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THE GAME SOLDIERS PLAY
- An integrated approach to AI
Presented by,
Abhijeet.Sham.Nadgouda
Vinay.A.Choudapurkar
6th semester B.E.
6th semester B.E.
Information Science Engg
Electronics and communication Engg
E-mail:
[email protected]
[email protected]
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Abstract
This paper is a technical presentation of Artificial Intelligence (AI).It is the science and
engineering of making machines that have an ability to “think” or simulate the human
brain. It is related to the similar task of using computers to understand human
intelligence. The various aspects of AI are dealt in brief, its branches, its applications in
neural networks, robotics, defense, gaming etc, with a detailed historical background
being provided. Finally, the influence of AI research will set the trend in the future of
computing. The products available today are only bits and pieces of what are soon to
follow, but they are a movement towards the future of artificial intelligence. The
advancements in the quest for artificial intelligence have, and will continue to affect our
jobs, our education, and our lives.
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Contents.
1. Introduction to AI
1.1. What is AI?
1.2. Historical background.
1.3. Why use AI?
2. Branches
of AI
2.1. Pattern recognition
2.2. Neural Networks
3. An Engineering approach
3.1.AnatomyofA.L.I.C.E.
3.2. AI agent
4. Applications of AI
4.1. Robotics
4.2. Playstation games
4.3. Hi-tech systems in Defense
5. Conclusion.
6. References.
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1. Introduction
1.1 What is AI?
Artificial intelligence is the science and engineering of making intelligent
machines, especially intelligent computer programs. It is related to the similar task of
using computers to understand human intelligence, but AI does not have to confine itself
to methods that are biologically observable. The element that the fields of AI have in
common is the creation of machines that can “think”. In order to classify machines as
"thinking", it is necessary to define intelligence. To what degree does intelligence consist
of, for example, solving complex Problems, or making generalizations and relationships?
And what about perception and comprehension? Research into the areas of learning, of
language, and of sensory perception has aided scientists in building intelligent machines.
One of the most challenging approaches facing experts is building systems that mimic the
behavior of the human brain, made up of billions of neurons, and arguably the most
complex matter in the universe. Perhaps the best way to gauge the intelligence of a
machine is British computer scientist Alan Turing's test. He stated that a computer would
deserve to be called intelligent if it could deceive a human into believing that it was
human.
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1.2 Historical background
Artificial Intelligence has come a long way from its early roots, driven by dedicated
researchers. The beginnings of AI reach back before electronics, Evidence of Artificial
Intelligence folklore can be traced back to ancient Egypt, but with the development of the
electronic computer in 1941, the technology finally became available to create machine
intelligence. The term artificial intelligence was first coined in 1956, at the Dartmouth
conference, and since then Artificial Intelligence has expanded because of the theories
and principles developed by its dedicated researchers. Through its short modern history,
advancement in the fields of AI have been slower than first estimated, progress continues
to be made. From its birth 4 decades ago, there have been a variety of AI programs, and
they have impacted other technological advancements although the computer provided
the technology necessary for AI, it was not until the early 1950's that the link between
human intelligence and machines was really observed. Norbert Wiener was one of the
first Americans to make observations on the principle of feedback theory. The most
familiar example of feedback theory is the thermostat: It controls the temperature of an
environment by gathering the actual temperature of the house, comparing it to the desired
temperature, and responding by turning the heat up or down. What was so important
about his research into feedback loops was that Wiener theorized that all intelligent
behavior was the result of feedback mechanisms. Mechanisms that could possibly be
simulated by machines. This discovery influenced much of early development of AI.
In late 1955, Newell and Simon developed The Logic Theorist, considered by many to be
the first AI program. The program, representing each problem as a tree model, would
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attempt to solve it by selecting the branch that would most likely result in the correct
conclusion. The impact that the logic theorist made on both the public and the field of AI
has made it a crucial stepping stone in developing the AI field.
In 1958 John McCarthy regarded as the father of AI, announced his new development;
the LISP language, which is still used today. LISP stands for LISt Processing, and was
soon adopted as the language of choice among most AI developers.
1.3 Why use AI?
As discussed above, Intelligence is the computational part of the ability to achieve goals
in the world. Varying kinds and degrees of intelligence occur in people, many
and
animals
some machines.
AI is the area of computer science focusing on creating machines that can engage on
behaviors that humans consider intelligent. The ability to create intelligent machines has
intrigued humans since ancient times and today with the advent of the computer and 50
years of research into AI programming techniques, the dream of smart machines is to
understand
speech,
and
even
beat
the
best
human
chess
player.
Intelligence involves mechanisms, and AI research has discovered how to make
computers carry out some of them and not others. If doing a task requires only
mechanisms that are well understood today, computer programs can give very impressive
performances on these tasks. Such programs should be considered ``somewhat
intelligent''.
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2. Branches of AI:
In the quest to create intelligent machines, the field of Artificial Intelligence has split into
several different approaches based on the opinions about the most promising methods and
theories.
The fields in which AI is applied are:
Pattern recognition, Neural networks, Logical AI, Common sense knowledge and
reasoning, learning from experience, Genetic programming etc.
The branches we are going to deal with are:
2.1. Pattern Recognition : When a program makes observations of some kind, it is often
programmed to compare what it sees with a pattern. For example, a vision program may
try to match a pattern of eyes and a nose in a scene in order to find a face. More complex
patterns, e.g. in a natural language text, in a chess position, or in the history of some
event are also studied. These more complex patterns require quite different methods than
do the simple patterns that have been studied the most.
2.2. Neural Networks: To create AI systems, theorists have proposed different
approaches. The two approaches are: bottom-up and top-down. Bottom-up theorists
believe the best way to achieve artificial intelligence is to build electronic replicas of the
human brain's complex network of neurons, while the top-down approach attempts to
mimic the brain's behavior with computer programs. Neural networks deal with the
bottom-up approach.
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The human brain is made up of a web of billions of cells called neurons, and
understanding its complexities is seen as one of the last frontiers in scientific research.
AI researchers prefer bottom-up approach to construct electronic circuits that act as
neurons do in the human brain. Although much of the working of the brain remains
unknown, the complex network of neurons is what gives humans intelligent
characteristics. By itself, a neuron is not intelligent, but when grouped together, neurons
are able to pass electrical signals through networks. The figure 1.1 shows that a signal
received by a neuron travels through the dendrite region, and down the axon. Separating
nerve cells is a gap called the synapse. In order for the signal to be transferred to the next
neuron, the signal must be converted from electrical to chemical energy. The signal can
then be received by the next neuron and processed.
Based on experiments with neurons, they can be considered as devices for processing
binary numbers. George.Boole assumed that the human mind works according to his
principles, it performs logical operations that could be reasoned. His logic is the basis of
neural networks.
3. An Engineering approach.
3.1 Anatomy of A.L.I.C.E.
A.L.I.C.E.(Artificial Linguistic Internet Computer Entity) is an artificial intelligence
natural language. The A.L.I.C.E. software utilizes AIML (Artificial Intelligence Markup
Language), an XML language designed for creating stimulus-response chat robots.
Some view A.L.I.C.E. and AIML as a simple extension of the old ELIZA psychiatrist
program. The comparison is fair regarding the stimulus-response architecture. But the
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A.L.I.C.E. has at present more than 40,000 categories of knowledge, whereas the original
ELIZA had only about 200. Another innovation was provided by the web, which enabled
natural language sample data collection possible on an unprecedented scale.
A.L.I.C.E. won the Loebner Prize, an annual Turing Test, in 2000 and 2001. Although no
computer has ever ranked higher than the humans in the contest she was ranked “most
human computer” by the two panels of judges.
Some have argued that Turing, when he predicted that a machine could play his game in
“50 years” after his 1950 paper, envisioned something more like a general purpose
learning machine, which does not yet exist. The concept is simple enough: build a robot
to grow like a child, able to be taught language the way we are. But even a child does
not, or at least should not, go forth into the world, unprotected, to learn language “on the
street,” without supervision.
Automatic generation of chat robot questions and answers appears likely to raise the
same trust issues forced upon the abandoned child. People are simply too untrustworthy
in the “facts” that they would teach the learning machine. There would still have to be an
editor, a supervisor, or teacher to cull the wheat from the chaff.
The brain of A.L.I.C.E. consists of roughly 41,000 elements called categories. Each
category combines a question and answer, or stimulus and response, called the “pattern”
and “template” respectively. The AIML software stores the patterns in a tree structure
managed by an object called the Graphmaster, implementing pattern storage and
matching algorithm. The Graphmaster is compact in memory, and permits efficient
pattern matching time.
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3.2. AI Agent:
Referring to the figure 1.2, we can see that the data is fed to the AI agent through the
sensors to two blocks, namely problem representation block and world knowledge block.
In problem representation block, a problem is posed according to the data received by the
sensors. In world knowledge block, the data is verified with the existing one in the block.
If the match is obtained, the solution is fed to the actuators(devices providing power to
robots), else the problem is redefined to match with the data in the world knowledge
block.
4. Applications of AI:
We have been studying this issue of AI application for quite some time now and know all
the terms and facts. But what we all really need to know is what we can do to get our
hands on some AI today. How can we as individuals use our own technology?
Here are some applications we hope to discuss this in depth (but as briefly as possible).
4.1Robotics:
In the past, very simple artificial intelligence systems on board rovers allowed them to
make some simple decisions, but much smarter AI will enable these mobile robots to
make
many
decisions
now
made
by
mission
controllers.
The figure1.3 shows future robotic rovers which will have enough intelligence to
navigate the Martian landscape without detailed instructions from scientists on Earth.
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"Human beings make decisions in response to their environment. How do you
encapsulate this behavior into a rover, or a robot, sitting on a planet millions of miles
away? That's what we are working on," said a computer scientist at NASA. "We want to
put software on rovers to give them the capability to be artificially intelligent,” he
explained.
Large teams of human beings on Earth direct the Mars Exploration Rovers (MER) now
rolling across the Martian terrain to look for evidence of water. It now takes the humanrobot teams on two worlds several days to achieve each of many individual objectives.
A robot equipped with AI, on the other hand, could make an evaluation on the spot,
achieve its mission faster and explore more than a robot dependant on decisions made by
humans on Earth. Today's technology can make a rover as smart as a cockroach, but the
problem is it's an unproven technology.
Figure 1.4 shows: Rovers with additional artificial intelligence will be able to avoid
hazards
including
holes,
impassible
rocks
or
steep
grades.
Tactile learning interfaces: This project investigates how a robotic learning system can
learn to mimic the behavior of a human driver, and how the system gradually can take
control of the steering wheel. A force-feedback control device is used to collect target
values for learning, as well as give the user direct tactile feedback on how the learning
progresses.
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4.2. Playstation Games:
Real Time Strategy in computer games has become one of the most important features of
any real time game. With the introduction of artificial intelligence in the computer games,
the RTS games have grown and evolved with the development of the AI and technology.
these games adopt the techniques and theories adopted in other games as well as have
their own techniques and theories that they follow. In RTS games it becomes almost
impossible to follow the trends adopted in other games for analyzing the game state due
to several factors such as increasing complexity, too many constantly-changing variables,
not enough time for real analysis as the responses have to be immediate, ability to make
Simultaneous moves and many others.
Ever since the beginning of AI, there has been a great fascination in pitting the human
expert against the computer. Game playing provided a high-visibility platform for this
contest. As the computational speed of modern computers increases, the contest of
Knowledge vs. speed is tilting more and more in the computers favor, accounting
for triumphs like Deep Blue’s win over Gary Kasparov.
Artificial Intelligence is now recognized as an important part of the game design process.
It is no longer regarded as the backwater of the schedule. Now crafting a game’s AI has
become every bit as important as the features of the game’s graphics engine.
Artificial Intelligence makes games more fun, more interactive and more appealing
However, majority of the games imposes a constraint on the application of AI:
The AI algorithm to be used in the game must be operative in real time. Some high profile
2D and 3D games like Starcraft, Age of Empires, and Warcraft make use of AI.
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The main objective of an RTS game is to control units which perform tasks to overcome the
opponent’s units. Being able to react immediately to opponents and do several things at once
are key features to popularity of RTS games. These features, though, are also what make
creating an efficient artificial intelligence for this type of game a bit different than others.
4.3. Hi-tech Systems In Defense:
One of the major Application of AI in recent times has been in field of defense. The initial
goal of a Dominant battlespace Knowledge is to know with certainty where enemy and
friendly forces are within a given battlespace, or knowing what these forces are doing or
will do, not just where they are located. Advanced sensor and information fusion will be
expected to provide near-perfect, real time discrimination between targets and non targets
on the battlefield of the future. Artificial Intelligence technologies will be key to solving
the awareness/knowledge problem. Vast amounts of digital data will need to be
processed, correlated, stored, and displayed. The data base of a particular battlespace will
have to be continuously updated with real-time information to make it useful to a
warfighter. At the foundation of any awareness data base must be a common weather,
terrain, and electromagnetic picture concerning a particular battlespace. Precise geolocation data is particularly vital so that information can be used for targeting, both to
successfully destroy an enemy and to prevent fratricide. After gathering all possible data
the AI system will establish information dissemination server(s) that access multiple data
sources to include national and theater intelligence, operational, and logistics databases.
For the user it will provide a graphical depiction of the current situation which is
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consistent between echelons and that allows the user to tailor his view of the battlespace
by drilling down through the supporting information infrastructure to find the precise
information he needs. For example, if the user is looking at an image of a bridge, the data
base could be interrogated to yield information on the length, width, height, and condition
of the bridge. Another aspect of using these systems is that humans don't see in X-ray or
listen to sonar signals, and that is a role for AI in target recognition. The Air Force has
vast databases and needs to get useful patterns. The approach is to organize the data into
a table in which each column is a different attribute of the target and each row is the
target by using AI techniques to reduce the number of columns, the idea is to find the
minimum number of features to identify all targets. This estimates the data reduction can
cut the 128 candidate attributes to about 25 important attributes for target recognition.
Some of the Hi-Tech systems used in defense are High Altitude Endurance Unmanned
Aerial Vehicles (HAE UAV) are Global Hawk and Dark Star, Joint Combat
Identification (CID) ACTD, Battlefield Awareness and Data Dissemination ACTD etc.
‘The AI simulation systems provide the soldiers real time illusion of a war conducted in a
battlefield provides them a rich hand on experience to plan their strategies’ this justifying
the title given to our paper presentation.
Figure 1.6: The figure shows the TUAV system (Tactical Unmanned Aerial
Vehicle).This system provides crucial intelligence-delivered efficiently from its
electronic payload directly to tactical command centers.
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5. Conclusion:
The computing world has a lot to gain from AI. Their ability to learn by example makes
them very flexible and powerful. They are also very well suited for real time systems
because of their fast response and computational times.
Humans employ the pattern matching technique while associating known facts,
images, or other human “data structures” with their real world counterparts. This is how
we recognize people’s faces and voices, as well as identify common objects. This type of
problem is parallel, easily handled by brain’s 100 billion or so neurons. Because it is an
inherently massively parallel process, this type of analysis is much more difficult for a
computer. Computers excel at searching type problems: linear, mathematical type
problems which rely on pure computational brute force.
In some fields such as forecasting weather or finding bugs in computer software, expert
systems are sometimes more accurate than humans. But for other fields, such as
medicine, computers aiding doctors will be beneficial, but the human doctor should not
be replaced.
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6. References
Books:
Artificial Intelligence -Stuart Russell and Peter Norvig.
Artificial Intelligence: A New Synthesis by Nils Nilsson, Morgan Kaufman
Artificial intelligence- George F Luger
URL:
http://www.library.thinkquest.org
http://www.ieee.org
http://www.gameAI.com
http://www.emsl.pnl.gov:2080/docs/cie/neural/neural.homepage.html
http://www.machinebrain.com
Total Word Count: 3210
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Figure 1.1The neuron "firing", passing a signal to the next
in the chain.
Figure 1.2The AI Agent
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.
Figure 1.3
Figure 1.4
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Figure 1.5
Figure 1.6
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