Download Knowledge Based Systems

The Further Education and Training Awards Council (FETAC)
was set up as a statutory body on 11 June 2001
by the Minister for Education and Science.
Under the Qualifications (Education & Training) Act, 1999,
FETAC now has responsibility for making awards
previously made by NCVA.
Module Descriptor
Knowledge Based Systems
Level 6
C30142
www.fetac.ie
1
Title
Knowledge Based Systems
2
Code
C30142
3
Level
6
4
Value
2 credits
5
Purpose
This module has been designed to enable the learner to acquire
the programming concepts and techniques associated with
Intelligent Knowledge Based environments. The module is
language independent and includes only those concepts that are
unique to both the declarative and procedural style of
programming.
This is an elective module on the Advanced Certificate in
Networks and Software Systems at Level 6.
6
7
8
Preferred
Entry Level
Special
Requirements
Level 5 Certificate in or equivalent.
The learner should have successfully completed FETAC
Computer Architecture and Systems (C20012) or
Information Systems (B20015) or equivalent.
General Aims
This module aims to enable the learner to:
8.1
understand the need for a coherent approach to logic
programming
8.2
demonstrate an understanding of the concepts of logical
program design
8.3
develop good programming techniques and appreciate the need
for a deliberated approach to logic program design
8.4
appreciate the problems inherent in interpreting natural language
input to a computer
8.5
understand that programmable artificial intelligence focuses on
activities that cannot be treated at a simple algorithmic level
8.6
code and test a series of short structured logic problems
8.7
write and test a specific logic problem.
1
9
Units
Unit 1
Unit 2
Unit 3
Unit 4
10
Natural Language and Programmable Logic
Data Structures, Data Variables and Semantic Logic
Knowledge Based Systems and Artificial Intelligence
Intelligent Knowledge Based Systems and Expert Systems
Specific Learning
Outcomes
Unit 1
Natural Language and Programmable Logic
The learner should be able to:
10.1.1
specify the factors for and against using natural language in a
knowledge based system
10.1.2
identify logic languages as being rules-based with built in
pattern matching and backtracking facilities
10.1.3
recognize that knowledge and metaknowledge can interact with
strategic rules to help solve logic problems
10.1.4
list the criteria involved in good logic programming
10.1.5
explain what are goals
10.1.6
explain the term backtracking and its use in logic programs
10.1.7
generate and implement a set of logic rules as facts
10.1.8
generate and implement a set of logical predicates
10.1.9
identify predicates as a series of factual arguments
10.1.10
generate and implement a set logical clauses
10.1.11
identify clauses as a set of rules and predicates
10.1.12
specify predicates, rules, domains and goals as elements of a
logic program
10.1.13
describe what are instantiated variables.
Unit 2
Data Structures, Data Variables and Semantic Logic
The learner should be able to:
10.2.1
specify the requirements of a logic program
10.2.2
understand the need for a procedural approach to logic
programming
10.2.3
distinguish between backward and forward chaining
10.2.4
code a procedure as a set of clauses about the same relation
2
10.2.5
write a program that will execute the procedure as a set of goals
or objectives
10.2.6
define a list of factual rules as facts
10.2.7
code and initiate a list of elements with associate members
10.2.8
identify the importance of the head and tail of a structured list
10.2.9
write a short program that will execute a structured list
10.2.10
perform a search on a structured list using a member program
10.2.11
add and delete elements of a structured list
10.2.12
perform logical checks on a structured list
10.2.13
sort a structured list using a quick sort, bubble sort or insertion
sort algorithm
10.2.14
demonstrate the use of debugging to trace missed goals
10.2.15
implement the fail goal using negation by failure
10.2.16
identify unnecessary backtracking in a logic program
10.2.17
eliminate unnecessary backtracking using a cut procedure.
Unit 3
Knowledge Based Systems and Artificial Intelligence
The learner should be able to:
10.3.1
explain why artificial intelligence looks at all activities which
cannot be treated at a simple, standard, algorithmic level
10.3.2
explain how an artificial intelligence system can bypass the
normal linguistic process
10.3.3
outline with examples how the Turing Test of machine
intelligence works e.g., ELIZA, ALICE, GENERAL
PROBLEM SOLVER, Frudes
10.3.4
explain the term Mixed Initiative System (MIS)
10.3.5
list examples of artificial intelligence as mailbox, handwriting
recognition, multi modality
10.3.6
identify artificial intelligence languages as both procedural and
declarative.
Unit 4
Intelligent Knowledge Based Systems and Expert Systems
The learner should be able to:
10.4.1
illustrate and outline the components of a general expert system
10.4.2
describe the architecture of an intelligent knowledge based
system (IKBS)
3
11
10.4.3
distinguish between an intelligent knowledge based system and
an informational database system
10.4.4
define a knowledge based system as a component of an expert
system
10.4.5
describe the merits of input devices in a knowledge based
system such as mouse, trackball, touch-screen, light pen
10.4.6
evaluate a short case study of a suitable working expert system
10.4.7
explain how object oriented programming (OOP) can be a useful
tool in developing an intelligent knowledge based system
10.4.8
describe the role of an inference engine
10.4.9
explain what is an intelligent front end processor (IFE)
10.4.10
list the tasks that an IFE processor can perform.
Assessment
Summary
Project
Written Examination
11.1
Technique
Details
Project
Code and test a specified logic program based on the short
assignments developed in the specific learning outcomes.
11.2
Technique
Written Examination
Format
6 structured questions based on all the units.
5 Questions to be answered.
12
Performance
Criteria
12.1
Project
12.2
Written
Examination
13
Grading
50%
50%
The performance criteria for each component of the project
should be detailed in the accompanying Individual Candidate
Marking Sheet.
The Assessor must devise an examination paper and an outline
marking scheme. These must be made available to the external
Authenticator.
Pass
Merit
Distinction
50 - 64%
65 - 79%
80 - 100%
4
Knowledge Based Systems
Individual Candidate
Marking Sheet 1
C30142
Project 50%
Candidate Name: _________________________________ PPSN: ____________________
Centre: _________________________________________________ Centre No: _________
Maximum
Mark
Performance Criteria
Candidate
Mark
Note to Assessor:
List the performance criteria and outline marking scheme used to
assess the project completed by the candidate.
Total
WEIGHED TOTAL (= TOTAL ÷2)
100
50%
Assessor Signature: _______________________________________ Date: ______________
External Authenticator’s Signature: _________________________ Date: ______________
5
Knowledge Based Systems
Individual Candidate
Marking Sheet 2
C30142
Written Examination 50%
Candidate Name: _________________________________ PPSN: ____________________
Centre: _________________________________________________ Centre No: _________
Maximum
Mark
Performance Criteria
Candidate
Mark
5 questions to be answered
Question (…)
20
Question (…)
20
Question (…)
20
Question (…)
20
Question (…)
20
Total
100
WEIGHED TOTAL (= TOTAL ÷ 2)
50%
Assessor Signature: ________________________________________ Date: ____________
External Authenticator’s Signature: __________________________ Date: ____________
6
FETAC Module Results Summary Sheet
Module: Knowledge Based Systems
Module Code: C30142
Elements of Assessment
Project
Maximum Marks per element of assessment
Candidate Name
Exam No.
50%
Signed:
Assessor: _________________________________________________ Date: ___________
This sheet is for teachers/Assessors to record the overall marks of individual candidates. It
should be retained in the centre. The marks awarded should be transferred to the official
FETAC Module Results Sheet issued to centres before the visit of the external Authenticator.
7
Written
Examination
50%
%
Marks
100%
Grade*
Grade*
D: 80 - 100%
M: 65 - 79%
P: 50 - 64%
U: 0 - 49%
W: candidates entered who did not present for assessment