KNOWLEDGE BASED SYSTEMS & ARTIFICIAL INTELLIGENCE - 2020/1
Module code: ENGM075
In light of the Covid-19 pandemic, and in a departure from previous academic years and previously published information, the University has had to change the delivery (and in some cases the content) of its programmes, together with certain University services and facilities for the academic year 2020/21.
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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.
Chemical and Process Engineering
CECELJA Franjo (Chm Proc Eng)
Number of Credits: 15
ECTS Credits: 7.5
Framework: FHEQ Level 7
JACs code: I400
Module cap (Maximum number of students): N/A
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|
|Examination||EXAMINATION 2 HOURS||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
Overall student workload
Independent Study Hours: 117
Lecture Hours: 22
Tutorial Hours: 11
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.
Reading list for KNOWLEDGE BASED SYSTEMS & ARTIFICIAL INTELLIGENCE : http://aspire.surrey.ac.uk/modules/engm075
Programmes this module appears in
|Petroleum Refining 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|
|Process 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 2020/1 academic year.