cse2aif artificial intelligence
ARTIFICIAL INTELLIGENCE FUNDAMENTALS
CSE2AIF
2016
Credit points: 15
Subject outline
This subject covers the fundamental areas of Artificial Intelligence, focussing on knowledge representation and search. Main topics include historical foundations and applications areas; state-space search; game-playing; knowledge representation languages including predicate calculus, semantic networks, conceptual graphs and frames; rule-based expert systems; and an introduction to symbolic and connectionist machine learning.
SchoolSchool Engineering&Mathematical Sciences
Credit points15
Subject Co-ordinatorAndrew Skabar
Available to Study Abroad StudentsYes
Subject year levelYear Level 2 - UG
Exchange StudentsYes
Subject particulars
Subject rules
Prerequisites CSE1IOO
Co-requisitesN/A
Incompatible subjects CSE2AI, CSE4FAI
Equivalent subjectsN/A
Special conditionsN/A
Learning resources
Readings
| Resource Type | Title | Resource Requirement | Author and Year | Publisher |
|---|---|---|---|---|
| Readings | Artificial intelligence: structures and strategies for complex problem solving. | Prescribed | Luger, G. | 6TH EDN., ADDISON WESLEY |
| Readings | Artificial intelligence: A modern approach. | Recommended | Russel, S. and Norvig, P. | 2ND EDN., PRENTICE-HALL |
Graduate capabilities & intended learning outcomes
01. Describe broadly the scope of the field of Artificial Intelligence.
- Activities:
- Students learn about the scope of AI in introductory lectures.
- Related graduate capabilities and elements:
- Ethical Awareness(Ethical Awareness)
- Discipline-specific GCs(Discipline-specific GCs)
02. Devise appropriate representations for state space search, and other search problems including two-player games.
- Activities:
- Approximately two lectures are devoted to this. Students explore representation schemes on simple problems in practice classes in two practice classes. These teaching activities link to assessment on both the assignment and examination.
- Related graduate capabilities and elements:
- Quantitative Literacy/ Numeracy(Quantitative Literacy/ Numeracy)
- Discipline-specific GCs(Discipline-specific GCs)
- Critical Thinking(Critical Thinking)
- Creative Problem-solving(Creative Problem-solving)
03. Represent knowledge using predicate calculus, and manually apply resolution refutation theorem-proving.
- Activities:
- Approximately five lectures are devoted to this ILO. Two of the practice classes give students direct experience in applying these techniques to reasoning problems. These teaching activities link to assessment on both the assignment and examination.
- Related graduate capabilities and elements:
- Quantitative Literacy/ Numeracy(Quantitative Literacy/ Numeracy)
- Discipline-specific GCs(Discipline-specific GCs)
- Critical Thinking(Critical Thinking)
- Creative Problem-solving(Creative Problem-solving)
- Writing(Writing)
04. Represent simple situations using a variety of other discipline-specific knowledge representation schemes including conceptual graphs and frames.
- Activities:
- Approximately two lectures are devoted explicitly to this ILO. Practice classes allow students to represent situations using both frame representations and conceptual graph representation schemes. These teaching activities link to assessment on the examination.
- Related graduate capabilities and elements:
- Quantitative Literacy/ Numeracy(Quantitative Literacy/ Numeracy)
- Critical Thinking(Critical Thinking)
- Discipline-specific GCs(Discipline-specific GCs)
- Inquiry/ Research(Inquiry/ Research)
- Creative Problem-solving(Creative Problem-solving)
05. Construct simple expert systems using an expert system shell.
- Activities:
- Theory of Expert Systems is covered in lectures. In one practical class students learn about the CLIPS Expert System Shell; in another practice class they use the CLIPS expert system shell to construct a simple expert system. These teaching activities link to assessment on the examination.
- Related graduate capabilities and elements:
- Critical Thinking(Critical Thinking)
- Creative Problem-solving(Creative Problem-solving)
- Discipline-specific GCs(Discipline-specific GCs)
- Writing(Writing)
- Quantitative Literacy/ Numeracy(Quantitative Literacy/ Numeracy)
06. Write simple programs using the LISP and PROLOG computer programming languages.
- Activities:
- One lecture in each of the first two weeks is used to teach students introductory LISP programming. These skills are then reinforced during three practice classes. Approximately one and a half lectures are devoted to teaching students PROLOG programming skills. These skills are then reinforced in practice classes. Teaching activities for this ILO link to assessment on both the assignment and the examination.
- Related graduate capabilities and elements:
- Creative Problem-solving(Creative Problem-solving)
- Discipline-specific GCs(Discipline-specific GCs)
- Quantitative Literacy/ Numeracy(Quantitative Literacy/ Numeracy)
- Critical Thinking(Critical Thinking)
Subject options
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Melbourne, 2016, Semester 2, Day
Overview
Online enrolmentYes
Maximum enrolment sizeN/A
Enrolment information
Subject Instance Co-ordinatorAndrew Skabar
Class requirements
Computer LaboratoryWeek: 31 - 43
One 2.0 hours computer laboratory per week on weekdays during the day from week 31 to week 43 and delivered via face-to-face.
LectureWeek: 31 - 43
Two 1.0 hours lecture per week on weekdays during the day from week 31 to week 43 and delivered via face-to-face.
Assessments
| Assessment element | Comments | % | ILO* |
|---|---|---|---|
| Assignment 1 (equiv to 500 words) | 10 | 02, 06 | |
| Assignment 2 (equiv to 1000 words) | 20 | 02, 03, 05, 06 | |
| One 3-hour examination | 70 | 01, 02, 03, 04, 05, 06 |