Table 1 shows the statistics based on all questions answered,... some students answered four questions. Averages are fairly consistent across

Table 1 shows the statistics based on all questions answered,... some students answered four questions. Averages are fairly consistent across

BCS THE CHARTERED INSTITUTE FOR IT
BCS HIGHER EDUCATION QUALIFICATIONS
BCS Level 6 Professional Graduate Diploma in IT
MARCH 2014
EXAMINERS’ REPORT
KNOWLEDGE BASED SYSTEMS
General
Table 1 shows the statistics based on all questions answered, including where
some students answered four questions. Averages are fairly consistent across
questions 1,2,3,5, though, a little on the low side. Q4 seems to be the best
answered. Standard deviation results are fairly low across all questions.
Thus, it may be concluded that the candidates appear to be fairly equated in
their ability, but perhaps were overly challenged by the examination (as was
the case in the previous years). However, the spread of results (range) is wide
which would indicate that there were some high achieving candidates. The
statistics are very much in line with previous years. It was expected that
Question 2 would challenge candidates the most since it covers relatively new
issues for this course (sustainability and green IT), and although it produced
the lowest average mark, results were on comparative levels of performance
with most of the other questions. Question 4 was anticipated to attract the
best performance since it has appeared on previous exam papers, and
indeed, it was the best attempted. In general, candidates seem not to have
prepared well for the paper, and have taken an instrumental approach in
which they have revised previous exam questions and attempted to recreate
the answers verbatim. Where variations in questions have been included, or
new questions are presented, candidates have struggled.
Table I
1
1. There is a growing concern today about sustainable systems and
green IT as these issues can affect the choice of systems built and
manner in which they are operated and managed.
i. Develop an argument either in favour of or against the
preference for KBS over conventional IT systems. The
discussion must be justified by appeal to sustainability principles
and green IT issues. (15 marks)
ii. Discuss the broad implications for KBS arising from the
introduction of cloud computing techniques that provide
intelligent computing power over the Internet. (10 marks)
MODEL ANSWER POINTERS
Question 1.i: 15 marks. General distribution of marks according to
salient features.
Candidates must demonstrate an appreciation of sustainability and green IT
issues and show the impact that these issues have on KBS and intelligent
systems.
Learning Outcomes/Assessment Criteria:
2. The learner will be able to critically compare and contrast various
knowledge representation systems. [2.1, 2.2, 2.3, 2.4, 2.5]
3. The learner will understand methodological approaches to developing
knowledge based systems. [3.1, 3.2, 3.3, 3.4]
4. The learner will understand concepts and appreciate the significance of
issues relevant to adaptive computing. [4.1, 4.2, 4.3, 4.4]
7. The learner will know how AI technology has been used in real situations
particularly on the Internet.[7.3, 7.4]
Indicative solution
There are many perspectives that can be taken, for instance, issues regarding
the comparative amount of resources needed to develop KBS and the need to
maintain/update systems will draw on sustainability issues. Also, higher levels
of labour involved in the development need to be balanced with lower levels of
labour required for subsequent use.
Autonomy, where the autonomous nature of embedded KBS systems over the
operator-dependent design of conventional systems saves resources.
Similarly, with Reusability in the context of software components (rule
base/object oriented). Efficiency through better usability (e.g. intelligent
interfaces) reduces operating time and energy usage, Flexibility, where for
instance agile approaches may be seen to be more sustainable for re-design
and maintenance than overly rigid and intensive development methods in
some cases, etc.
2
Question 1.ii: 10 marks. General distribution of marks according to
salient features.
Candidates must demonstrate an appreciation of cloud computing concepts
and show the potential for this technology to support KBS and intelligent
systems usage.
Learning Outcomes/Assessment Criteria:
3. The learner will understand methodological approaches to developing
knowledge based systems. [3.3, 3.4]
4. The learner will understand concepts and appreciate the significance of
issues relevant to adaptive computing. [4.1, 4.2, 4.3, 4.4]
6. The learner will know the major AI application areas and understand the AI
techniques used within them. [6.1, 6.2, 6.3, 6.5, 6.6, 6.7]
7. The learner will know how AI technology has been used in real situations
particularly on the Internet. [7.1, 7.2, 7.3, 7.4]
Indicative solution
This is a relatively new area so candidates will need to rely on general
knowledge of KBS and the IT industry if they have not studied the topic.
Sustainability issues - a paramount concern as without the need to develop
personal copies of software a large positive environmental impact can be
produced. Applicability issues - relating to the relevance of generic KBS to
specific company related tasks need to be addressed. Generality issues relating to how the knowledge in a KBS can be represented in general forms
so as to be usable by different organisations. Robustness issues - greater
usage places more strain on the limits of a KBS. Maintenance issues particularly with CBR approaches may be enhanced with the wider coverage
that comes from a larger user base (over the internet). Confidentiality associated with company and task sensitivity. Etc.
EXAMINER’S COMMENTS
A1.i
a) Answers confused the sustainability issues involved in the
development and use of KBS systems (such as more efficient
algorithms leading to reduced computing resources), with applications
built from KBS that may be used to achieve sustainability goals (such
as fuzzy controllers for heating).
b) Answers presented appropriate sustainability issues but did not provide
adequate discussion to support the main ideas.
c) Answers covered general principles of sustainability and Green IT
without linking to KBS.
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A1.ii
a) Main issues of cloud computing for KBS were identified but not
discussed adequately. Mostly single sentence explanation
accompanied each issue.
b) Answers discussed general principles of cloud computing without
linking to KBS.
2. Consider a multi-agent based system (MAS) for an application of your
own choice (real or hypothetical):
i.
Specify the nature of the task facing the multi-agent system and
justify why it requires a solution reliant on decentralisation and
distribution of responsibilities. (10 marks)
ii.
Explain the differences between cooperative and competitive
MAS and discuss which is most appropriate for the example. (15
marks)
MODEL ANSWER POINTERS
Question 2.i: 10 marks. General distribution of marks according to
salient features.
Candidates must demonstrate an appreciation of how MAS function.
Learning Outcomes/Assessment Criteria:
1. The learner will understand the main approaches used in AI problem
solving. [1.1, 1.2]
5. The learner will know methods used in adaptive computing and understand
how to apply them to problem solving.[5.1, 5.2]
6. The learner will know the major AI application areas and understand the AI
techniques used within them. [6.1, 6.2,6.3, 6.4, 6.5, 6.6, 6.7]
7. The learner will know how AI technology has been used in real situations
particularly on the Internet.[7.2]
Indicative solution
Any task may be chosen and answers should focus on the need to distribute
intelligence due to the complexity of the task. Explanations must demonstrate
that multiple agents are necessary for solution generation.
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Question 2.ii: 15 marks. Five marks for each according to salient
features. Five marks for justification.
Candidates must demonstrate an appreciation of how MAS function.
Learning Outcomes/Assessment Criteria:
1. The learner will understand the main approaches used in AI problem
solving.[1.1, 1.2, 1.3]
4. The learner will understand concepts and appreciate the significance of
issues relevant to adaptive computing. [4.1, 4.2, 4.3, 4.4]
5. The learner will know methods used in adaptive computing and understand
how to apply them to problem solving. [5.1, 5.2, 5.3, 5.4, 5.5]
6. The learner will know the major AI application areas and understand the AI
techniques used within them. [6.1, 6.2, 6.3, 6.4, 6.5, 6.6, 6.7]
Indicative solution
Cooperative: agents work towards shared goals (all win or all loose).
Competitive: agents work towards different goals in an environment of limited
resources (some win and some loose).
Answers should explain these concepts in detail and provide justification for
the choice of the most appropriate one for the chosen task.
EXAMINER’S COMMENTS
A2.i
a) Although general characteristics of MAS were presented, answers
failed to present detailed description of the example application and did
not include justification of the need for decentralisation and distribution.
b) Answers often showed confusion over meaning of competitive MAS –
these are within the same application but compete for resources to
achieve subgoals of the same task NOT two separate applications
vying to win over the other.
A2.ii
a) Justification for choice of competitive or cooperative not given in detail
but just stated. Full rationale is expected.
3. Data Mining has been capitalised on in the commercial world through
the implementation of Business Intelligence Systems.
i.
Discuss how Data Mining technologies have made the transition
from research laboratory to business applications. Focus on the
real business problems that data mining technologies address
and the benefits perceived to have been realised. (15 marks)
ii.
Discuss the implications of using intelligent data analytics to
unfairly exploit data on user behaviour. (10 marks)
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MODEL ANSWER POINTERS
Question 3.i: 15 marks. General distribution of marks according to
salient features.
Candidates must demonstrate an appreciation of commercial usage of data
mining technology, and how application tools have been developed for use by
the non-technical business user.
Learning Outcomes/Assessment Criteria:
1. The learner will understand the main approaches used in AI problem
solving. [1.2]
4. The learner will understand concepts and appreciate the significance of
issues relevant to adaptive computing. [4.1, 4.3, 4.4]
6. The learner will know the major AI application areas and understand the AI
techniques used within them. [6.1, 6.2, 6.3, 6.4, 6.6, 6.7]
7. The learner will know how AI technology has been used in real situations
particularly on the Internet. [7.1, 7.2, 7.3,7.4]
Indicative solution
Data mining is often used in business, particularly on the web, to provide
support for the “implicit web,” in which personal information about the user is
indirectly “discovered” from their normal interactions. Such valuable
knowledge enables tailoring and personalization of services.
With the increased power of Web 2.0, in which users are generators of data
and not just consumers, many new opportunities have emerged for
technologies that can make value of the available data. Data mining as
employed in business intelligence systems and data analytics are means by
which Strategic management is implemented. The knowledge derived from
analysis of data using data mining helps a decision maker understand the
nature of the problem better and supports better informed decision making.
Data Mining can support the tactical detail about exactly how a strategy will
translate to actions. In the business world, data mining is used extensively for
personalized marketing, e.g. for profiling users and targeting advertisements
to their interests and needs.
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Question 3.ii: 10 marks. General distribution of marks according to
salient features.
Candidates must demonstrate current awareness of an important application
of artificial intelligence technology, data mining, and comment on its potential
dangers in the context of privacy.
Learning Outcomes/Assessment Criteria:
1. The learner will understand the main approaches used in AI problem
solving. [1.1, 1.2]
5. The learner will know methods used in adaptive computing and understand
how to apply them to problem solving. [5.1, 5.2, 5.3, 5.4, 5.5]
6. The learner will know the major AI application areas and understand the AI
techniques used within them. [6.1, 6.2, 6.3, 6.4, 6.5]
7. The learner will know how AI technology has been used in real situations
particularly on the Internet. [7.1, 7.2, 7.3, 7.4]
Indicative solution
Data mining is now used in intelligent search engines on the web (Google),
games, consumer robots with vision systems, handwriting reading in PDA,
speech synthesis in word processors, intelligent tutoring, knowledge
management, etc. As knowledge working grows, it is expected that such
technologies would continue to become more prevalent and the potential for
abuse of privacy thus grows.
A major factor impacting on the spread of data analytics is concerns with the
manner in which data are collected, interpreted, distributed and manipulated
to achieve a competitive advantage. The limits of what is acceptable to the
consumer can often be pushed by companies seeking to capitalize on the
value of data. As an example, employers these days can and do survey
personal data on social network sites, such as Facebook, to glean
background data on prospective employees.
Answers could describe how monitoring of pages visited (times and location)
can be used to develop a profile of users’ interests without their explicit
consent to that information, which could be potentially sold on, or used to
target marketing campaigns.
Answers must consider realistic and timely reasons, e.g. right now
technological growth in terms of mobile systems enables data to be stored on
location, which can be interpreted to the advantage of marketers.
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EXAMINER’S COMMENTS
A3.i
a) Descriptions of data mining approaches were provided but there was a
lack of explanation on evolution from lab to business, e.g. due to
eBusiness, internet, cloud, big data.
b) Although examples were given of how data mining has been used, these
were without discussion of the facilitating factors (i.e. big data, cloud,
internet, eBusiness).
A3.ii
a) Although issues about privacy are mentioned in answers, details are
lacking about the consequences of unethical use of data.
4. Knowledge based Systems (KBS) are developed to deal with particular
application domains in which elements of human intelligence are essential in
producing solutions.
i.
Identify and discuss five aspects of human intelligence that could be
used to characterise intelligent knowledge-based systems. (15 marks)
ii.
Take one aspect of human intelligence and explain how it would be
difficult to emulate using rule based systems. (10 marks)
MODEL ANSWER POINTERS
Question 4.i: 15 marks. 3 marks for each aspect of human intelligence
identified and related to KBS.
Some possibilities include:






Ability to temporarily alter behaviour according to environmental
stimulus (adaptability)
Ability to permanently alter behaviour as a result of accumulated
experience with environmental stimuli (learning)
Ability to deal with ill-defined and ambiguous situations (uncertainty)
Ability to prioritise and focus (goal directedness)
Ability to bring to bear subjective insight (judgement)
Ability to deal with complexity and recognise relevance (abstraction)
Application of these traits to a medical diagnostic KBS should consider the
points outlined below.
Adaptability: an investigative procedure can be refined as new symptoms
emerge and new test results become available.
Abstraction: the most significant symptoms will be considered, and those may
be considered as special cases of more general categories of symptoms.
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Connections between symptoms and causes may be established at the
general level, e.g. fever is one of the flu-like symptoms, and flu-like symptoms
could point to influenza, pneumonia, viral infection, etc.
Goal directedness: the diagnostic system has a main goal: to identify the
cause of the symptoms; and will plan procedures that progress towards
achieving that goal.
Judgement: recommendations and decisions will be based on experience with
previous patient cases; thus, different experts may have different judgement
(c.f. second opinion).
Learning: procedures for selecting and conducting tests are based on
treatment of previous patients (with similar symptoms). Results of the
treatment fed back to the KBS will be used to influence future decisions.
Uncertainty: prognoses about illnesses are often predictions that are based
on incomplete information about the progression of the illness in the patient.
That is, there can be incomplete information about the disease and about the
patient.
Question 4.ii: 10 marks. General distribution of marks according to
salient features.
Some possibilities include:
Creativity and imagination are currently not well understood, and are thus,
difficult to describe in an executable computer model. An ability to recognise
faces and differentiate between male and female faces is another area where
clear rules are difficult to elicit because the relevant knowledge is not easily
brought to conscious attention. In general, activities that could be better
described as skills (opposed to knowledge) are not amenable to knowledge
based systems.
EXAMINER’S COMMENTS
A4.i
a) Answers introduced aspects of intelligence but (i) did not provide an
example from humans and/or (ii) did not apply them to KBS. Instead
answers often just gave superficial descriptions of each aspect in
humans or just mentioned that “it applies to KBS too.”
b) Some answers failed to identify appropriate aspects of human
intelligence that set apart KBS from conventional systems, e.g. “being
able to solve a problem.”
A4.ii
a) Answers mostly identified creativity as being hard to emulate in KBS,
but restricted definition to “producing something new”. KBS can
discover knowledge, but the processes employed by humans and
machines are different. KBS cannot be artistic in creating new things.
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b) Many answers stated two or three tasks difficult for RBS (creativity,
skills, face recognition) but failed to discuss any in depth. The question
asked for 1 task to be discussed with supporting explanation.
5. Using examples to illustrate your answers, explain the differences
between the following concepts:
i. procedural and declarative knowledge. (10 marks)
ii. data driven and goal driven reasoning methods. (10 marks)
iii. abductive reasoning (5 marks)
MODEL ANSWER POINTERS
Question 5: 25 marks. five marks for definition of each concept.
Candidates must demonstrate knowledge of fundamental concepts and be
able to distinguish similar or competing elements.
Learning Outcomes/Assessment Criteria:
1. The learner will understand the main approaches used in AI problem
solving. [1.1, 1.2, 1.3]
2. The learner will be able to critically compare and contrast various
knowledge representation systems. [2.1, 2.2, 2.3, 2.4, 2.5]
4. The learner will understand concepts and appreciate the significance of
issues relevant to adaptive computing. [4.1, 4.2, 4.3]
5. The learner will know methods used in adaptive computing and understand
how to apply them to problem solving. [5.1, 5.2, 5.3, 5.4, 5.5]
6. The learner will know the major AI application areas and understand the AI
techniques used within them. [6.3, 6.4, 6.5]
Indicative solution
Procedural: represents progression towards a goal.
Declarative: represents static factual/conditional factual propositions.
Data driven: solutions derived from awareness of all relevant facts for the
problem.
Goal: solutions derived from efforts to satisfy problem related goals.
Abductive reasoning: reasoning from exemplifier to explanatory theory based
on possibilities only. Explanation may be wrong, but appears to be plausible.
Examples of each of these concepts should be included in the answer. For
instance, forward and backward chaining should be described with a real
knowledge base of rules. Also, examples of procedural and declarative
knowledge representations should be included.
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EXAMINER’S COMMENTS
A5.i
a) Answers confused knowledge “type” with knowledge “representation”,
so discussed procedural and declarative representations instead of the
knowledge itself.
A5.ii
a) Question answered well on the whole, though some answers did not
include clear examples of data and goal driven reasoning to illustrate.
A5.iii
a) Few answers explained abductive reasoning well. Some referred to
CBR and analogical reasoning as equivalents without explaining the
process of hypotheses generation fully.
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