AI Problem Solving with Search and Logic - COM00050H

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  • Department: Computer Science
  • Credit value: 20 credits
  • Credit level: H
  • Academic year of delivery: 2025-26
    • See module specification for other years: 2024-25

Related modules

Pre-requisite modules

  • None

Co-requisite modules

  • None

Prohibited combinations


Elective Pre-Requisites

These pre-requisites only apply to students taking this module as an elective.

A Level Maths or Equivalent, plus good knowledge of AI fundamentals, predicate logic and programming.

Module will run

Occurrence Teaching period
A Semester 2 2025-26

Module aims

This module will introduce key approaches in Artificial Intelligence for tasks such as: finding a sequence of actions to achieve a goal; playing adversarial games; and solving discrete optimization problems such as configuration and scheduling. Students will learn the theory and practice of AI search, logic, and constraint-based approaches. The module aims to equip students with a wide range of problem-solving tools, how to design effective heuristics for them, and enable comparison of methods to determine which are best suited to a given problem. Some of the tools covered are state-space search algorithms (i.e. A* Search, IDA*, and Greedy Best-First Search), game-tree search algorithms (i.e. Minimax and Monte-Carlo Tree Search), local search methods for solving discrete optimization problems, constraint programming, and the satisfiability (SAT) problem in knowledge representation and reasoning.

Module learning outcomes

  1. Represent a given search problem in terms of states, actions, and a goal, and identify a suitable heuristic.

  2. Represent a given scenario using propositional logic to enable logical inference (for example, using a SAT solver).

  3. Model (represent) and solve discrete optimization problems using a modern constraint programming system.

  4. Select and apply an appropriate AI state-space search algorithm for a given problem, identifying reasons for the choice of algorithm in comparison to others.

  5. Select and apply an appropriate adversarial (game-tree) search method to solve a given game, including design of a suitable heuristic if required.

  6. Describe the algorithms commonly used in SAT (propositional satisfiability) solvers, local search solvers, and constraint solvers, and apply them to small examples.

Indicative assessment

Task % of module mark
Closed/in-person Exam (Centrally scheduled) 100

Special assessment rules

None

Indicative reassessment

Task % of module mark
Closed/in-person Exam (Centrally scheduled) 100

Module feedback

Feedback is provided throughout the sessions, and after the assessment as per normal University guidelines.

Indicative reading

Artificial Intelligence: A Modern Approach (4th Edition) by Stuart Russell and Peter Norvig

Multiagent Systems: Algorithmic, Game-Theoretic, and Logical Foundations by Yoav Shoham and Kevin Leyton-Brown