Course Title: Intelligent Decision Making

Part A: Course Overview

Course Title: Intelligent Decision Making

Credit Points: 12.00

Course Coordinator: Professor Sebastian Sardina

Course Coordinator Phone: +61 3 9925 9824

Course Coordinator Email:

Course Coordinator Location: 14.08.07D

Course Coordinator Availability: by appointment

Pre-requisite Courses and Assumed Knowledge and Capabilities

Enforced Prerequisite: COSC1127 Artificial Intelligence 

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 complex and dynamic environments (e.g., autonomous robots, smart house, personal assistants, smart process management systems). From an Artificial Intelligence perspective, such systems are built to be able to perceive and understand their environment, reason about it, and build and execute plans 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

The course is a program option course, however, will contribute to following program learning outcomes.

PLO 1: Enabling Knowledge 

  • Demonstrate mastery of a body of knowledge that includes recent developments in Artificial Intelligence, Computer Science and information technology; 
  • Understand and use appropriate and relevant, fundamental and applied AI knowledge, methodologies and modern computational tools; 
  • Recognise and use research principles and methods applicable to Artificial Intelligence.   

PLO 2: Critical Analysis 

  • Analyse and model complex requirements and constraints for the purpose of designing and implementing software artefacts and IT systems; 
  • Evaluate and compare designs of software artefacts and IT systems on the basis of organisational and user requirements; 
  • Bring together and flexibly apply knowledge to characterise, analyse and solve a wide range of AI problems.

PLO 3: Problem Solving 

  • Design and implement software solutions that accommodate specified requirements and constraints, based on analysis or modelling or requirements specification; 
  • Apply an understanding of the balance between the complexity / accuracy of the Artificial techniques used and the timeliness of the delivery of the solution. 

The objective of this course is to develop an understanding of and gain experience with AI techniques and tools for building software that is able to make intelligent decisions autonomously. Upon successful completion of this course you should be able to:

  • CLO 1: understand the existing AI approaches to complex action decision, and be able to judge when and how to use them; 
  • CLO 2: understand the role of knowledge representation in intelligent decision making and the various approaches depending on context; 
  • CLO 3: use state of the art technologies for complex decision making, like agent and planning systems, decision theoretic solvers; and knowledge-base systems. 
  • CLO 4: apply critical analysis and problem solving skills to extend and enhance existing techniques; 
  • CLO 5: have ability to seek and read scientific literature in a critical manner; 
  • CLO 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 tutorials and/or labs and/or 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 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 will be supported via RMIT's online Learning Management System (LMS) which will provide the specific learning resources, including papers, videos, software to be used, etc. 

Overview of Assessment

☒This course has no hurdle requirements.
☐ All hurdle requirements for this course are indicated clearly in the assessment regime that follows, against the relevant assessment task(s) and all have been approved by the College Deputy Pro Vice-Chancellor (Learning & Teaching).

The assessment for this course mostly comprises of practical-oriented work involving the development and analysis of agent systems that perform sequential decision making in complex domains. Active oral and written communication skills about the technical material will also be assess during the course. This course has no hurdle requirements. 

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 

Assessment Component 3:Participation and Presentations Weighting 20%  
This assessment task supports CLOs 1, 2, 5, 6