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FIT3080 Artificial intelligence - Semester 1 , 2008

Unit leader :

Kevin Korb

Lecturer(s) :


This subject gives students an introduction to the field of Artificial Intelligence, covering the basic techniques and mechanisms for AI programming and the construction of intelligent agents, with a focus on reasoning and actions.

Unit synopsis

020119 Artificial Intelligence

Topics include history and philosophy of artificial intelligence; intelligent agents; problem solving and search (problem representation, heuristic search, iterative improvement, game playing); knowledge representation and reasoning (extension of material on propositional and first-order logic for artificial intelligence applications, situation calculus, planning, frames and semantic networks); expert systems overview (production systems, certainty factors); reasoning under uncertainty (belief networks compared to other approaches such as fuzzy logic); machine learning (decision trees, neural networks, genetic algorithms).

Learning outcomes

Knowledge and Understanding

K1. The historical and conceptual development of AI

K2. The goals of AI and the main paradigms for achieving them, including logical inference, search, nonmonotonic logics, neural network methods and Bayesian inference

K3. The social and economic roles of AI

K4. Heuristic AI for problem solving

K5. Basic knowledge representation and reasoning mechanisms

K6. Automated planning and decision-making systems

K7. Probabilistic inference for reasoning under uncertainty

K8. Machine learning techniques and their uses

K9. Foundational issues for AI, including the frame problem and the Turing test

K10. AI programming techniques.

Attitudes, Values and Beliefs

A1. Appreciate the potential and limits of the main approaches to AI

A2. Be ready to reason critically about claims of the effectiveness of AI programs

Practical Skills

P1. Analyze problems and determine where AI techniques are applicable

P2. Implement AI problem-solving techniques in Lisp

P3. Compare AI techniques in terms of complexity, soundness and completeness


The expected weekly workload is 2 hours lecture, 2 to 3 hours programming, 7 or 8 hours reading and study.

Unit relationships


Before attempting this unit you must have satisfactorily completed FIT2004 and FIT2014 OR CSE2303 and CSE2304, or equivalent.

Students beginning FIT3080 (Artificial Intelligence) are assumed to know:

  • Basic data structures (lists, trees, graphs)
  • Basic search algorithms
  • Elementary analysis of algorithms
  • Elementary logic


FIT3080 is an elective unit in BCS, BSE, BCS/BA, BCS/LLB, BSc/BCS, and ina computer science major sequence in BSc. It is preparatory for a variety of subjects at Honours level.

You may not study this unit and CSC2091, CSC3091, CSE2309, CSE3309, DGS3691, GCO3815, GCO7835, RDT3691 in your degree.

Continuous improvement

Monash is committed to ‘Excellence in education' and strives for the highest possible quality in teaching and learning. To monitor how successful we are in providing quality teaching and learning Monash regularly seeks feedback from students, employers and staff. Two of the formal ways that you are invited to provide feedback are through Unit Evaluations and through Monquest Teaching Evaluations.

One of the key formal ways students have to provide feedback is through Unit Evaluation Surveys. It is Monash policy for every unit offered to be evaluated each year. Students are strongly encouraged to complete the surveys as they are an important avenue for students to "have their say". The feedback is anonymous and provides the Faculty with evidence of aspects that students are satisfied and areas for improvement.

Student Evaluations

The Faculty of IT administers the Unit Evaluation surveys online through the my.monash portal, although for some smaller classes there may be alternative evaluations conducted in class.

If you wish to view how previous students rated this unit, please go to http://www.monash.edu.au/unit-evaluation-reports/

Over the past few years the Faculty of Information Technology has made a number of improvements to its courses as a result of unit evaluation feedback. Some of these include systematic analysis and planning of unit improvements, and consistent assignment return guidelines.

Monquest Teaching Evaluation surveys may be used by some of your academic staff this semester. They are administered by the Centre for Higher Education Quality (CHEQ) and may be completed in class with a facilitator or on-line through the my.monash portal. The data provided to lecturers is completely anonymous. Monquest surveys provide academic staff with evidence of the effectiveness of their teaching and identify areas for improvement. Individual Monquest reports are confidential, however, you can see the summary results of Monquest evaluations for 2006 at http://www.adm.monash.edu.au/cheq/evaluations/monquest/profiles/index.html

Unit staff - contact details

Unit leader

Dr Kevin Korb
Phone +61 3 990 55198
Fax +61 3 990 55157

Contact hours : Tue 3-4pm, Thr 12-1pm or by email appointment.

Lecturer(s) :

Teaching and learning method

2 lectures per week on AI theory, techniques and applications. Lisp tutorials and programming assignments will reinforce what is learned in lectures and readings.

Tutorial allocation

Tutorials are voluntary.

Communication, participation and feedback

Monash aims to provide a learning environment in which students receive a range of ongoing feedback throughout their studies. You will receive feedback on your work and progress in this unit. This may take the form of group feedback, individual feedback, peer feedback, self-comparison, verbal and written feedback, discussions (on line and in class) as well as more formal feedback related to assignment marks and grades. You are encouraged to draw on a variety of feedback to enhance your learning.

It is essential that you take action immediately if you realise that you have a problem that is affecting your study. Semesters are short, so we can help you best if you let us know as soon as problems arise. Regardless of whether the problem is related directly to your progress in the unit, if it is likely to interfere with your progress you should discuss it with your lecturer or a Community Service counsellor as soon as possible.

Unit Schedule

Week Topic Key dates
1 Introduction;  
2 Lisp  
3 Search  
4 Search and Games Assignment 1 due
Mid semester break
5 Lisp II  
6 Logic  
7 Defeasible Reasoning Assignment 2 due
8 Planning  
9 Bayesian Networks  
10 Machine Learning  
11 ANNs and Evolutionary Learning  
12 Bayesian Learning Assignment 3 due
13 Philosophy of AI  

Unit Resources

Prescribed text(s) and readings

Prescribed Reading

R. Russell and P. Norvig (2003). Artificial Intelligence: A Modern Approach, 2nd edition. Prentice Hall.

P. Graham (1996), ANSI Common Lisp. Prentice Hall.

Text books are available from the Monash University Book Shops. Availability from other suppliers cannot be assured. The Bookshop orders texts in specifically for this unit. You are advised to purchase your text book early.

Recommended text(s) and readings

Supplementary Reading

A Hodges (1992), Alan Turing: The Enigma. London: Vintage.

P McCorduck (1979), Machines Who Think. Freeman.

J Haugland (1985), Artificial Intelligence: The Very Idea. MIT.

M Boden (Ed.) (1990), The Philosophy of AI. Oxford.

Required software and/or hardware

CLISP. Available on Linux lab machines and for free download from GNU.

Equipment and consumables required or provided

Linux lab machine.

Study resources

Study resources we will provide for your study are:

  • Weekly detailed lecture notes outlining the learning objectives, discussion of the content, required readings and  exercises;
  • Exercises with sample solutions provided one to two weeks later;
  • Assignment specifications and sample solutions;
  • A sample examination and suggested solution;
  • Discussion groups;
  • This Unit Guide outlining the administrative information for the unit;
  • The unit web site on Blackboard, where resources outlined above will be made available.

Library access

The Monash University Library site contains details about borrowing rights and catalogue searching. To learn more about the library and the various resources available, please go to http://www.lib.monash.edu.au.  Be sure to obtain a copy of the Library Guide, and if necessary, the instructions for remote access from the library website.

Monash University Studies Online (MUSO)

All unit and lecture materials are available through MUSO (Monash University Studies Online). Blackboard is the primary application used to deliver your unit resources. Some units will be piloted in Moodle.

You can access MUSO and Blackboard via the portal (http://my.monash.edu.au).

Click on the Study and enrolment tab, then Blackboard under the MUSO learning systems.

In order for your Blackboard unit(s) to function correctly, your computer needs to be correctly configured.

For example :

  • Blackboard supported browser
  • Supported Java runtime environment

For more information, please visit


You can contact the MUSO Support by: Phone: (+61 3) 9903 1268

For further contact information including operational hours, please visit


Further information can be obtained from the MUSO support site:


If your unit is piloted in Moodle, you will see a link from your Blackboard unit to Moodle at http://moodle.med.monash.edu.au.
From the Faculty of Information Technology category, click on the link for your unit.


Unit assessment policy

Three assignments worth a total of 40% and a final exam  worth 60%.

The three assignments are programming assignments in Lisp, weighted 10%, 15% and 15% and tentatively due at the end of weeks: 4, 7 and 11.

The final exahm is 3hrs, closed-book during the exam period. Faculty policy dictates that to pass this unit, a student must obtain :

  • 40% or more in the unit's examination and
  • 40% or more in the unit's non-examination assessment
  • an overall unit mark of 50% or more
If a student does not achieve 40% or more in the unit examination or the unit non-examination assessment then a mark of no greater than 44-N will be recorded for the unit.

Assignment tasks

  • Assignment Task

    Title : Assignment 1

    Description :

    Lisp assignment

    Weighting : 10%

    Criteria for assessment :

    Completion of lisp problems. The specific tasks and marking criteria will be distributed at the appropriate time during the semester.

    Due date : 20 Mar

  • Assignment Task

    Title : Assignment 2

    Description :

    Search and/or game playing program.

    Weighting : 15%

    Criteria for assessment :

    Performance of program. The specific tasks and marking criteria will be distributed at the appropriate time during the semester.

    Due date : 18 April

  • Assignment Task

    Title : Assignment 3

    Description :

    Learning and decision-making program.

    Weighting : 15%

    Criteria for assessment :

    Performance of program. The specific tasks and marking criteria will be distributed at the appropriate time during the semester.

    Due date : 23 May


  • Examination

    Weighting : 60%

    Length : 3 hours

    Type ( open/closed book ) : Closed book

    Remarks ( optional - leave blank for none ) :

    Sample exams will be made available.

Assignment submission

Assignments will be submitted by [electronic/paper] submission to [enter submission URL/location] On-campus Students Submit the assignment to the [enter submission location] by [enter submission date], with the appropriate cover sheet correctly filled out and attached Off Campus (OCL) students [OCL only] Mail your assignment to the Off-Campus Learning Centre with the cover sheet attached. Singapore and Hong Kong Students [Gippsland only] Mail your assignment to the Distance Education Centre with the cover sheet attached. Do not email submissions. The due date is the date by which the submission must be received/the date by which the the submission is to be posted.

Assignment coversheets

Assignments will be submitted via Blackboard, with coversheets provided within Blackboard.

University and Faculty policy on assessment

Due dates and extensions

The due dates for the submission of assignments are given in the previous section. Please make every effort to submit work by the due dates. It is your responsibility to structure your study program around assignment deadlines, family, work and other commitments. Factors such as normal work pressures, vacations, etc. are seldom regarded as appropriate reasons for granting extensions. Students are advised to NOT assume that granting of an extension is a matter of course.

Requests for extensions must be made to the unit lecturer at your campus. You will be asked to forward original medical certificates in cases of illness, and may be asked to provide other forms of documentation where necessary. A copy of the email or other written communication granting an extension must be attached to the assignment submission.

Late assignment

Late assignments receive penalties up to two weeks, after whichlate submission is not allowed. A "hidden" penalty is that late assignmentsmay be marked and returned late.The complete list is (for "working days late"; weekends don't count)
  1. mark penalty for 1 days late: 1 pt
  2. mark penalty for 2 days late: 2 pt
  3. mark penalty for 3 days late: 3 pt
  4. mark penalty for 4 days late: 4 pt
  5. mark penalty for 5 days late: 8 pt
  6. mark penalty for 6 days late: 10 pt
  7. mark penalty for 7 days late: 12 pt
  8. mark penalty for 8 days late: 14 pt
  9. mark penalty for 9 days late: 16 pt
  10. mark penalty for 10 days late: 20 pt

Return dates

Students can expect assignments to be returned within two weeks of the submission date or after receipt, whichever is later.

Assessment for the unit as a whole is in accordance with the provisions of the Monash University Education Policy at http://www.policy.monash.edu/policy-bank/academic/education/assessment/

We will aim to have assignment results made available to you within two weeks after assignment receipt.

Plagiarism, cheating and collusion

Plagiarism and cheating are regarded as very serious offences. In cases where cheating  has been confirmed, students have been severely penalised, from losing all marks for an assignment, to facing disciplinary action at the Faculty level. While we would wish that all our students adhere to sound ethical conduct and honesty, I will ask you to acquaint yourself with Student Rights and Responsibilities (http://www.infotech.monash.edu.au/about/committees-groups/facboard/policies/studrights.html) and the Faculty regulations that apply to students detected cheating as these will be applied in all detected cases.

In this University, cheating means seeking to obtain an unfair advantage in any examination or any other written or practical work to be submitted or completed by a student for assessment. It includes the use, or attempted use, of any means to gain an unfair advantage for any assessable work in the unit, where the means is contrary to the instructions for such work. 

When you submit an individual assessment item, such as a program, a report, an essay, assignment or other piece of work, under your name you are understood to be stating that this is your own work. If a submission is identical with, or similar to, someone else's work, an assumption of cheating may arise. If you are planning on working with another student, it is acceptable to undertake research together, and discuss problems, but it is not acceptable to jointly develop or share solutions unless this is specified by your lecturer. 

Intentionally providing students with your solutions to assignments is classified as "assisting to cheat" and students who do this may be subject to disciplinary action. You should take reasonable care that your solution is not accidentally or deliberately obtained by other students. For example, do not leave copies of your work in progress on the hard drives of shared computers, and do not show your work to other students. If you believe this may have happened, please be sure to contact your lecturer as soon as possible.

Cheating also includes taking into an examination any material contrary to the regulations, including any bilingual dictionary, whether or not with the intention of using it to obtain an advantage.

Plagiarism involves the false representation of another person's ideas, or findings, as your own by either copying material or paraphrasing without citing sources. It is both professional and ethical to reference clearly the ideas and information that you have used from another writer. If the source is not identified, then you have plagiarised work of the other author. Plagiarism is a form of dishonesty that is insulting to the reader and grossly unfair to your student colleagues.

Register of counselling about plagiarism

The university requires faculties to keep a simple and confidential register to record counselling to students about plagiarism (e.g. warnings). The register is accessible to Associate Deans Teaching (or nominees) and, where requested, students concerned have access to their own details in the register. The register is to serve as a record of counselling about the nature of plagiarism, not as a record of allegations; and no provision of appeals in relation to the register is necessary or applicable.

Non-discriminatory language

The Faculty of Information Technology is committed to the use of non-discriminatory language in all forms of communication. Discriminatory language is that which refers in abusive terms to gender, race, age, sexual orientation, citizenship or nationality, ethnic or language background, physical or mental ability, or political or religious views, or which stereotypes groups in an adverse manner. This is not meant to preclude or inhibit legitimate academic debate on any issue; however, the language used in such debate should be non-discriminatory and sensitive to these matters. It is important to avoid the use of discriminatory language in your communications and written work. The most common form of discriminatory language in academic work tends to be in the area of gender inclusiveness. You are, therefore, requested to check for this and to ensure your work and communications are non-discriminatory in all respects.

Students with disabilities

Students with disabilities that may disadvantage them in assessment should seek advice from one of the following before completing assessment tasks and examinations:

Deferred assessment and special consideration

Deferred assessment (not to be confused with an extension for submission of an assignment) may be granted in cases of extenuating personal circumstances such as serious personal illness or bereavement. Information and forms for Special Consideration and deferred assessment applications are available at http://www.monash.edu.au/exams/special-consideration.html. Contact the Faculty's Student Services staff at your campus for further information and advice.