Download This technique is used to represent different types

Q1
A. Explain knowledge representation scheme and give examples for each. (12M)
i)
Rules based
This technique is useful when representing heuristics (rules of thumb) of how we do
things or what we do and don't know, e.g. diagnosis or fault repair. Rules are linked into
chains of reasoning by an expert system which can use either backward chaining or
forward chaining. Backward chaining tries to prove a hypothesis by finding rules with
the hypothesis result in its conclusion; whereas forward chaining is data-driven and
investigates the consequences of the knowledge and finds the rules whose conditions
are satisfied by the knowledge.
ii)
Semantic Networks
This technique is used to represent different types of relationships between different
properties. The relationships can then be linked together to form a large network of
nodes. The network should contain all the knowledge an expert has to think about
when solving a problem. The network can then be checked by the knowledge expert by
working through the network, to ensure the knowledge engineers understanding of the
problem.
iii)
Frames
This technique uses a remembered framework (generic framework) and adapts it to fit the
reality of each specific frame. The technique involves frames which are data structures
for representing a stereotyped situation. The frames can then be arranged in hierarchies,
with the generic frame at the top and each instance of a specific frame below.
B. What is inference engine? Explain by example. (2M)
Is a computer prog that tries to derive answer from knowledge base major element :
- Interpreter
- Scheduler
- Consistency enforce
C. Explain what is fwd chaining and backward chaining by providing an example for each? (2M)
-
Forward Channing :
Data driven
Example :
Flying from Denver to Tokyo
Flights leaving Denver – Destinations
Are any destinations Tokyo?
If not, from those non Tokyo dests, what flights leave?
Which of those go to Tokyo?
-
Backward Channing
Goal Driven
Example : Flying to Tokyo from Denver
What flights arrive in Tokyo
Do any originate in Denver
If not, for each origination, what flights end there?
And where do they originate (Do any originate in Denver)
….
::Refer notes PG4/KR3(week7)::
D. Until now there is still no intelligent machine can fully challenge human intelligence. Give 3
reasons to compare and contrast (to show the difference) between Artificial Intelligence and
Human Intelligence. (3M)
Human intelligence
Artificial intelligence
Human intelligence revolves around
The field of Artificial intelligence
adapting to the environment using a
focuses on designing machines that
combination of several cognitive
can mimic human behavior.
processes.
humans dump some of the intelligence in
Artificial Intelligence only tries to be intelligent
order to be creative.
can deal with unexpected situation when
it demands it .
function on some pre-set rules . So in
this way humans can learn from
experience whereas computers can't
E. Using your own words, discuss what you think the future of intelligence systems would be like?
Give your opinion by providing examples to justify your arguments. (6M)
Contoh 1 :
Good question. People are often confused in thinking that AI's sole purpose is to be AH
(artifically human). Intelligence is simply making the best choice. It has nothing to do with art,
music, creativity or emotions. People think AI is in the future but its been done many times.
Anytime a machine makes "human decisions" its AI. It directs traffic, installs software without
asking you 100 questions, it can fly planes on autopilot, and has even explored other planets.
But that still isnt AH.
The best example is in games. People often complain that the AI sucks but what they really want
is AH (artificially human). Going from one spot on a map to another is easy for an AI. Its a
straight line, go around the obstacles. But if that is your opponent in a game then players quickly
exploit it. They want it to choose straight line, or flanking movement, or a fake direct action with
a move up the side, and even a small random chance of a totally stupid action except that
sometimes it might work.
Most AI applications do not need to come close to being human. They only need to know the
intelligent choices and pick the best one without picking a stupid one. For AH projects (art,
music, games, anything creative) its kindof a giggle to AI programmers that to get close to
human you need to dump more and more of the "intelligent" part and work with more and
more randoms. So I guess the answer to your question is that Artificial Intelligence only tries to
be intelligent, while humans dump some of the intelligence in order to be creative.
Contoh 2 :
Artificial Intelligence (AI) is the area of computer science focusing on creating machines that can
engage in behaviors that humans consider intelligent. The ability to create intelligent machines
has intrigued humans since the dawn of the industrial era and after 50 years of research the
dream of smart machines is gradually becoming a reality. We are increasingly becoming more
dependent on intelligent machines.
Intelligent machines are found everywhere in today's world. At least once a day most people
would use an ATM machine, a grocery store scanner and register, a car, or a microwave. The list
of computer controlled systems is immense. Personal computers have become common
household items, and more people interact with computers at a personal level. The demand is
growing that this technology should not only fulfil our demands but anticipate our needs. The
consumer wants this technology to become intelligent and the developers are trying hard to
achieve this goal.
As our understanding of our own consciousness grows so too does out ability to recreate this in
a machine. It is not unlikely that one day it will be possible to build artificial machines whose
intelligence matches, and possibly even exceeds, that of humans. The study of artificial
intelligence has provided better programming techniques for building smarter computer
systems. Can computers become intelligent? Is this really possible and if so, how?
::Sumber Google::
Q3
A.
Steps in machine learning
- Data Collection : Training data , optionally with labels provided by a teacher
- Representation : How the data are encoded into features when presented to learning algorithm
- Modelling : Choose the class of models that the learning algorithm will choose from
Validation : evaluate the learned model and compare to solution found using other model classess.
B.
Supervised
The neural network trained with data that h
as known right & wrong answer
Use to identify error & adjust the weight
accordingly
Example : Perceptor learning algorithm, least
mean – square learning & back propagation
Unsupervised
No answer given in the test data
Use to analyse data to understand their
similarities
Example : k-mean clustering , adaptive resonanic
theory (ARD & kohonen self – organizing Maps
C.
Three application of classification in pattern recognition
i)
ii)
iii)
Face Recognition - Pose, Lighting, occlusion, make up, hair style
Speech Recognition- Temporal dependency, Use of a dictionary or the syntax of the
language,
Character Recognition- Different Handwriting styles
D.
Partitioning :
-
Commonly are grouped in an exclusive way, one data can only belong to one cluster
It is heuristic method where each cluster is represented by the centre of cluster
Example : K-means
Agglomerative :
-
Every data is a cluster initially & interactive unions between the two nearest clusters reduces
the numbers of clusters
Example : Hierarchical Clustering
Overlapping :
-
Uses fuzzy sets to clusters data, so each points may belong to two or more clusters with
different degrees of membership
In this case, data will be associated to an appropriate membership value
Example : Fuzzy c-means
Probabilistic :
-
Uses probability distribution measures to create the clusters
Example : Gaussian mixture model clustering, which is a variant of K-mean.
E. Explain how we can determine the goodness of clustering using intra-cluster similarity and intercluster dissimilarity measurements?
A good clustering method will produce high quality clusters where:
- the intra-class similarity (that is within a cluster) is high.
- the inter-class similarity (that is between clusters) is low.
The quality of a clustering result also depends on the similarity measure used by the method.
The quality of a clustering result also depends on the definition and representation of cluster –
different clustering algorithms may have different underlying notions of clusters.