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CUSTOMER_CODE
SMUDE
DIVISION_CODE
SMUDE
EVENT_CODE
JAN2016
ASSESSMENT_CODE BT9402_JAN2016
QUESTION_TYPE
DESCRIPTIVE_QUESTION
QUESTION_ID
13592
QUESTION_TEXT
Explain the different fundamental principles considered in artificial
intelligence.
SCHEME OF
EVALUATION
i.The first principle: Bounded rationality implies opportunities search:
This is the fundamental result of Simon’s research on decision making in
organizations, leading to the Nobel prize.
ii.The second principle: A physical symbol system is necessary and
sufficient for intelligent action:
The second principle of AI is the physical system hypothesis, i.e. a
physical symbol system is necessary and sufficient for intelligent action.
iii.The third principle: the magic no 70,000+20,000: The third principle
of AI is that an expert knows 70,000 counts of information give or take a
binary order of magnitude.
iv.Fourth principle: Search compensate for lack of knowledge: The
fourth principle of AI is that search compensate for lack of knowledge.
v.The fifth principle: knowledge compensate for lack of search: - This is
an important insight which was clearly understood even as late as 1970
i.e. knowledge reduces uncertainty and helps us constrain the
exponential growth leading to the solution of the many otherwise
unsolved problems.(2 marks each)
QUESTION_TYPE
DESCRIPTIVE_QUESTION
QUESTION_ID
13596
QUESTION_TEXT
Explain various building blocks of expert systems.
SCHEME OF
EVALUATION
i.Knowledge base: The knowledge base of expert systems contains both
factual and heuristic knowledge. Factual knowledge is that knowledge
of the task domain that is widely shared.
Heuristic knowledge is the less rigorous, more experiential, more
judgmental knowledge.
ii.Reasoning or inference process/ engine: The problem solving model
or paradigm organizes & controls the steps taken to solve the problem.
iii.Explaining how or why: The explanation module provides the user
with an explanation of the reasoning process when requested. This is
done in response to a how query or a why query.
iv.Building a knowledge base: The editor is used by developers to create
new rules for addition to the knowledge base, to delete outmoded rules,
or to modify existing rules in some way.
v.The I/O interface: It permits the user to communicate with the system
in a more natural way by permitting the use of simple selection menus
or the use of restricted language which is close to a natural language. (2
marks each)
QUESTION_TYPE
DESCRIPTIVE_QUESTION
QUESTION_ID
72846
QUESTION_TEXT
Write any five Features of Expert System
SCHEME OF
EVALUATION
(2*5=10 marks)
Goal driven reasoning or backward chaining–an inference technique which uses if
THEN rules to repetitively break a goal into smaller sub goals which are easier to
prove.
Coping with uncertainty: the ability of the system to reason with rules and data which
are not precisely known.
Data driven reasoning or forward chaining – an inference technique which uses IF
THEN rules to deduce a problem solution from initial data.
Data representation– the way in which the problem specific data in the system is
stored and accessed
User interface –that portion of the code which creates an easy to use system
Explanations –the ability of the system to explain the reasoning process that it used
to reach a recommendation.
QUESTION_TYPE
DESCRIPTIVE_QUESTION
QUESTION_ID
72847
QUESTION_TEXT
Write any five guidelines to determine whether a problem is suitable for
an expert system solution.
SCHEME OF
EVALUATION
(2*5=10 marks)
Ans:
The need for a solution must justify the costs involved in development. There must be
a realistic assessment of the costs and benefits involved.
Human expertise is not available in all situations where it is needed. If the expert
knowledge is widely available it is unlikely that it will be worth developing an expert
system
The problem may be solved using symbolic reasoning techniqures. It should’nt
require manual dexterity or physical skill.
The problem is well structured and does not require common sense knowledge.
Common sense knowledge is notoriously hard to capture and represent.
The problem cannot be easily solved using more traditional computing methods. If
there is a good algorithm solution to a problem, you don’t want to use an expert
system
Cooperative and articulate expert exist. For an expert system project to be successful
it is essential that the experts are willing to help and don’t feel that their job is
threatened.
The problem is a proper size and scope. Typically you need problems that require
highly specialized expertise, but would only take a human expert a short time solve.
QUESTION_TYPE
DESCRIPTIVE_QUESTION
QUESTION_ID
72848
QUESTION_TEXT
List the architectural principles for Expert system.
SCHEME OF EVALUATION
(1*10=10marks)
Knowledge is power
Knowledge is often inexact and incomplete
Knowledge is often poorly specified
Expert system need to b flexible
Expert systems need to be transparent
Separate inference engine and knowledge base level
Use uniform fact representation
Keep inference engine simple
Explicit redundancy
Amateurs experts slowly.
QUESTION_TYPE
DESCRIPTIVE_QUESTION
QUESTION_ID
126151
QUESTION_TEXT
What is hill climbing strategy? Discuss its drawbacks and solutions.
1.pick a random point in the search space
2.consider all the neighbors of the current state
3.choose the neighbor with the best quality and move to that state
4.repeat 2 through 4 until all the neighboring states are of lower quality
5.return the current state as the solution state
Drawbacks:
SCHEME OF
EVALUATION
local maxima: a peak that is lower than the highest peak in the state
space. Once on a local maximum, hill climbing will halt, even
though there is a better solution
plateau: area of the state space where the evaluation function is nearly
fla. Hill climbing will do a random walk in such an area
ridges: a ridge can have steeply sloping sides, so that the search reaches
the top with ease, but on the top may slope gently toward a peak.
Unless there happens to be operators that move directly along the
top of the ridge the search may oscillate from side to side making
little or no progress.
Solutions:
backtracking
making big jumps
applying multiple rules before testing