Course Title: Intelligent Decision Making
Part A: Course Overview
Course Title: Intelligent Decision Making
Credit Points: 12.00
Please note that this course may have compulsory in-person attendance requirements for some teaching activities.
To participate in any RMIT course in-person activities or assessment, you will need to comply with RMIT vaccination requirements which are applicable during the duration of the course. This RMIT requirement includes being vaccinated against COVID-19 or holding a valid medical exemption.
Please read this RMIT Enrolment Procedure as it has important information regarding COVID vaccination and your study at RMIT: https://policies.rmit.edu.au/document/view.php?id=209.
Please read the Student website for additional requirements of in-person attendance: https://www.rmit.edu.au/covid/coming-to-campus
Please check your Canvas course shell closer to when the course starts to see if this course requires mandatory in-person attendance. The delivery method of the course might have to change quickly in response to changes in the local state/national directive regarding in-person course attendance.
Course Coordinator: Professor Sebastian Sardina
Course Coordinator Phone: +61 3 9925 9824
Course Coordinator Email: email@example.com
Course Coordinator Location: 14.08.07D
Course Coordinator Availability: by appointment
Pre-requisite Courses and Assumed Knowledge and Capabilities
Required Prior Study:
You should have satisfactorily completed COSC1127 Artificial Intelligence before you commence this course
Alternatively, you may be able to demonstrate the required skills and knowledge before you start this course.
Contact your course coordinator if you think you may be eligible for recognition of prior learning.
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
The course is a program option course, however, will contribute to following program learning outcomes:
PLO1: Knowledge - Apply a broad and coherent set of knowledge and skills for developing user-centric computing solutions for contemporary societal challenges.
PLO2: Problem Solving - Apply systematic problem solving and decision-making methodologies to identify, design and implement computing solutions to real world problems, demonstrating the ability to work independently to self-manage processes and projects.
PLO3: Cognitive and Technical Skill - Critically analyse and evaluate user requirements and design systems employing software development tools, techniques, and emerging technologies.
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 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 will be supported via RMIT's online Learning Management System (LMS) and other tools and systems. Specific learning resources, including papers, videos, software to be used, etc. will be provided.
You are encouraged to bring your laptops and use the freely available software to conduct the laboratories.
Overview of Assessment
The assessment for this course mostly comprises of practical-oriented work. Active oral and written communication skills about the technical material will also be assessed during the course.
This course has no hurdle requirements.
Assessment Component 1: Assignments
This assessment task supports CLOs 2, 3, 4
Assessment Component 2: Project
This assessment task supports CLOs 1, 2, 3, 4, 6
Assessment Component 3: Participation and Presentations
This assessment task supports CLOs 1, 2, 5, 6