Part A: Course Overview

Course Title: Intelligent Decision Making

Credit Points: 12.00

Terms

Course Code

Campus

Career

School

Learning Mode

Teaching Period(s)

COSC2780

City Campus

Postgraduate

171H School of Science

Face-to-Face

 Sem 1 2021

COSC2780

City Campus

Postgraduate

175H Computing Technologies

Face-to-Face

 Sem 1 2022,
Sem 1 2023,
Sem 1 2024,
Sem 1 2025

COSC3009

RMIT University Vietnam

Postgraduate

175H Computing Technologies

Face-to-Face

 Viet3 2023,
Viet3 2024

Course Coordinator: Sebastian Sardina

Course Coordinator Phone: +61 3 9925 9824

Course Coordinator Email: sebastian.sardina@rmit.edu.au

Course Coordinator Location: 014.07.007D

Course Coordinator Availability: By appointment, by email


Pre-requisite Courses and Assumed Knowledge and Capabilities

Enforced Pre-Requisite Courses

Successful completion of the following course/s:

Note: it is a condition of enrolment at RMIT that you accept responsibility for ensuring that you have completed the prerequisite/s and agree to concurrently enrol in co-requisite courses before enrolling in a course.

For information go to RMIT Course Requisites webpage.

 

If you have completed prior studies at RMIT or another institution that developed the skills and knowledge covered in the above course/s you may be eligible to apply for credit transfer.

Alternatively, if you have prior relevant work experience that developed the skills and knowledge covered in the above course/s you may be eligible for recognition of prior learning.

Please follow the link for further information on how to apply for credit for prior study or experience.

 

Assumed Knowledge:

You should have knowledge of programming and basic discrete mathematics (e.g., sets, functions, relations).


Course Description

This course covers the foundations and practical aspects in the area of Artificial Intelligence for building systems that are able to make intelligent decisions in knowledge-intensive settings. From an Artificial Intelligence perspective, such systems are built to be able understand their environment, reason about it, and build and execute plans or strategies that aim to bring about their goals. Topics are drawn from the field of advanced artificial intelligence including knowledge representation, automated planning, agent-oriented programming, reinforcement learning, reactive synthesis, reasoning about action and change, and cognitive robotics. The course covers both theoretical and practical aspects, including building concrete systems with state-of-the-art tools. Being a course in a rapidly advanced area of active research, the particular approaches and systems covered may vary on each course edition. 


Objectives/Learning Outcomes/Capability Development

Program Learning Outcomes

This course contributes to the program learning outcomes for the following program(s):

MC271 - Master of Artificial Intelligence
MC271VRI - Master of Artificial Intelligence (Vietnam Saigon)

PLO 1    Enabling Knowledge
PLO 2    Critical Analysis
PLO 3    Problem Solving

MC208 - Master of Information Technology

PLO 1    Enabling Knowledge
PLO 2    Critical Analysis
PLO 3    Problem Solving
PLO 4    Communication
PLO 6    Teamwork

For more information on the program learning outcomes for your program, please see the program guide.


Upon successful completion of this course you should be able to:

  1. Understand the existing AI approaches to complex action decision, and be able to judge when and how to use them;
  2. Understand the role of knowledge representation in intelligent decision making and the various approaches depending on context;
  3. Use state of the art technologies for complex decision making, like agent and planning systems, decision theoretic solvers; and knowledge-base systems.
  4. Apply critical analysis and problem solving skills to extend and enhance existing techniques;
  5. Have ability to seek and read scientific literature in a critical manner;
  6. Be able to communicate effectively scientific knowledge and cutting-edge techniques, both orally and in writing.


Overview of Learning Activities

The learning activities included in this course are:

  • classes run by academic staff, to introduce you to the key concepts, techniques, and tools required for successful completion of the assessments and programming tasks; 
  • face-to-face workshops and/or individual/group discussions focused on projects and problem solving, providing feedback on progress and understanding, and used to discuss technical issues; 
  • online forums participation (among students and teaching staff ) to exchange information and receive help and support to resolve technical or conceptual questions; 
  • assignment/project deliverables, as described in Overview of Assessment and Assessment Tasks, designed to develop and demonstrate the practical aspects of the learning outcomes; and 
  • private and group study, for working through readings and gaining practice at solving conceptual and technical problems. Private study is fundamental to consolidate your understanding of the theory and practice. 


Overview of Learning Resources

The course is supported by various online tools, such as the  Canvas learning management system and/or Google-based systems, which provide specific learning resources. See also the RMIT Library Guide at http://rmit.libguides.com/compsci.


Overview of Assessment

This course has no hurdle requirements.

Assessment Tasks

Assessment Task 1: Assignments
Weighting: 30%
This assessment task supports CLOs 2, 3, 4

Assessment Task 2: Project
Weighting: 50%
This assessment task supports CLOs 1, 2, 3, 4, 6

Assessment Task 3: Timed and Timetabled Assessments
Weighting: 20%
This assessment task supports CLOs 1, 2, 5, 6

If you have a long-term medical condition and/or disability it may be possible to negotiate to vary aspects of the learning or assessment methods. You can contact the program coordinator or Equitable Learning Services if you would like to find out more.