Part A: Course Overview

Course Title: Intelligent Decision Making

Credit Points: 12.00

Terms

Course Code

Campus

Career

School

Learning Mode

Teaching Period(s)

COSC2973

City Campus

Undergraduate

175H Computing Technologies

Face-to-Face

 Sem 1 2023,
Sem 1 2024,
Sem 1 2025

Course Coordinator: Professor Sebastian Sardina

Course Coordinator Phone: +61 3 9925 9824

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

Course Coordinator Location: 14.08.07D

Course Coordinator Availability: by appointment


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.

Assumed Knowledge

In addition, 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 is an option course so it is not required to contribute to the development of program learning outcomes (PLOs) though it may assist your achievement of several PLOs.

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

The Assessments for this course mostly comprise practical-oriented work, but will also cover theoretical and conceptual aspects. Active oral and written communication skills about the technical material will also be assessed during the course. The course may have compulsory in-person attendance requirements for some teaching and assessment activities.

This course has no hurdle requirements. 

Assessments:

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

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

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

*This assessment is a timed and timetabled assessment that students must attend on campus except for international students who are outside Australia.

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.