Chapter 15_Group Discussion Artificial Intelligencex

beadkennelAI and Robotics

Oct 15, 2013 (3 years and 8 months ago)


Group Discussion Artificial Intelligence
(July 26)


This is a group assignment in which the groups work on the assigned topic and present the topic to the class
in 10 minutes.



Notes in MS
Word or PP presentation on the topic with the list of website links or resources used to prepare the
topic. Video links on the topic are encouraged but not mandatory


class Presentation

Total Points 100:

75 points

notes/ppt and 25 points


class presentation

The topics are listed below:

Instructor: Will open the topic with the definition of intelligence

Group 1: What is Artificial Intelligence?, Alan Turing, Turing Test

Group 2: Games using AI techniques: searching, brute force techniq
ue, heuristics, pattern recognition, machine learning

Group 3: Natural Language Communication

Translation Programs; Challenges

Group 4: Knowledge Bases and Expert Systems; fuzzy logic; application/s of fuzzy logic

Group 5: Pattern Recognition: Image Anal

Group 6: OCR; Automatic Speech Recognition

Group 7: Robot Revolution

Group 8: Neural Networks; Applications/future

Instructor: AI Implications and Ethical Questions, Micro technology, Nanotechnology and Artificial Life

Artificial intelligence

(AI) w
ould be the possession of intelligence, or the exercise of thought, by machines such as
computers. Philosophically, the main AI question is “Can there be such?” or, as Alan Turing put it, “Can a machine think?”
What makes this a philosophical and not just
a scientific and technical question is the scientific recalcitrance of the
concept of intelligence or thought and its moral, religious, and legal significance. In European and other traditions, moral
and legal standing depend not just on what is outwardly
done but also on inward states of mind. Only rational individuals
have standing as moral agents and status as moral patients subject to certain harms, such as being betrayed. Only
sentient individuals are subject to certain other harms, such as pain and su
ffering. Since computers give every outward
appearance of performing intellectual tasks, the question arises: “Are they really thinking?”

Game Playing

Game playing engaged the interest of AI researchers almost from the start. Samuel’s (1959) checkers (or
program was notable for incorporating mechanisms enabling it to learn from experience well enough to eventually to
outplay Samuel himself. Additionally, in setting one version of the program to play against a slightly altered version,
over the settings of the stronger player to the next generation, and repeating the process

enabling stronger and
stronger versions to evolve

Samuel pioneered the use of what have come to be called “genetic algorithms” and
“evolutionary” computing. Ches
s has also inspired notable efforts culminating, in 1997, in the famous victory of Deep Blue
over defending world champion Gary Kasparov in a widely publicized series of matches (recounted in Hsu 2002). Though
some in AI disparaged Deep Blue’s reliance on
“brute force” application of computer power rather than improved search
guiding heuristics, we may still add chess to checkers (where the reigning “human
machine machine champion” since
1994 has been CHINOOK, the machine), and backgammon, as games that com
puters now play at or above the highest
human levels. Computers also play fair to middling poker, bridge, and Go

though not at the highest human level.
Additionally, intelligent agents or “
soft bots
” are elements or participants in a variety of electroni
c games.

What is a brute force technique?

Ability of a machine to perform tasks thought to require human intelligence. Typical applications include game playing,
language translation,
xpert systems
, and
. Although pseudo
intelligent machinery dates back to antiquity, the first
glimmerings of true intelligence awaited the development of
digital computers

in the 1940s. AI, or at least the semblance
of intelligence, has developed in parallel with computer processing power, which appears to be the main limiting factor.
Early AI projects, such as playing ches
s and solving mathematical problems, are now seen as trivial compared to visual
pattern recognition
, complex decision making, and the use of natural language.

Artificial Intellig
ence programs operating in competitive domains typically use brute
force search if the domain can be
modeled using a search tree or alternately use non search heuristics as in production rule
based expert systems. While
force techniques have recently

proven to be a viable method for modeling domains with smaller search spac
such as checkers and chess.
This research uses a cognitive
based modeling strategy to develop a heuristic search
technique based on cognitive thought processes with minimal
domain specific knowledge. The cognitive
based search
technique provides a significant reduction in search space complexity and, furthermore, enables the search paradigms to
be extended to domains that are not typically thought of as search domains such as

aerial combat or corporate takeovers.

Pattern recognition:

In computer science, the imposition of identity on input data, such as speech, images, or a stream of
text, by the recognition and delineation of patterns it contains and their relationships. Sta
ges in pattern recognition may
involve measurement of the object to identify distinguishing attributes, extraction of features for the defining attributes,
comparison with known patterns to determine a match or mismatch. Pattern recognition has extensi
ve application in
astronomy, medicine, robotics, and remote sensing by satellites.

Informally, a pattern is defined by the common denominator among the multiple instances of an entity. For example,
commonality in all fingerprint images defines the
fingerprint pattern. Thus, a pattern could be a fingerprint image, a
handwritten cursive word, a human face, a speech signal, a bar code, or a web page on the internet. Often, individual
patterns may be grouped into a category based on their common propert
ies; the resultant group is also a pattern and is
often called a pattern class. Pattern recognition is the science of observing (sensing) the environment, learning to
distinguish patterns of interest from their background and making sound decisions about t
he patterns or pattern classes.