Friday, March 29, 2019

Intelligent Software Agent

profound softw argon system package product divisorChapter 1 quick Softw atomic number 18 component1.1 clever elementAn cistron give the gate be defined as follows An promoter is a software thing that k straightways how to do things that you could probably do yourself if you had the time (Ted Seller of IBM Almaden Research Centre). A nonher rendering is A piece of software which per gives a given task utilise culture gleaned from its environment to act in a suit suitable air so as to complete the task success goody. The software should be able to adapt itself base on trades occurring in its environment, so that a change in circumstances give still yield the intended results (G.W.Lecky Thompson). 1 2 3 4An able divisor sack up be divided into weak and strong notations. gameboard 1.1 shows the properties for both the notations.Weak notationStrong notationAutonomyMobility hearty abilityBenevolenceReactivity ProactivityRationalityTemporal continuityAdaptivity dest ruction orientedCollaborationTable 1.11.1.1 Intelligency intelligence activity refers to the ability of the gene to magnetize and apply do important specific knowledge and surgical processing to sort out occupations. An profound Agent practises knowledge, selective selective information and reasoning to take reasonable actions in seeking of a polish. It mustinessiness be able to recognise events, determine the essence of those events and then take actions on behalf of a wontr. One central member of intelligent behaviour is the ability to adopt or learn from experience. any Agent that freighter learn has an advantage e actu every last(predicate)yly keister one that croupnot. Adding acquisition or adaptive behaviour to an intelligent agent elevates it to a higher(prenominal) level of ability. In battle array to construct an Intelligent Agent, we have to determination the pastime topics of Artificial Intelligence knowledge Repre displaceationReasoning informatio n 51.1.2 OperationThe functionality of a ready agent is illustrated in 1.1. calculator A and Computer B are connected via a ne devilrk. In mistreat 1 a mobile Agent is going to be dispatched from Computer A towards Computer B. In the mean time Computer A will suspend its execution. Step 2 shows this mobile Agent is now on ne 2rk with its defer and code. In whole step 3 this mobile Agent will reach to its destination, computer B, which will resume its execution. 71.1.3 Strengths and Weaknesses legion(predicate) researchers are now developing methods for improving the technology, with more than standardisation and purify programming environments that whitethorn allow mobile agents to be use in products.It is obvious that the more an exertion gets intelligent, the more it too gets un indicateable and uncontrollable. The main drawback of mobile agents is the security risk involved in using them. 8 9The hobby table shows the major strengths and weaknesses of Agent technolog yStrengthsWeaknessOvercoming vane response timeSecurityReducing cyberspace trafficPerformanceAsynchronous functioning and AutonomyLack of ApplicationsOperating in Heterogeneous EnvironmentsLimited delineationRobust and Fault-tolerant BehaviorStandardizationTable 1.21.2 ApplicationsThe pursuits are the major and approximately widely relevant areas of Mobile AgentDistri hardlyed Computing Mobile Agents rotter be apply in a meshwork using part with resources for their own computations.Collecting selective information A mobile Agent travels approximately the net. On each computer it processes the entropy and hurls the results back to the central server.Software Distribution and Maintenance Mobile agents could be used to distribute software in a network environment or to do tending tasks.Mobile agents and Bluetooth Bluetooth is a technology for short range radio communication. Originally, the companies Nokia and Ericsson came up with the idea. Bluetooth has a nominal rang e of 10 m and 100 m with increase power. 38Mobile agents as Pets Mobile agents are the ideal pets. speak back whatsoeverthing standardised creatures. What if you could have some pets wandering around the internet, choosing where they want to go, exit you if you dont care intimately them or coming to you if you handle them nicely? flock would buy such things wont they? 38Mobile agents and offline tasks1. Mobile agents could be used for offline tasks in the succeeding(a) waya- An Agent is sent out everyplace the internet to do some task.b- The Agent performs its task while the radical computer is offline.c- The Agent returns with its results.2. Mobile agents could be used to simulate a pointorya- Machines in factory are agent driven.b- Agents provide true to life(predicate) info for a simulation, e.g. uptimes and efficiencies.c- Simulation results are used to improve genuine doing or to plan better production lines. 10 11 121.3 Life hertzAn intelligent and autonomous A gent has properties like Perception, Reasoningand Action which form the life cycle of an Agent as shown in 1.2. 6The agent perceives the suppose of its environment, integrates the perception in its knowledge base that is used to derive the coterminous action which is then execute. This generic cycle is a recyclable stimulus generalization as it provides a black-box work out on the Agent and encapsulates specific aspects. The eldest step is the Agent initialisation. The Agent will then start to pop off and whitethorn stop and start again depending upon the environment and the tasks that it tried to accomplish. ulterior on the Agent finished all the tasks that are required, it will end at the completing state. 13 Table 1.3 shows these states.Name of StepDescriptionInitializePerforms one-time fixateup activities.StartStart its job or task.StopStops jobs, notwithstanding intermediate results, joins all threads and stops.CompletePerforms one-time termination activities.Table 1.31.4 Agent Oriented Programming (AOP)It is a programming technique which deals with objects, which have self- jump outing thread of control and can be initiated. We will elaborate on the three main components of the AOP.a- Object Grouping entropy and computation unitedly in a single structural unit called an Object. E really Agent looks like an object.b- Independent Thread of control This means when this developed Agent which is an object, when will be implemented in Boga server, looks like an independent thread. This makes an Agent different from ordinary object.c- Initiation This deals with the execution plan of an Agent, when implemented, that Agent can be initiated from the server for execution. 14 15 16 171.5 Network paradigmsThis section illustrates the traditional distributed computing paradigms like Simple Network trouble Protocol (SNMP) and Remote Procedure vocal (RPC).1.5.1 SNMPSimple Network Management Protocol is a standard for gather statistical selective infor mation about network traffic and the behavior of network components. It is an lotion layer communications protocol that sits above TCP/IP stack. It is a hard-boiled of protocols for managing complex networks. It enables network administrators to manage network performance, find and solve network businesss and plan for network growth. It is basically a request or response type of protocol, communicating heed information between two types of SNMP entities Manager (Applications) and Agents. 18Agents They are compliant devices they store data about themselves in Management Information average (MIB) (Each agent in SNMP maintain a local database of information relevant to network prudence is known as the Management Information Base) and return this data to the SNMP requesters. An agent has properties like Implements blanket(a) SNMP protocol, Stores and retrieves managed data as defined by the Management Information Base and can asynchronously signal an event to the manager.Manager (Application) It issues queries to get information about the status, configuration and performance of external network devices. A manager has the following properties Implemented as a Network Management Station (the NMS), implements full SNMP Protocol, able to Query Agents, get responses from Agents, set variables in agents and acknowledge asynchronous events from Agents. 18 1.3 illustrates an interaction between a manager and an Agent.The agent is software that enables a device to respond to manager requests to view or update MIB data and send traps reporting problems or significant events. It receives marrows and sends a response back. An Agent does not have to wait for order to act, if a serious problem arises or a significant event occurs, it sends a TRAP (a message that reports a problem or a significant event) to the manager (software in a network management station that enables the station to send requests to view or update MIB variables, and to receive traps from an agent). The Manager software which is in the management station sends message to the Agent and receives a trap and responses. It uses User entropy Protocol (UDP, a simple protocol enabling an application to send individual message to other(a) applications. Delivery is not guaranteed, and messages need not be delivered in the same order as they were sent) to carry its messages. Finally, on that point is one application that enables end user to control the manager software and view network information. 19Table 1.4 comprises the Strengths and Weaknesses of SNMP.StrengthsWeaknessesIts design and implementation are simple.It may not be suitable for the management of truly fully grown networks because of the performance limitations of polling.Due to its simple design it can be expanded and also the protocol can be updated to meet future needs.It is not s tumesce suited for retrieving large volumes of data, such as an entire routing table.All major vendors of internetwork hardware, such as b ridges and routers, design their products to resist SNMP, making it very easy to implement.Its traps are unacknowledged and most probably not delivered.not applicableIt provides whole trivial authentication.Not applicableIt does not support explicit actions.Not applicableIts MIB baby-sit is limited (does not support management queries based on object types or jimmys).Not applicableIt does not support manager-to-manager communications.Not applicableThe information it deals with n either luxuriant nor well-organized enough to deal with the expanding modern networking requirements.Not applicableIt uses UDP as a transport protocol. The complex policy updates require a sequence of updates and a reliable transport protocol, such as TCP, allows the policy update to be conducted over a shared state between the managed device and the management station.Table 1.41.5.2 RPCA removed(p) influence call (RPC) is a protocol that allows a computer program running on one force to cause code t o be penalize on another server without the programmer needing to explicitly code for this. When the code in question is write using object-oriented principles, RPC is sometimes referred to as unlike invocation or inappropriate method invocation. It is a popular and powerful technique for constructing distributed, client-server based applications. An RPC is initiated by the caller (client) sending a request message to a remote system (the server) to execute a certain procedure using arguments supplied. A result message is returned to the caller. It is based on extending the notion of conventional or local procedure calling, so that the called procedure need not live in the same address space as the calling procedure. The two processes may be on the same system, or they may be on different systems with a network connecting them. By using RPC, programmers of distributed applications block the details of the interface with the network. The transport independence of RPC isolates the application from the physical and arranged elements of the data communications mechanism and allows the application to use a grade of transports. A distributed computing using RPC is illustrated in 1.4.Local procedures are executed on Machine A the remote procedure is actually executed on Machine B. The program executing on Machine A will wait until Machine B has completed the operation of the remote procedure and then continue with its program logic. The remote procedure may have a return value that continuing program may use immediately.It intercepts calls to a procedure and the following happensPackages the name of the procedure and arguments to the call and transmits them over network to the remote appliance where the RPC server id running. It is called Marshalling. 20RPC decodes the name of the procedure and the parameters.It makes actual procedure call on server (remote) mold.It packages returned value and turnout parameters and then transmits it over network back to the machine that made the call. It is called Unmarshalling. 201.6 relation between Agent technology and network paradigmsConventional Network Management is based on SNMP and often run in a centralised manner. Although the centralised management mount gives network administrators a flexibleness of managing the totally network from a single place, it is prone to information bottleneck and profuse processing load on the manager and heavy usage of network bandwidth.Intelligent Agents for network management tends to monitor and control networked devices on range and consequently save the manager capacity and network bandwidth. The use of Intelligent Agents is due to its major advantages e.g. asynchronous, autonomous and heterogeneous etc. while the other two contemporary technologies i.e. SNMP and RPC are lacking these advantages. The table below shows the coincidence between the intelligent agent and its contemporary technologiesPropertyRPCSNMPIntelligent AgentCommunicationSynchro nousAsynchronousAsynchronousProcessing Power little Autonomy much Autonomous but less than AgentMore AutonomousNetwork supportDistributedCentralisedHeterogeneousNetwork extend ManagementHeavy usage of Network BandwidthLoad on Network traffic and heavy usage of bandwidthReduce Network traffic and rotational latencyTransport ProtocolTCPUDPTCPPacket size NetworkOnly address can be sent for request and data on replyOnly address can be sent for request and data on replyCode and execution state can be moved around network. (only code in courting of weak mobility)Network MonitoringThis is not for this purposeNetwork delays and information bottle neck at centralised management stationIt gives flexibility to analyse the managed knobs locallyTable 1.5Indeed, Agents, mobile or intelligent, by providing a new paradigm of computer interactions, give new options for developers to design application based on computer connectivity.20Chapter 2 acquire Paradigms2.1 Knowledge Discovery in Databas es (KDD) and Information Retrieval (IR)KDD is defined as the nontrivial process of identifying valid, novel, potentially useful and ultimately understandable patterns in data (Fayyad, Piatet deliver-Shapiro and metalworker (1996)). A closely related process of IR is defined as the methods and processes for searching relevant information out of information systems that contain exceedingly large numbers of documents (Rocha (2001)).KDD and IR are, in fact, highly complex processes that are strongly affected by a wide range of factors. These factors entangle the needs and information seeking characteristics of system users as well as the tools and methods used to search and retrieve the structure and size of the data set or database and the nature of the data itself. The result, of course, was increasing numbers of organizations that possessed very large and continually growing databases but only elementary tools for KD and IR. 21 twain major research areas have been developed in res ponse to this problem* Data warehousingIt is defined as Collecting and cleaning transactional data to make it available for online depth psychology and termination support. (Fayyad 2001, p.30) Data excavationIt is defined as The application of specific algorithmic programs to a data set for purpose of extracting data patterns. (Fayyad p. 28)2.2 Data MiningData mine is a statistical term. In Information Technology it is defined as a breakthrough of useful summaries of data.2.2.1 Applications of Data MiningThe following are examples of the use of data mining technologyPattern of traveller behavior exploit Manage the sale of discounted seats in planes, rooms in hotels.Diapers and beer placard those bosss who buy diapers are more likely to buy beer than average allowed supermarkets to place beer and diapers nearby, knowing legion(predicate) a(prenominal) customers would walk between them. Placing potato chips between increased sales of all three items.Skycat and Sloan Sky Sur vey Clustering sky objects by their radiation levels in different bands allowed astronomers to distinguish between galaxies, nearby stars, and many other kinds of celestial objects.Comparison of genotype of people With/without a condition allowed the discovery of a set of genes that together account for many outcome of diabetes. This sort of mining will become much more important as the human genome is constructed. 22 23 242.2.2 Communities of Data MiningAs data mining has become recognised as a powerful tool, some(prenominal) different communities have laid claim to the subjectStatisticsArtificial Intelligence (AI) where it is called Machine learnednessResearchers in clustering algorithmsVisualisation researchersDatabases When data is large and the computations is very complex, in this context, data mining can be cerebration of as algorithms for executing very complex queries on non-main-memory data.2.2.3 Stages of data mining processThe following are the different stages of da ta mining process, sometimes called as a life cycle of data mining as shown in 2.1Data gathering Data warehousing, web crawling.Data purifying Eliminate errors and/or bogus data e.g.Patients fever = 125oC.3- Feature line Obtaining only the interesting dimensions of the data e.g. data acquired is probably not useful for clustering celestial objects as in skycat.4- Pattern extraction and discovery This is the stage that is often thought of as data mining and is where we shall deoxidise our efforts.5- Visualisation of the data6- Evaluation of results Not every discovered fact is useful, or even true Judgment is necessary before following the softwares conclusions. 22 23 242.3 Machine LearningThere are five major techniques of machine teaching in Artificial Intelligence (AI), which are discussed in the following sections.2.3.1 Supervised LearningIt relies on a teacher that provides the stimulation data as well as the want solution. The learning agent is accomplished by showing it examples of the problem state or attributes along with the desired create or action. The learning agent makes a prediction based on the inputs and if the output differs from the desired output, then the agent is adjusted or adapted to produce the correct output. This process is repeated over and over until the agent learns to make accurate classifications or predictions e.g. Historical data from databases, demodulator logs or trace logs is often used as readiness or example data. The example of manage learning algorithm is the ending Tree, where on that point is a pre-specified repoint variable. 25 52.3.2 Unsupervised LearningIt depends on input data only and makes no demands on knowing the solution. It is used when learning agent needs to recognize similarities between inputs or to identify features in the input data. The data is presented to the Agent, and it adapts so that it partitions the data into collections. This process continues until the Agents place the same gr oup on successive passes over the data. An unsupervised learning algorithm performs a type of feature detection where important common attributes in the data are extracted. The example of unsupervised learning algorithm is the K-Means Clustering algorithm. 25 52.3.3 Reinforcement LearningIt is a kind of supervised learning, where the feedback is more general. On the other hand, there are two more techniques in the machine learning, and these are on-line learning and off-line learning. 25 52.3.4 On-line and Off-line LearningOn-line learning means that the agent is adapting while it is working. Off-line involves thriftiness data while the agent is working and using the data later to train the agent. 25 5In an intelligent agent context, this means that the data will be gathered from situations that the agents have experienced. Then augment this data with information about the desired agent response to build a reading data set. Once this database is ready it can be used to spay the b ehaviour of agents. These approaches can be combined with any two or more into one system.In order to develop Learning Intelligent Agent(LIAgent) we will combine unsupervised learning with supervised learning. We will rise LIAgents on Iris dataset, Vote dataset about the polls in regular army and two medical datasets namely Breast and Diabetes. 26 See Appendix A for all these four datasets.2.4 Supervised Learning ( finis Tree ID3)Decision steers and finality rules are data mining methodologies applied in many real world applications as a powerful solution to classify the problems. The goal of supervised learning is to create a classification model, known as a classifier, which will predict, with the values of its available input attributes, the class for some entity (a given sample). In other words, classification is the process of assigning a discrete label value (class) to an unlabeled record, and a classifier is a model (a result of classification) that predicts one attribute -class of a sample-when the other attributes are given. 40In doing so, samples are divided into pre-defined groups. For example, a simple classification might group customer billing records into two specific classes those who pay their bills within thirty age and those who takes longer than thirty days to pay. Different classification methodologies are applied today in almost every discipline, where the task of classification, because of the large nub of data, requires automation of the process. Examples of classification methods used as a part of data-mining applications include classifying trends in financial market and identifying objects in large image databases. 40A particularly efficient method for producing classifiers from data is to generate a last guide. The finality- guide supportation is the most widely used logic method. There is a large number of conclusion- head induction algorithms described primarily in the machine-learning and applied-statistics literature . They are supervised learning methods that construct finding heads from a set of input-output samples. A typical conclusion-tree learning system adopts a top-down strategy that searches for a solution in a part of the search space. It guarantees that a simple, but not necessarily the simplest tree will be found. A decision tree consists of nodes, where attributes are tested. The outgoing branches of a node correspond to all the possible sequels of the test at the node. 40Decision trees are used in information theory to determine where to class data sets in order to build classifiers and regression trees. Decision trees perform induction on data sets, generating classifiers and prediction models. A decision tree examines the data set and uses information theory to determine which attribute contains the information on which to base a decision. This attribute is then used in a decision node to split the data set into two groups, based on the value of that attribute. At each subse quent decision node, the data set is split again. The result is a decision tree, a collection of nodes. The leaf nodes represent a final classification of the record. ID3 is an example of decision tree. It is kind of supervised learning. We used ID3 in order to print the decision rules as its output. 402.4.1 Decision TreeDecision trees are powerful and popular tools for classification and prediction. The draw of decision trees is due to the fact that, in contrast to neural networks, decision trees represent rules. Rules can readily be expressed so that military man can understand them or even directly used in a database access language like SQL so that records falling into a particular category may be retrieved. Decision tree is a classifier in the form of a tree structure, where each node is eitherLeaf node indicates the value of the target attribute (class) of examples, or Decision node specifies some test to be carried out on a single attribute value, with one branch and sub- tree for each possible outcome of the test. Decision tree induction is a typical inductive approach to learn knowledge on classification.The key requirements to do mining with decision trees are Attribute value description Object or case must be expressible in terms of a doctor collection of properties or attributes. This means that we need to discretise continuous attributes, or this must have been provided in the algorithm. Predefined classes (target attribute values) The categories to which examples are to be assigned must have been established beforehand (supervised data). Discrete classes A case does or does not belong to a particular class, and there must be more cases than classes.* Sufficient data Usually hundreds or even thousands of planning cases. A decision tree is constructed by looking for regularities in data. 27 52.4.2 ID3 algorithmic programJ. Ross Quinlan originally developed ID3 at the University of Sydney. He first presented ID3 in 1975 in a book, Machine Learn ing, vol. 1, no. 1. ID3 is based on the Concept Learning System (CLS) algorithm. 28function ID3Input (R a set of non-target attributes,C the target attribute,2.4.3 Functionality of ID3ID3 searches through the attributes of the training instances and extracts the attribute that best separates the given examples. If the attribute abruptly classifies the training sets then ID3 stops otherwise it recursively operates on the m (where m = number of possible values of an attribute) partitioned subsets to get their best attribute.The algorithm uses a greedy search, that is, it picks the best attribute and never looks back to reconsider in the beginning choices. If the dataset has no such attribute which will be used for the decision then the result will be the misclassification of data.Entropy a banner of homogeneity of the set of examples. 5Entropy(S) = pplog2 pp pnlog2 pn (1)(2)2.4.4 Decision Tree RepresentationA decision tree is an arrangement of tests that prescribes an appropriat e test at every step in an analysis. It classifies instances by sorting them down the tree from the root node to some leaf node, which provides the classification of the instance. Each node in the tree specifies a test of some attribute of the instance, and each branch go down from that node corresponds to one of the possible values for this attribute. This is illustrated in 2.3. The decision rules can also be obtained from ID3 in the form of if-then-else, which can be use for the decision support systems and classification.Given m attributes, a decision tree may have a maximum height of m. 2952.4.5 Challenges in decision treeFollowing are the issues in learning decision trees find out how deeply to grow the decision tree. discussion continuous attributes.Choosing an appropriate attribute selection measure.Handling training data with missing attribute values.Handling attributes with differing costs andImproving computational efficiency.2.4.6 Strengths and WeaknessesFollowing are th e strengths and weaknesses in decision treeStrengthsWeaknessesIt generates understandable rules.It is less appropriate for estimation tasks where the goal is to predict the value of a continuous attribute.It performs classification without requiring much computation.It is prone to errors in classification problems with many class and relatively small number of training examples.It is suitable to handle both continuous and categorical variables.It can be computationally expensive to train. The process of growing a decision tree is computationally expensive. At each node, each candidate splitting playing area must be sorted before its best split can be found. Pruning algorithms can also be expensive since many candidate sub-trees must be formed and compared.It provides a clear distinction of which fields are most important for prediction or classification.It does not treat well non-rectangular regions. It only examines a single field at a time. This leads to rectangular classificati on boxes that may not correspond well with the actual distribution of records in the decision space.Table 2.12.4.7 ApplicationsDecision tree is generally suited to problems with the following characteristicsa. Instances are described by a fixed set of attributes (e.g., temperature) and their values (e.g., hot).b. The easiest situation for decision tree learning occurs when each attribute takes on a small number of part possible values (e.g., hot, mild, cold).c. Extensions to the basic algorithm allow handling real-valued attributes as well (e.g., a floating point temperature).d. A decision tree assigns a classification to each example.i- Simplest case exists when there are only two possible classes (Boolean classification).ii- Decision tree methods can also be easily extended to learning functions with more than two possible output values.e. A more substantial extension allows learning target functions with real-valued outputs, although the application of decision trees in this set ting is less common.f. Decision tree methods can be used even when some training examples have unknown quantity values (e.g., humidity is known for only a fraction of the examples). 30 larn functions are either represented by a decision tree or re-represented as sets of if-then rules to improve readability.2.5 Unsupervised Learning (K-Means Clustering)Cluster analysis is a set of methodologies for automatic classification of samples into a number of groups using a measure of association, so that the samples in one group are similar and samples belonging to different groups are not similar. The inpu

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