Evaluation functions for adversarial search, alpha-beta pruning implementation, Monte-Carlo Tree Search overview.

Lecture 7: Knowledge and Problem Solving

  • Production systems for knowledge representation; procedural representation.

Lecture 8: Production Systems

  • Core components: production rules, interpreters, environment.

Lecture 9: MYCIN System

  • Knowledge representation methods; reasoning with uncertainty and explanation generation.

Lecture 10: Machine Learning Fundamentals

  • Key elements, functioning, and tasks involved in machine learning.

Lecture 11: Decision Trees

  • Structure, induction procedures, and information gain evaluation.

Lecture 12: Challenges in Decision Trees

  • Issues like inconsistent data, numeric attributes, and overfitting.

Lectures 13-14: Neural Networks

  • MP Neurons, learning rules, delta rule limitations, and necessity of nonlinear neurons for complex models.

Lectures 15: Error Back-Propagation

  • Understanding error back-propagation for multilayer neural networks and its challenges.

Lecture 16: Clustering Techniques

  • Methods: Agglomerative Hierarchical Clustering, K-Means; evaluation metrics.

Lectures 17-18: Reinforcement Learning & Q Learning

  • Concepts, representation of MDPs, Q learning algorithms, and usage in practice.

Lectures 19-20: Genetic Algorithms & Agents

  • Basis of genetic algorithms; characteristics of intelligent agents, architecture arrangements for task accomplishment.