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TitleSolution Manual to Third Edition AI
TagsTechnology Artificial Intelligence Knowledge Representation And Reasoning Fuzzy Logic Genetic Algorithm
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Total Pages62
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Elaine Rich is Director of the Artificial Intelligence Laboratory at the Microelectronics
and Computer Technology Corporation (MCC). Formerly on the faculty in computer sciences
at the University of Texas, she received her PhD from Carnegie Mellon University. Her
research interests include natural language processing. knowledge representation, and
machine translation.

Kevin Knight received his B.A. from Harvard University and is presently completing his
PhD in computer science at Carnegie Mellon University. A regular consultant at MCC, his
research interests include natural language processing, unification, and machine translation,
and search.

Shivashankar B Nair received his Master’s and Doctoral degrees in Engineering from
Amravati University, Amravati, where he served as a faculty member from 1986 to 1998.
He later joined the Indian Institute of Technology Guwahati, where he is currently an
Associate Professor in Computer Science and Engineering. His major areas of interest include
real-world Artificial Immune System Applications, Intelligent and Emotional Robotics,
Natural Language Processing, Genetic Algorithms and Mobile Agent Systems.

Dr Nair has been the chief investigator for projects funded both by the Indian government
and foreign agencies and is a member of several international and national journal and
conference committees. Presently, he is on a sabbatical as a Visiting Professor (Korean
Brain Pool) at the Human-Centred Advanced Research Education Centre, Hanbat National
University, Daejeon, South Korea, where he is investigating new ways of instilling emotions
into robots.

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Chapter 24


1. A smart house needs an efficient burglar detection and alarm system. Can you
model these using AIS concepts?

You could imagine numerous strategically located burglar detectors each comprising
of cameras and proximity sensors that can trigger alarms. Cameras could sense
movement.


Immune system Approx. Metaphors
Lymphocyte (B-cell,T-cell)
Antibody variable region
Memory cell

Pathogen
Circulation


MHC

Cytokines
Signals
Pathogen detection
Pathogen elimination

Burglar Alarm
Alarm tone/intensity
A vulnerable area (in the fence) detected earlier
by one of the detectors (camera).
Burglar
Detectors could be networked and signal others
of a possibly intrusion causing increase their
sensitivity levels.
Representation parameters/Image and proximity
processing
Sensitivity level
Activation threshold, human owner
Detection event
Response from alarm and human intervention


The student could be asked to provide his/her own viewpoints about the model being
tailored to suit AIS concepts within.

2. Many events can be looked upon as a defence mechanism. For instance the simple fact
that you can balance on two legs is a mechanism to defend (or prevent) your centre of
gravity from moving away from a stable point. Self preservation can be viewed, likewise,
as the capability to defend your body. At every stage your actions (be it moving, talking,
thinking, …) are all measures taken to make you feel stable (comfortable). With this in
mind try modelling the following into an AIS based system:


(i) Riding a cycle
(ii) Spam detection and elimination


The Instructor is urged to motivate the students to think of each of the above problems as one that has
an Antigen and a Pathogen. Rest of the metaphors could then be discussed.


(i) This is hypothetical unless we actually model an AIS based robot to
actually ride the cycle. There have been attempts to balance a pole
using a numerous sensors, each acting on the system to stabilize it.
Riding a cycle could also be looked upon as one such system wherein
sensors could find the tilt, the movement of the centre of gravity and
take corrective action using AIS concepts. Destabilization/shift in the
centre of gravity could be viewed as an antigen. Its detection could
trigger antibodies in the form of motor (muscle) activations that

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counteract this change in an attempt to balance the cycle. Initially
generated antibodies may not be all that successful but a clonal
selection mechanism will allow those successful to become memory
cells and result in stable and safe riding over a period of time.


(ii) Spam has become a perennial problem. While many attempts have

been made to circumvent it, the fact that spammers change their modes
of spamming is highly frustrating.. An initial repertoire of words
(naïve B/T-cells) could be extracted from an existing 100% spam
database (similar to the bone marrow model). These could be used as
starting material to counter spam. These words could undergo somatic
mutation/hypermutation to catch the ever changing camouflaged
words within the incoming email and trigger a detection. For instance
the word – Viagra may have been spelt as Vi$agra. Mutation can help
generate such non-words from real words. Students could be
encouraged to use WordNet (http://wordnet.princeton.edu/) to find
words related to spam words (such as synonyms) so as to make the
word population diverse.

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