This module introduces students to the fundamental concepts of intelligent agents and multi-agent systems, emphasizing their applications in various domains. It covers the design and challenges of intelligent agents, including reasoning, reactive, hybrid, and layered agent architectures, and explores the complexities of multi-agent societies, focusing on cooperation, coordination, and competition among agents. Students will gain hands-on experience with contemporary frameworks for implementing agent systems. The module prepares students for real-world applications and distributed computing by combining theoretical foundations with practical skills in agent-based modeling and multi-agent interaction.
A Demonstrate understanding of the concept of an agent, differentiate agents from other software paradigms (such as objects), and identify the types of applications suited for agent-based solutions. B Critically analyze the challenges and key issues in constructing intelligent, autonomous agents, and evaluate the main approaches used in developing such agents. C Evaluate the design of multi-agent societies, focusing on cooperation mechanisms and interaction types, and assess their effectiveness in solving complex problems within agent-based systems. D Identify key application areas for agent-based solutions, and use a contemporary agent development platform to design, develop, and implement a functional agent-based system.
The teaching philosophy of the module follows very much the philosophy of Syntegrative Education. This module will be delivered through a combination of lectures, group discussions, case studies, and hands-on practical exercises etc. Lectures and group discussions are conducted using the Problem Based Learning paradigm focusing on student-centered learning, where they develop critical thinking and problem-solving skills to address open-ended problems that lacks a straightforward solution This module is taught with an emphasis on student learning through practice and by projects, facilitated by a module leader, and where appropriate, industrial mentors. Students can identify particular areas of learning needs or interests according to the available project(s). They will conduct independent research to gather information and resources to better define the problem. Progress towards the learning outcomes will be facilitated and monitored, where students are guided to progressively address the given problem through tasks. Independent learning will form an important aspect of the educational activities in this module. Case studies will be used to provide students with real-world examples of how the concepts and techniques covered in this module can be applied. Lab/Practical sessions will allow students to apply the techniques and tools acquired to solve real-world industry focused problems. This module will leverage generative AI to enhance course content and teaching methods in line with the learning outcomes. By integrating advanced AI technologies, we aim to improve the efficiency of teaching and interaction, while fostering greater student autonomy and flexibility in learning.