KNOWLEDGE BASED SYSTEMS & ARTIFICIAL INTELLIGENCE - 2023/4
Module code: ENGM075
Knowledge is the most critical part of any decision making process, being it the process of design, management or general business. Last several decades have witnessed numerous attempts to present knowledge in the form processable by computers, starting with descriptive logic, through the production rules and ending with ontologies as the last and the latest form known today, the process known as artificial intelligence. The module will help students in consolidating all of these technologies and using them in supporting decision making in the area of student’ interest, primarily in the process of design for processing industry.
Chemistry and Chemical Engineering
CECELJA Franjo (Chst Chm Eng)
Number of Credits: 15
ECTS Credits: 7.5
Framework: FHEQ Level 7
JACs code: I400
Module cap (Maximum number of students): N/A
Overall student workload
Independent Learning Hours: 95
Lecture Hours: 22
Tutorial Hours: 11
Guided Learning: 11
Captured Content: 11
Prerequisites / Co-requisites
Process design: conceptual design in Chemical Engineering, modelling the conceptual design process, design process representation
Decisions as the building-blocks of the design process: design intent, artefact and rationale, decision rationale management systems, use of decision rationale
Decision support systems: an overview, a historical perspective of design support systems in process engineering, knowledge-based decision support
Knowledge representation and management: introduction and scope, semantic networks as knowledge representation formalism, production rules as knowledge representation formalism, ontologies as a knowledge representation formalism, overview of languages to express ontologies, d development and use of rules, development and use of ontologies, application to decision support
|Assessment type||Unit of assessment||Weighting|
|Oral exam or presentation||VIVA VOCE||60|
The assessment strategy is designed to provide students with the opportunity to demonstrate:
- Understanding of scientific principles, methodologies and mathematical methods associated with decision making and knowledge representation, as well as the ability to formulate and solve particular engineering problem in the final examination. The coursework tests and amplifies awareness and ability to formulate and solve a practical problem in engineering.
Thus, the summative assessment for this module consists of:
- Coursework 1: design of a rule based expert system, (10 hrs) – 20% (LOs 1, 3, 4, 7, 8)
- Coursework 2: design of an ontology for engineering application, (10 hrs) – 20% (LOs 1, 3, 6, 7, 8)
- Examination, 2 hrs – 60% (LOs 1, 2, 4, 5, 6, 7)
Formative assessment and feedback
- Formative verbal feedback is given during laboratory experiments
- Formative feedback on coursework is given verbally and available on SurreyLearn to provide feedback on understanding of optimisation and decision making process and respective problem formulation and solution.
- The aims of the module are to present the current understanding of the development of decision support systems and knowledge management systems. It will be used as a common thread and example of the design of chemical processes, focusing on the use of artificial intelligence techniques such as knowledge representation, knowledge-based decision support and agents technology. Students will be provided with state-of-the-art versions or demonstrations of decision support systems.
|001||Represent a design process as a space of states.||KP|
|002||Understand the relationship between design artefact, design intent and design rationale.||K|
|003||Record design rationale in a systematic way and use those records during re-design and retrofit.||CP|
|004||Design a decision support system using production rules.||K|
|005||Identify the representation and management techniques appropriate for a particular problem.||KP|
|006||Build an ontology in a specific area of application.||KT|
|007||Apply an agent-based architecture to the solution of a problem||K|
|008||Apply synthesis of data, information and knowledge, and reviewing and assessing problems and solutions by using standard computer tools such as spreadsheets and text processors.||KT|
C - Cognitive/analytical
K - Subject knowledge
T - Transferable skills
P - Professional/Practical skills
Methods of Teaching / Learning
The learning and teaching strategy is designed to:
Introduce principles of knowledge management and knowledge representation, as well as decision making process, and their implementation and use through theory and worked examples. This is mainly delivered through lectures and laboratory experiments using CLIPS and ontology editors on independently worked out examples.
The learning and teaching methods include:
- 2 hours lecture per week x 11 weeks
- 1 hour tutorial x 11 weeks
- 2 hours revision lectures
Indicated Lecture Hours (which may also include seminars, tutorials, workshops and other contact time) are approximate and may include in-class tests where one or more of these are an assessment on the module. In-class tests are scheduled/organised separately to taught content and will be published on to student personal timetables, where they apply to taken modules, as soon as they are finalised by central administration. This will usually be after the initial publication of the teaching timetable for the relevant semester.
Upon accessing the reading list, please search for the module using the module code: ENGM075
In line with Surrey’s Curriculum Framework, we are committed to developing graduates with strengths in Employability, Digital Capabilities, Global and Cultural Capabilities, Sustainability and Resourcefulness and Resilience. This module contributes to the five pillars in the following ways:
Most of tutorial sessions and the coursework are designed and positioned here such to provide students with and expose them to a more authentic (real world) problem solving experience towards at the end of their programme. Through this students will gain experience of report writing for specific audiences/stakeholders, and ability to demonstrate capability in problem solving directly applicable to a wide range of sectors, which could be cited in interviews and applications to show students’ experience of applying scholarly knowledge to another sector.
Significant level of digital skill is a clear output of this module which students gain through a direct and indirect exposure:
- Digital capability gained through a direct exposure includes the use of CLIPS software package for rule based approact to knowledge modelling and Protégé software package for semantic base knowledge modelling and programming. During every tutorial students use these software packages in computer laboratory gaining experience in running the software but also in using computers in more general terms;
- Digital capability gained through an indirect exposure includes teaching materials and key content available in multimedia forms through the Virtual Learning Environment Surreylearn.
Global and Cultural Capabilities
Engineering in general and Decision Making in particular are global synonyms and the tools and skills used on this module can be used internationally and multiculturally. Students learn about generic engineering and professional code of conduct and the importance of respect in teamwork. Students learn to work together in groups with other students from different backgrounds to solve a problem. This module allows students to develop skills that will allow them to develop applications with global reach and collaborate with their peers around the world.
Students will complete this module with social, ethical and contextually aware knowledge. This module has gender inequality (in the broadest and most inclusive use of the term) at its core, aligned with the UN’s gender equality sustainability goal. It also seeks to ensure community sustainability through the knowledge, skills and awareness students will have upon completion of the module.
Resourcefulness and resilience
This module directly contributes to the educational elements of resourcefulness and resilience as students are honing their autonomous learning to a sophisticated and advanced level. Throughout the module, from concept of formulating the problem to implementation of tools and finding solutions, students will be highly independent, yet supported by their supervisor in the course of tutorials. Students will gain particular skills in informed decision making as this is the core nature of the module They will have to problem solve, navigate ethical considerations and
consider their arguments and findings in context. Within a network of support, students will further develop the extent to which they are independent and resourceful learners with a great deal of confidence in conducting and leading independent work towards solution.
Programmes this module appears in
|Process Systems Engineering MSc||2||Optional||A weighted aggregate mark of 50% is required to pass the module|
|Information and Process Systems Engineering MSc||2||Compulsory||A weighted aggregate mark of 50% is required to pass the module|
|Renewable Energy Systems Engineering MSc||2||Optional||A weighted aggregate mark of 50% is required to pass the module|
|Petroleum Refining Systems Engineering MSc||2||Optional||A weighted aggregate mark of 50% is required to pass the module|
Please note that the information detailed within this record is accurate at the time of publishing and may be subject to change. This record contains information for the most up to date version of the programme / module for the 2023/4 academic year.