KNOWLEDGE BASED SYSTEMS & ARTIFICIAL INTELLIGENCE - 2018/9

Module code: ENGM075

Module Overview

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.

Module provider

Chemical and Process Engineering

Module Leader

CECELJA F Dr (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

Module Availability

Semester 2

Prerequisites / Co-requisites

None

Module content

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 pattern

Assessment type Unit of assessment Weighting
Examination EXAMINATION 2 HOURS 60
Coursework COURSEWORK 1 20
Coursework COURSEWORK 2 20

Alternative Assessment

N/A

Assessment Strategy

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.

Module aims

  • 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.

Learning outcomes

Attributes Developed
1 Represent a design process as a space of states. KP
2 Understand the relationship between design artefact, design intent and design rationale. K
3 Record design rationale in a systematic way and use those records during re-design and retrofit. CP
4 Design a decision support system using production rules. K
5 Identify the representation and management techniques appropriate for a particular problem. KP
6 Build an ontology in a specific area of application. KT
7 Apply an agent-based architecture to the solution of a problem K
8 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

Attributes Developed

C - Cognitive/analytical

K - Subject knowledge

T - Transferable skills

P - Professional/Practical skills

Overall student workload

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

Reading list for KNOWLEDGE BASED SYSTEMS & ARTIFICIAL INTELLIGENCE : http://aspire.surrey.ac.uk/modules/engm075

Programmes this module appears in

Programme Semester Classification Qualifying conditions
Information and Process Systems Engineering MSc 2 Compulsory 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
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 2018/9 academic year.