Autonomous and Autonomic Intelligent Systems

Please note

This document only provides information for the academic year selected and does not form part of the student contract

School:

School of Computing and Engineering

Credit Rating:

Level (including FHEQ):

M (FHEQ Level 7)

Graded or Non Graded:

Graded

Version Valid From:

2022-09-01

Module Leader:

Sangeet Saha

Version Number

2022.01

Learning Methods

Practical Classes and Demonstrations

Lecture

Guided Independent Study

Synopsis

Autonomous systems are intelligent systems that can act independently to accomplish goals based on their knowledge and understanding of their environment and the tasks they have to complete. This module aims to cover the background and requirements for intelligent systems autonomy in a wide range of applications, taken from a … For more content click the Read More button below.

Autonomous systems are intelligent systems that can act independently to accomplish goals based on their knowledge and understanding of their environment and the tasks they have to complete. This module aims to cover the background and requirements for intelligent systems autonomy in a wide range of applications, taken from a computer science and software-oriented viewpoint. As well as the technical challenges of system autonomy, you’ll get the opportunity to study ethical and legal issues, and human factors implications.

Learning Strategy

Lectures will present the theory and basic concepts underlying autonomy in embedded, cyber-physical and software systems. A number of case studies will be used drawing from ongoing research, illustrating the theory and technology e.g. in transport management and autonomous vehicles.
To support the teaching, practical classes will allow the students to gain experience of technology used in embedding autonomy into systems. In particular, the students will have hands on use of humanoid robots to investigate deployment of intelligent navigational and planning software to real artefacts.
The University’s Virtual Learning Environment will be used to support and supplement lectures and individual students and group requirements.

Outline Syllabus

• Definitions of autonomy and autonomic properties• Architectures for autonomous and autonomic systems• Introduction to cyber-physical systems• Reactive autonomy: sensor processing, data fusion • Reactive autonomy: action execution and reinforcement learning• Introduction to intelligent systems: reasoning and planning• Distributed intelligence: agent-based systems• Design autonomy: goal oriented behaviour• Design autonomy: environment … For more content click the Read More button below.

• Definitions of autonomy and autonomic properties
• Architectures for autonomous and autonomic systems
• Introduction to cyber-physical systems
• Reactive autonomy: sensor processing, data fusion
• Reactive autonomy: action execution and reinforcement learning
• Introduction to intelligent systems: reasoning and planning
• Distributed intelligence: agent-based systems
• Design autonomy: goal oriented behaviour
• Design autonomy: environment representation and self-awareness
• Design autonomy: semantic service architecture
• Legal issues with autonomy; trust and institutional barriers to take-up
• Human interfaces and human behaviour implications
• Key research challenges

Learning Outcomes

On successful completion of this module students will

Critically evaluate autonomous and autonomic systems, their architectures and implementation challenges.

Analyse current practices in embodying systems with autonomy in relevant application areas, and related ethical and human-related issues.

Use a range of established techniques or algorithmic methods which implement aspects of autonomic behaviours, in reactive or design autonomy.

Reason with and analyse declarative structures used as logical building blocks of design autonomy.

Formative Assessment

Assessment 1: Other

Summative Assessment

Assessment 1: Portfolio

Description:Portfolio produced individually resulting from answering specific questions on the deployment of autonomous and/or autonomic systems to a particular sector or application, e.g. space craft, sub aqua craft, road transport management, humanoid robots, cars, aircraft, public transport etc. This will include an investigation of the technical and non-technical (legal, trust, institutional, human interface) related issues.

Assessment Weight:40

Assessment Length:12 Hours (Typical Effort)

Module Learning Outcomes:MLO1, MLO2

Individual or Group:Individual

Final Assessment Component:No

Marked Anonymously:No

Eligible for Tutor Reassessment:Yes

Assessment 2: Portfolio

Description:Portfolio produced individually on the design and development of an addition to an existing autonomous system using specified software tools, defending the chosen design and critically evaluating the appropriateness of the developed system.

Assessment Weight:60

Assessment Length:18 Hours (Typical Effort)

Module Learning Outcomes:MLO1, MLO3, MLO4

Individual or Group:Individual

Final Assessment Component:Yes

Marked Anonymously:No

Eligible for Tutor Reassessment:Yes

Assessment Criteria

Task 1• Demonstration of a deep knowledge about the application of autonomous systems ideas to the chosen investigated application, obtained through a literature review; and a critical appreciation of the advantages, disadvantages and challenges of these advanced approaches. • A clear understanding and explanation of the non-technical aspects (societal, institutional, … For more content click the Read More button below. Task 2• Quality of design criteria, appropriateness and presentation of solution. Complexity and correctness of the solution.• Critical understanding of the advantages and limitations of the overall approach and the function that the student has worked on, as evidenced by the supporting report.

Task 1
• Demonstration of a deep knowledge about the application of autonomous systems ideas to the chosen investigated application, obtained through a literature review; and a critical appreciation of the advantages, disadvantages and challenges of these advanced approaches.
• A clear understanding and explanation of the non-technical aspects (societal, institutional, human Interface, human behavioural change etc) as well as the technical challenges (e.g. verification and validation) of the implementation and use of autonomous/autonomic systems. Reports will be evaluated on clarity, depth, cogency, use of literature and presentation.

Task 2
• Quality of design criteria, appropriateness and presentation of solution. Complexity and correctness of the solution.
• Critical understanding of the advantages and limitations of the overall approach and the function that the student has worked on, as evidenced by the supporting report.

Course and Module Catalogue