Artificial Intelligence Network Load Comparison in Ant Colony Optimization by IJIRST

IJIRST –International Journal for Innovative Research in Science & Technology| Volume 4 | Issue 4 | September 2017 ISSN (online): 2349-6010

Artificial Intelligence Network Load Comparison in Ant Colony Optimization M. Sibisakkaravarthi Research Scholar Department of Computer Science Muthayammal College of Arts & Science, India

P. Subramaniam Head of Dept. & Associate Professor Department of Computer Science Muthayammal College of Arts & Science, India

Abstract Ants first evolved around 120 million years ago, take form in over 11,400 different species and are considered one of the most successful insects due to their highly organized colonies, sometimes consisting of millions of ants. One particular notability of ants is their ability to create "ant streets", long bi-directional lanes of single file pathways in which they navigate landscapes in order to reach a purpose in optimal time. These ever-changing networks are made possible by the use of pheromones which guide them using a shortest path mechanism. This practice allows an adaptive routing system which is updated should a more optimal path be found or an obstruction placed across an existing pathway. Computer scientists began researching the behavior of ants in the early 1990's to discover new routing algorithms. The result of these studies is Ant Colony Optimization (ACO) and in the case of well implemented ACO techniques, optimal performance is comparative to existing top-performing routing algorithms. This article details how ACO can be used to vigorously route traffic efficiently. An efficient routing algorithm will minimize the number of nodes that a call will need to connect to in order to be completed thus; minimizing network load and growing reliability. An implementation of ANT Net based on Marco Dorigo has been designed and through this a number of visually aided test were produced to compare the genetic algorithm to a non-generic algorithm. The report will final conclude with a synopsis of how the algorithm perform and how it could be further optimized. Keywords: Neural Networks, Optical Language, Multi-Layer, Artificial Intelligence _______________________________________________________________________________________________________ I.

INTRODUCTION

Artificial intelligence (AI) is defined as intelligence exhibited by an artificial entity. Such a system is generally assumed to be a computer. Although AI has a strong science fiction connotation, it forms a vital branch of computer science, dealing with intelligent behavior, learning and version in machines. Research in AI is concerned with producing machines to mechanize tasks requiring intelligent behavior. Examples include control, planning and preparation, the ability to answer diagnostic and consumer questions, handwriting, speech, and facial recognition. As such, it has become a scientific discipline, focused on providing solutions to real life problems. AI systems are now in routine use in economics, medicine, engineering and the martial, as well as being built into many common home computer software applications, traditional policy games like computer chess and other video games. Categories of AI AI divides roughly into two schools of thought: Conventional AI Conventional AI mostly involves methods now classified as machine learning, characterized by formalism and statistical analysis. This is also known as symbolic AI, logical AI, neat AI and Good Old Fashioned Artificial Intelligence (GOFAI).  Expert systems: apply reasoning capabilities to reach a conclusion. An expert system can process large amounts of known information and provide conclusions based on them.  Case based reasoning  Bayesian networks  Behavior based AI: a modular method of building AI systems by hand. Computational Intelligence (CI) Computational Intelligence involves iterative growth or learning (e.g. parameter tuning e.g. in connectionist systems). Learning is based on empirical data and is associated with non-symbolic AI, scruffy AI and Neural networks: systems with very strong pattern recognition capabilities.  Fuzzy systems: techniques for reasoning under uncertainty, has been widely used in modern industrial and consumer product control systems.  Evolutionary computation: applies biologically inspired concepts such as populations, mutation and survival of the fittest to generate increasingly better solutions to the problem. These methods most notably divide into evolutionary algorithms (e.g. genetic algorithms) and swarm intelligence (e.g. ant algorithms).

Artificial Intelligence Network Load Comparison in Ant Colony Optimization (IJIRST/ Volume 4 / Issue 4/ 012)

American Association for Artificial Intelligence (AAAI) Founded in 1979, the American Association for Artificial Intelligence (AAAI) is a nonprofit scientific society devoted to advancing the scientific considerate of the mechanisms underlying thought and intelligent behavior and their embodiment in machines. AAAI also aims to increase public understanding of artificial intelligence, improve the teaching and training of AI practitioners, and provide guidance for research planners and funders concerning the importance and potential of current AI developments and future directions. Applications of AI Game Playing You can buy machines that can play master level chess for a few hundred dollars. There is some AI in them, but they play well against people mainly through brute force computation--looking at hundreds of thousands of positions. Speech Recognition In the 1990s, computer speech recognition reached a practical level for limited purposes. Thus United Airlines has replaced its keyboard tree for flight information by a system using speech recognition of flight numbers and city names. It is quite convenient. On the other hand, while it is possible to instruct some computers using speech, most users have gone back to the keyboard and the mouse as still more convenient. Understanding Natural Language Just getting a sequence of words into a computer is not enough. Parsing sentences is not enough either. The computer has to be provided with an understanding of the domain the text is about, and this is presently possible only for very limited domains. II. COMPUTER VISION The world is composed of three-dimensional objects, but the inputs to the human eye and computer’s TV cameras are two dimensional. Some useful programs can work solely in two dimensions, but full computer vision requires partial three-dimensional information that is not just a set of two-dimensional views. At present there are only limited ways of representing three-dimensional information directly, and they are not as good as what humans evidently use. III. PROFESSIONAL SYSTEMS A “information engineer'” interviews experts in a certain domain and tries to embody their knowledge in a computer program for carrying out some task. How well this works depends on whether the intellectual mechanisms required for the task are within the present state of AI. One of the first expert systems was MYCIN in 1974, which diagnosed bacterial infections of the blood and suggested treatments. It did better than medical students or practicing doctors, provided its limitations were observed. Heuristic Classification One of the most feasible kinds of expert system given the present knowledge of AI is to put some information in one of a fixed set of categories using several sources of information. An example is advising whether to accept a proposed credit card purchase. Information is available about the owner of the credit card, his record of payment and also about the item he is buying and about the establishment from which he is buying it (e.g., about whether there have been previous credit card frauds at this establishment). Background Electronic communication networks can be categorized as either circuit-switched or packet-switched. Circuit-switched networks rely on a dedicated connection from source to destination which is made once at start-up and remains constant until the tear-down of the connection. An example of a circuit switched network would be British Telecom’s telephone network. Packet-switched networks work quite differently however and all data to be transmitted is divided into segment and sent as data-packet. Datapackets can arrive out of order in a packets-switched network with a variety of paths taken through different nodes in order to get to their destination. The internet and office local area networks are both good examples of packet-switched networks. A number of techniques can be employed to optimize the flow of traffic around a network. Such techniques include flow and congestion control where nodes action packet acknowledgements from destination nodes to either ramp-up or decrease packet transmission speed. The area of interest in this report concentrates on the idea of network routing and routing tables. These tables hold information used by a routing algorithm to make a local forwarding decision for the packet on the next node it will visit in order to reach its final destination. One of the issues with network routing (especially in very large networks such as the internet) is adaptability. Not only can traffic be randomly high but the structure of a network can change as old nodes are removed and new nodes added. This perhaps makes it almost impossible to find a combination of constant parameters to route a network optimally. Routing algorithms Packet-switched networks dynamically guide packets to their destination via routing tables stored in a link state and are selected via a link state algorithm.

Artificial Intelligence Network Load Comparison in Ant Colony Optimization (IJIRST/ Volume 4 / Issue 4/ 012)

The Link state algorithm works by giving every node in the network a connectivity graph of the network. This graph depicts which nodes are directly connected. Values are stored for connected nodes in map which represent the shortest path to other nodes. One such link state algorithm used in network routing is Dijkstras algorithm. When a path between two nodes is found, its weight is updated in the table. Should a shorter path be found the new optimal weight will be updated to the table replacing the old value. The algorithm allows traffic to be routed around the network whilst connecting to the least numb nodes as possible. The system works but doesn't take into account influx of traffic and load balancing. IV. ARTIFICIAL INTELLIGENCE We now move to a discussion of architectural proposals within the information processing perspective. Our goal is to try to place the multiplicity of proposals into perspective. As we review various proposals, we will present some judgments of our own about relevant issues. But first, we need to review the notion of architecture and make some additional distinctions. Form and Content Issues in Architectures In computer science, a programming language corresponds to a virtual architecture. A specific program in that language describes a particular (virtual) machine, which then responds to various inputs in ways defined by the program. The architecture is thus what Newell calls the fixed structure of the information processor that is being analyzed, and the program specifies a variable structure within this architecture. We can regard the architecture as the form and the program as the content, which together fully instantiate a particular information processing machine. We can extend these intuitions to types of machines which are different from computers. For example, the connectionist architecture can be abstractly specified as the set {{N}, {nI}, {nO}, {zi}, {wij}}, where {N} is a set of nodes, {nI} and {nO} are subsets of {N} called input and output nodes respectively, {zi} are the functions computed by the nodes, and {wij} is the set of weights between nodes. A particular connectionist machine is then instantiated by the "program" that specifies values for all these variables. We have discussed the prospects for separating intelligence (a knowledge state process) from other mental phenomena, and also the degree to which various theories of intelligence and cognition balance between fidelity to biology versus functionalism. We have discussed the sense in which alternatives such as logic, decision tree algorithms, and connectionism are all alternative languages in which to couch an information processing account of cognitive phenomena, and what it means to take a Knowledge Level stance towards cognitive phenomena. We have further discussed the distinction between form and content theories in AI. We are now ready to give an overview of the issues in cognitive architectures. We will assume that the reader is already familiar in some general way with the proposals that we discussing. Our goal is to place these ideas in perspective. Intelligence as Just Computation Until recently the dominant paradigm for thinking about information processing has been the Turing machine framework, or what has been called the discrete symbol system approach. Information processing theories are formulated as algorithms operating on data structures. In fact AI was launched as a field when Turing proposed in a famous paper that thinking was computation of this type (the term "artificial intelligence" itself was coined later) . Natural questions in this framework would be whether the set of computations that underlie thinking is a subset of Turing-computable functions, and if so how the properties of the subset should be characterized. Most of AI research consists of algorithms for specific problems that are associated with intelligence when humans perform them. Algorithms for diagnosis, design, planning, etc., are proposed, because these tasks are seen as important for an intelligent agent. But as a rule no effort is made to relate the algorithm for the specific task to a general architecture for intelligence. While such algorithms are useful as technologies and to make the point that several tasks that appear to require intelligence can be done by certain classes of machines, they do not give much insight into intelligence in general. Architectures for Deliberation Historically most of the intuitions in AI about intelligence have come from introspections about the relationships between conscious thoughts. We are aware of having thoughts which often follow one after another. These thoughts are mostly couched in the medium of natural language, although sometimes thoughts include mental images as well. When people are thinking for a purpose, say for problem solving, there is a sense of directing thoughts, choosing some, rejecting others, and focusing them towards the goal. Activity of this type has been called "deliberation." Deliberation, for humans, is a coherent goal-directed activity, lasting over several seconds or longer. For many people thinking is the act of deliberating in this sense. We can contrast activities in this time span with other cognitive phenomena, which, in humans, take under a few hundred milliseconds, such as real-time natural language understanding and generation, visual perception, being reminded of things, and so on. These short time span phenomena are handled by what we will call the subdeliberative architecture, as we will discuss later. Researchers have proposed different kinds of deliberative architectures, depending upon which kind of pattern among conscious thoughts struck them. Two groups of proposals about such patterns have been influential in AI theory-making: the reasoning view and the goal-subgoal view.

Artificial Intelligence Network Load Comparison in Ant Colony Optimization (IJIRST/ Volume 4 / Issue 4/ 012)

Alternative Proposals The various proposals differ along a number of dimensions: what kinds of tasks the architecture performs, degree of parallelism, whether it is an information processing architecture at all, and, when it is taken to be an information processing architecture, whether it is a symbolic one or some other type. With respect to the kind of tasks the architecture performs, we mentioned Newell’s view that it is just recognition architecture. Any smartness it possesses is a result of good abstractions and good indexing, but architecturally, there is nothing particularly complicated. In fact, the good abstractions and indexing themselves were the result of the discoveries of deliberation during problem state search. The real solution to the problem of memory, for Newell, is to get chunking done right: the proper level of abstraction, labeling and indexing is all done at the time of chunking. In contrast to the recognition view are proposals that see relatively complex problem solving activities going on in subdeliberative cognition. Cognition in this picture is a communicating collection of modular agents, each of whom is simple, but capable of some degree of problem solving. For example, they can use the means-ends heuristic (the goal-subtotaling feature of deliberation in the Soar architecture). Deliberation has a serial character to it. Almost all proposals for the subdeliberative architecture, however, use parallelism in one way or another. Parallelism can bring a number of advantages. For problems involving similar kinds of information processing over somewhat distributed data (like perception), parallelism can speed up processing. Ultimately, however, additional problem solving in deliberation may be required for some tasks. Situated Cognition Real cognitive agents are in contact with the surrounding world containing physical objects and other agents. A new school has emerged calling itself the situated cognition movement which argues that traditional AI and cognitive science abstract the cognitive agent too much away from the environment, and place undue emphasis on internal representations. The traditional internal representation view leads, according to the situated cognition perspective, to large amounts of internal representation and complex reasoning using these representations. Real agents simply use their sensory and motor systems to explore the world and pick out the information needed, and get by with much smaller amounts of internal representation processing. At the minimum, situated cognition is a proposal against excessive "intellection." In this sense, we can simply view this movement as making different proposals about what and how much needs to be represented internally. The situated cognition perspective clearly rejects the former view with respect to internal (sub-deliberative) processes, but accepts the fact deliberation does contain and use knowledge. Thus the Knowledge Level description could be useful to describe the content of agent’s deliberation. V. CONCLUSION We conclude that if the machine could successfully pretend to be human to a knowledgeable observer then you certainly should consider it intelligent. AI systems are now in routine use in various field such as economics, medicine, engineering and the military, as well as being built into many common home computer software applications, traditional strategy games etc. AI is an exciting and rewarding discipline. AI is branch of computer science that is concerned with the automation of intelligent behavior. The revised definition of AI is - AI is the study of mechanisms underlying intelligent behavior through the construction and evaluation of artifacts that attempt to enact those mechanisms. So it is concluded that it work as an artificial human brain which have an unbelievable artificial thinking power. REFERENCES [1] [2] [3] [4] [5] [6] [7] [8] [9] [10] [11] [12] [13]

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