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# AI Problem Solving with Search and Logic - COM00191M

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• Department: Computer Science
• Credit value: 20 credits
• Credit level: M
• Academic year of delivery: 2024-25

## Related modules

• None

• None

### Prohibited combinations

Prerequisite knowledge includes an understanding of AI, machine learning and optimisation. For undergraduate students this knowledge is taught in (IMLO COM00026I or INT2 COM00024I).

Co-requisite modules None

Prohibited combinations None.

MSc AI: Students who have previously studied CONS - Constraint Programming (COM00159M) are not able to take AIPS.

## Module will run

Occurrence Teaching period
A Semester 2 2024-25

## 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

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.