Module Identifier
CS26110
Module Title
THE ARTIFICIAL INTELLIGENCE TOOLBOX ¿ PART 1: HOW TO FIND SOLUTIONS
Academic Year
2008/2009
Co-ordinator
Dr Myra S Wilson
Semester
Semester 1
Co-Requisite
None
Mutually Exclusive
None
Pre-Requisite
CS12420
Other Staff
 

Course Delivery

Delivery Type Delivery length / details
Lecture 18 Hours.
Seminars / Tutorials 2 seminars on the assignment
Practical 6
 

Assessment

Assessment Type Assessment length / details Proportion
Semester Exam 2 Hours   STANDARD 2 HOUR EXAMINATION  70%
Semester Assessment WRITTEN ASSIGNMENT BASED ON COURSE MATERIAL  30%
Supplementary Assessment Will take the same form, under the terms of the Departments policy  100%

Learning Outcomes

Write simple programs in Java to search for solutions using the AI techniques discussed in the module.

Demonstrate an appreciation of at least one application of each AI method that they have studied.

Demonstrate an understanding of the learning methods discussed in this module.

Brief description

This module begins with a motivational section on the use of AI in computer games programming: one of the cutting-edge applications of AI in use today. It then focuses on the foundational issue of search (finding solutions to problems) and introduces some basic, but important, learning methods.

Content

1. Intro to AI via Games AI [2 lectures]

What is required to get computers to produce games AI? AI and agents as a principle for this module.What other areas use AI:? -- bioinformatics, business, robotics.

2. What AI methods are there? -- the rest of the course.

Introduce and describe minimax as an example of simplified game playing.
Uninformed Search [3 lectures]

Informed Search [3 lectures]

Evolutionary Computation for Search and Optimisation [3 lectures]

Learning and Adaptation (an overview) [1 lecture]

We can find solutions, using search, but how can we remember solutions, learn from them and adapt them to new situations? o What methods allow us to use our previous experience to predict the future?

Using the 'rlay Tennis? problem for Decision Trees and Neural Nets (this also raises the importance of knowledge representation)

Learning Method #1 Neural Nets [3 lectures]

Perceptrons Learning Method # 2 Decision Trees [3 lectures]

What is a decision tree? How do we use one? How do we build one?

TOTAL: 18 lectures, giving room to talk about how we use Java for AI, and room to talk about the assignment(s).

Module Skills

Skills Type Skills details
Communication During the seminars, students will be encouraged to `brainstorm¿ and communicate between themselves and with the lecturer.
Improving own Learning and Performance In order to solve the problem in (1), using the research from (2), students will improve their abilities in `learning to learn¿.
Information Technology Use of IT will be vital for the completion of this module.
Problem solving The assignment will require students to apply their newly gained AI knowledge to a problem. Only the outline of the solution will be give, so that the student must solve the details of the problem themselves.
Research skills Required to solve the problem in (1)

Notes

This module is at CQFW Level 5