The game soldiers play an integrated approach to ai presented by

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Abstract

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.

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 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.

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 animals and 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''.

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.

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 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.

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.

"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 human-robot 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.

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.

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.

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 geo-location 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 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.


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.

Books:

Artificial Intelligence: A New Synthesis by Nils Nilsson, Morgan Kaufman

Artificial intelligence- George F Luger

URL:

Figure 1.1The neuron "firing", passing a signal to the next in the chain.

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Figure 1.5

Figure 1.6