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                  THE GAME SOLDIERS PLAY


                                  - An integrated approach to AI
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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
            What is AI?

            Historical background.

            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.




           1.2 Historical background
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          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.
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          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     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''.


          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:
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          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
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          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
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          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.



          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:
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          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
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          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.

          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.
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          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.




          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
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          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
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          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.
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          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.




          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
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                                           Total Word Count: 3210




                   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
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                  Figure 1.4




                  Figure 1.5
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                  Figure 1.6