How children develop their interpersonal skills Essay

How children develop their interpersonal skills - Essay Example Infants may cry to communicate their needs to their parents; they learn that when they cry they are fed and thus carry on with this routine. Eventually, with the correct training and assistance, children will learn that making use of words or expressions to communicate their needs could be their most effective technique (Hersen, 2011). This essay discusses how children develop their interpersonal skills and how digital technologies or electronic devices can be used to enhance children’s learning experience. Nevertheless, interpersonal skills are not confined with social tact like being courteous and well-mannered. Children’s social and interpersonal skills develop as they acquire communication skills. Numerous parents are anxious that their children are deficient in interpersonal or social skills, but this is a misperception in most instances. The essentials of social behavior originate from the emotional area of the brain, which is a vital determinant of morality, compassion, and fellow feeling (Hersen, 2011). Babies usually fret when they hear another baby screaming, for they know that someone is disgruntled. Hence several antecedents of interpersonal skills are perhaps wired to the brain, but experiences also affect the ability of children to recognize, understand, and react to others’ needs (Hersen, 2011). Focusing jointly on something is an early sign of interpersonal skills. Babies who often draw people’s attention to fascinating objects at nine months are more probable to be classified as socially capable at roughly two years. By their first year babies want or prefer people who support or give comfort to other people (Mathieson, 2004). Genuine fellow feeling, the capacity to understand and reflect on the feelings of others, is manifested by age five. In this young age, children exhibit remarkable improvements in self-discipline. Children who have greater self-discipline also manifest greater fellow feeling and more advanced sense of right and

Criminal Law U4IP Research Paper Example | Topics and Well Written Essays - 500 words

Criminal Law U4IP - Research Paper Example It usually arises when someone who was committed to supervise or monitor someone else money or property steals the money for personal gain. The stealing of the money is a desecration of a specialty of trust which results in a distinctive crime. Embezzlement usually arises due to several circumstances such as, a bank teller has permitted access to client money hence trusted to handle the money or employees and officers of companies can also misuse the companys funds since they are in charge of running the company (Fjeldstad, 2003).. Nevertheless, this does not imply that it has to be done by employing but any kind of relationship where by trust is given to somebody else to manage your property. The following three elements that are essential for an event to be considered for embezzlement charges. In case any of the three sections are not satisfied, these charges will not apply to these cases. If the person was entrusted with ownership of property that belongs to somebody. Secondly, that the person hid or took the property or someway converted it to his own without the owners permission to do so. Thirdly, they had planned to do these crimes to enduringly take ownership pleasure away from the owner (Francken, 2009). There are a limitless number of ways that someone could oblige to the crime of embezzlement of public funds. Siphoning is a good example of embezzlement crime. This is usually accomplished by people who work in restaurants or stores. They invent a way to getting money using the register without any discrepancies between the records in the computer and a drawer. The item is not entered into the calculator section of the register but they keep a record of how much they pocketed after their shift. Lapping is a crime that is found in parts of the business that takes incoming payments from vendors and customers. Someone working for example church could use bank deposit for many companies and alter the distribution of the

Feminist organizations Essay Example | Topics and Well Written Essays - 500 words

Feminist organizations - Essay Example Accordingly, the following brief analysis will make an inventory of each of these aspects as a function of further delineating and defining the National Organization for Women. Firstly, the website which was analyzed denoted that fact that the ultimate goal of the group was to take immediate action for the equality of women. Although this is specifically tied to the manner in which women’s issues and women’s rights are exhibited within the United States, the group also seeks to effect change in various regions around the globe on behalf of women and their needs/rights. The mission statement itself further seeks to hone the areas of focus that NOW seeks to integrate with. Accordingly, the mission statement is as follows, â€Å"the purpose of NOW is to take action to bring women into full participation in the mainstream of American society now, exercising all privileges and responsibilities thereof in truly equal partnership with men† (NOW 1). Similarly, with regards to the political orientation of the group, the website itself promotes the understanding that the group is specifically interested in engaging in actions that promote women’s rights, feminism, anti-racism, ending homophobia, promoting LGBT rights, and promoting reproductive rights. As might be easily inferred, the group generally promotes a more liberal interpretation of the political paradigm; siding more often than not with liberal and progressive elements within the United States and typically aligning with the Democratic party. However, with that being said, it must also be understood that NOW does not promote any one single political party; rather, their interests are supra-political and the group only sides with more progressive and liberal ideologies due to the fact that these most specifically represent the goals and ends that the group is trying to effect. With such a constraining level of missions and values, the group is invariably at

An Ethical Dilemma Essay Example for Free

An Ethical Dilemma Essay There exists a strong link between the way and pace of life in a society and eating habits of the individuals. No matter how diverse and sophisticated the cuisine of a certain culture might be, todays hyper moving tempo necessitates cheaper and faster food. That is where the fast food sector steps in, saving the day. At first glance, they appear to be life savers with their affordable menus. However, when we take a closer look and observe the long-term effects of fast food on individuals, we are faced with health problems such as obesity and heart diseases. Nevertheless, fast food firms also do a very good job in marketing their products to carefully targeted audiences, especially children. This paper will focus on the case of McDonalds and argue how ethical it is for them to advertise for children directly, examining the issue from the perspective of social responsibility. The spread of McDonalds in other parts of the world creates mixed feelings in some countries, and people even claim that the McDonalds and the distorted image of Americanization is harmful for their culture and societies. This opinion is especially valid in Europe, but surprisingly McDonalds is welcome in Asia. What is a common reaction in all countries hosting McDonalds, including its homeland America, is the attitude taken towards the effects of McDonalds on eating habits and the following negative consequences. There are plenty of fast food advertisements in North America and this industry has especially become a part of the life of families with children. Starting from very little ages, children are used to eating this good-tasting, well-marketed and fun menus which usually come along with a toy for free; and their tastes and eating habits are influenced. Even though fast food companies have started to offer â€Å"light† menus and food with less fat and calories, they do not offer the best menus for children. They continue the habit of eating fast food as they go into adolescence and adulthood, and become another candidate for an obese person with various diseases resulting from being overweight. â€Å"Overweight children do tend to become obese adults, putting themselves at a much greater risk, and at a much earlier age, for chronic illnesses such as diabetes and cardiovascular disease† (Dalton, 2004, p. 2). One out of three children in the United States is either overweight or at serious risk of becoming so. The number of overweight children ages six to nineteen has tripled within three decades; the rate of overweight preschool children is nearly as great. The accelerating rate indicates that the current generation of children will grow into the most obese generation of adults in history. (Dalton, 2004, p. 2) Although parents are aware that food sold at McDonalds is not very healthy, they are misled by the fact that it is affordable and makes their children happy. What is more, some parents do not have very healthy eating habits either; so one should not be surprised in seeing their children liking McDonalds menus. Moreover, it is not easy to resist the tempting advertisements. â€Å"Some might say that no one is forcing parents to buy these products or foods for their children. But, these ads position the products as must haves. Even if their parents do not buy them the products, children are influenced† (How to Prevent Childhood Obesity. com, 2009). â€Å"Experts name Ray Kroc, founder of McDonalds franchise and Walt Disney as the pioneers of child-focused marketing, since they first recognized children as a separate marketing demographic from adults in the 1960s† (Veracity, D. , 2008). Today, we cannot help but wonder how the managers of McDonalds feel about the harsh criticisms that the company gets for advertising and promoting unhealthy food for children. From a business point of view, the company has done a great job since it was founded by Ray Kroc in 1955, generated enormous profits and even became a better known brand than Coca-Cola (Veracity, D. , 2008). Competition in the fast food sector is harsh, so McDonalds heavily invests in high quality advertisements aimed at targeting the right audience, using celebrities and partnerships with other brands, or cartoon characters in their campaigns if possible. They build playgrounds in their stores, which serve as a socializing place for children to meet other kids and have fun. McDonalds not only influences American children and families, but also exports its food, image and advertisements to the rest of the world. They have opened branches in almost all parts of the world, and keep expanding despite the economic recession. They are â€Å"optimistic about business prospects in China and plan to open about 500 stores in the country in three years† (Yan, F. Li, H. , 2009). This gives an important hint about the tastes and habits of the growing generation of children and it is not difficult to foresee that the global influence of McDonalds will intensify in the coming years, despite all criticisms that it is unethical to promote unhealthy food to children. How ethical is the advertising strategy of McDonalds really? Are the managers of McDonalds actually guilty, or is everyone being too harsh and oversensitive? Even though it is normal for a company to hold its own rights and benefits before everything, if it is as influential and global as McDonalds, it also has some moral and ethical responsibilities and should consider the social consequences of its actions as well as making profits and opening new stores. One of the most important causes of childhood obesity is lack of exercise, so it might not be totally fair to blame McDonalds and other fast food chains for obese children. On the one hand, the McDonalds culture heavily contributes in a bad way to developing irregular eating habits. But on the other hand, they cannot be the only ones to blame, as children and their parents are increasingly becoming computer and TV addicts, engaging in very little physical activity. When coupled with fast food consumption, health problems become inescapable. What is the solution to this moral problem then? It is obvious that a company this successful will not quit this business or abandon its strategy. However, McDon can at least modify its advertising approach slightly and recommend doing exercises as the underlying message after having a good McDonalds meal. They can include famous sportsmen in their advertisements and encourage children to engage in sports. They can give out toys associated with sports brands, even organize sports competitions for children with awards, sponsored by major brands like Nike or Adidas. These are just a few suggestions, and there is no doubt that professionals designing McDonalds marketing strategy can work wonders with this idea if they want to. This way, children can learn to associate the consumption of fast food with exercise in their minds and be convinced that they must be physically active in order to burn those calories taken at McDonald and be healthy. In conclusion, if McDonalds and other fast food chains would alter their advertisement campaigns so as to include the theme of more exercise and sports, they would have been more socially responsible. This way, even though they do not sell the most healthy meals, their customers, especially children would know that they have to pay a price for eating a high calorie and high fat meal by doing more exercise. They would also associate fast food meals with the energy and dynamism of sports, which also makes individuals happy. Therefore, this can be a very good formula for McDonalds to keep its happy customers all over the world and appease an angry crowd of protesters who argue that McDonalds advertisements are unethical. References Dalton, S. (2004). Our Overweight Children: What Parents, Schools, and Communities Can Do to Control the Fatness Epidemic. Berkeley, CA: University of California Press. Should there be Ethical Issues with Fast Food Companies Advertising to Children?How to Prevent Childhood Obesity. com. Retrieved March 25, 2009, from http://www. howtopreventchildhoodobesity. com/ethicalissues- fastfoodadvertisements. html Veracity, D. (2008, July 13). Americas Fast Food Giants Perfect the Art of Selling Junk Food to Children. Organic Consumers Association. Retrieved March 25, 2009, from http://www. organicconsumers. org/articles/article_1092. cfm Yan, F. Li, H. (2009, February 18). McDonalds eye 500 stores in China in 3 years. Reuters. Retrieved March 25, 2009, from http://www. reuters. com/article/ousiv/idUSTRE51H13F20090218

Innovation in strategy

Innovation in strategy This essay evaluates the role of innovation in strategy, and explores the ways management can promote it in organisations. It first looks at the nature of innovation, and examines its importance in current economic and social conditions. It then sets strategy in context, defining it primarily in terms of competitive advantage that is, as a search for capabilities which allow allows an organisation to meet consumers needs better than its rivals. It then investigates why, exactly, innovation is often seen as a key component of strategy. It comes up with two key reasons: its capacity to generate a sustainable competitive advantage for business organisations; and its ability to aid organisations in preventing strategic drift. As a result of these benefits, strategies which are centred upon innovation can add real value to an organisations value proposition, and consequently can substantially improve business performance. The essay then turns to look at the ways that management can promo te innovation in organisations. For this, it turns to the worlds most famous management thinker Peter Drucker and the worlds most innovative company Apple Inc. for guidance on theory and practice respectively. Having thus established the importance of the role of innovation for strategy, and the ways in which management can promote it in organisations, the essay then considers some limitations. In particular, it looks at the possible advantages of strategic drift; and also the other aspects of strategy beyond innovation which must be considered by an organisation. The essay thus concludes that innovation is a necessary component of a successful strategy in that it is able to generate a sustainable competitive advantage for a business but that it is not sufficient in and of itself: an organisation must consider more than innovation if it is to develop an effective strategy. Innovation is usually defined as ‘the successful commercial exploitation of new ideas or simply as ‘the successful implementation of new ideas. This encompasses ideas that are ‘new to the world, ‘new to an industry or merely ‘new to a particular firm (Gabriel, 2008, p. 146). The prominence given to the role of innovation in strategy is to a large extent the result of the prevailing social and economic conditions. In what Peter Drucker the most influential management thinker of the second-half of the twentieth century termed the ‘knowledge economy that has emerged due to the rise of the service industry and decline of manufacturing since the end of the Second World War, business organisations have increasingly had to react to change more rapidly if they wish to succeed in the marketplace (Drucker, 1992, p. 263). Indeed, so important is the successful implementation of new ideas that Drucker famously reflected that: ‘Business has only two bas ic functions marketing and innovation (Kotler Armstrong, 2008, p. 40). In other words, a business organisation must first create a customer, but consequently that business must constantly adapt to provide the necessary goods and services to keep them making a profit: they must pursue innovation both to survive and to thrive. Having explored the nature of innovation, it is useful now to define what is meant by ‘strategy, and examine briefly why it matters. The nature of strategy has traditionally been a contentious issue. A helpful starting point for understanding the concept is found in Anthony Henrys (2008) Understanding Strategic Management, where he provides a synopsis of forty years of heated debate on the issue. He first outlines that, ‘there is agreement that the role of strategy is to achieve competitive advantage for an organisation. He then continues: ‘Competitive advantage may usefully be thought of as that which allows an organisation to meet consumers needs better than its rivals . . . [and] its source may derive from a number of factors including its products or services, its culture, its technological know-how, and its processes (Henry, 2008, p. 4). It is an important issue for a business because a strategy which can enable a sustainable competitive advantage will allow an organisation to generate super-normal returns, and will have a distinct impact on overall organisational performance: an effective strategy can add value (Kay, 1995). Herein lies the essence of the role of innovation in strategy it is often a key component of a sustainable competitive advantage. For instance, Grant (2005, p. 513) has observed from empirical evidence based on such successful companies as 3M, Wal-Mart, and Toyota that, ‘ultimately, the only sustainable competitive advantage is the ability to create new sources of competitive advantage. Firms with a fixed commitment to innovation seem to prosper in the modern ‘knowledge economy. For instance, Apple a company which this essay examines in more depth below has become synonymous with strategic innovation. In Fortunes Americas Most Admired Companies 2008, Apple topped the chart. A senior commentator reflected on this development with the following remark: Apple not only takes the No. 1 slot on this years list of Americas Most Admired Companies but also tops the global survey and wins the highest marks for innovation too. Thats probably no coincidence. In an industry that changes every nanosecond, the 32-year-old company has time and again innovated its way out of the doldrums. Rivals always seem to be playing catch-up. (Fisher, 2008) Moreover, innovation can be key to preventing ‘strategic drift. Strategic drift is the tendency for strategies to develop incrementally on the basis of historical and cultural influences but to fail to keep pace with a changing environment (Johnson, Scholes, Whittington, 2008, p. 179). This is what happened to Sainsburys who were one of the most successful food retailers in the world until the early 1990s, using a tried-and-tested formula of selling high quality food at reasonable prices. Its strategy consisted of gradually extending its product lines, enlarging its stores, and expanding its geographical coverage; but under no circumstances would it deviate from its traditional ways of doing business (Johnson, Scholes, Whittington, 2008, p. 179). However, during Sainsburys period of strategic drift, its rival Tesco followed a policy of ruthless innovation developing Club-Card marketing, building a successful on-line retailing capability, and implementing new ideas to radica lly reduce its distribution costs (IMD, 2008). By having a strategy centred on innovation, therefore, Tesco was able to both establish a competitive advantage and avoid strategic drift. It was, in short, able to develop a strategy which added value, and which made the business organisation much more profitable. So where can business organisations look for innovation how can they promote it more effectively? Peter Drucker has suggested that there are seven areas where companies should look for such opportunities. These have been expertly surmised by Hindle (2008, p. 105), as being: ‘the unexpected success that is rarely dissected to see how it has occurred; any incongruity between what actually happens and what was expected to happen; any inadequacy in a business process that is taken for granted; a change in industry or market structure that takes everyone by surprise; demographic changes caused by things like wars, migrations or medical developments (such as the birth-control pill); changes in perception and fashion brought about by changes in the economy; and changes in awareness caused by new knowledge. Moreover, although it is often the case that ‘innovation has been used interchangeably with the term â€Å"creativity† (Forrester 1993, p. 3; cited in Thompson McHugh , 2002, p. 255), Drucker insists that this ought never to limit a business, claiming that: ‘There are more ideas in any organization, including businesses, than can possibly be put to use (Drucker, 1964, p. 188). Across the literature on innovation, there seems to be a general agreement with this approach set out above: that the opportunities for innovation are multitudinous, and that by paying attention to such factors organisations can develop strategies which can lead to a sustainable competitive advantage and prevent strategic drift. A brief case-study of Apple will help demonstrate how this theory outlined above works in practice, and help us to better understand the ways management can promote innovation in organisations. First, Apple appreciates that innovation is an inexact science: as the CEO and cofounder of Apple, Steve Jobs, puts it: ‘You cant ask people what they want if its around the next corner rather you have to simply provide what you think they might want (Morris, 2008). To guide them, Apple looks to the areas mentioned by Drucker above to gain insights into such potential needs and wants. Apple employees in particular focus on the inadequacies in every-day technology processes that are currently taken for granted, and innovate in these areas. New-product development, according to Apple sources, occurs as a result of conversations such as: ‘What do we hate? (Our cellphones.) What do we have the technology to make? (A cellphone with a Mac inside.) What would we like to own? (You guessed it, an iPhone) (Morris, 2008). Moreover, at Apple, innovation is centred on producing technology the employees really want: as Jobs says, ‘One of the keys to [innovation at] Apple is that we build products that really turn us on (Morris, 2008). This results in an organisation thoroughly committed to the successful commercial exploitation of new ideas at a strategic, operational and tactical level. Indeed Morris (2008), observing the culture of innovation at Apple, has pointed out that: ‘You wont find that word on a placard or a piece of propaganda at One Infinite Loop, Apples headquarters . . . there innovation is a way of life. It is this culture that ‘provides the push to overcome design and engineering obstacles, [and] to bring projects in on time (Morris, 2008). Thus a commitment to a strategy of innovation should foster a culture which reflects this aim of management, as this can lead to the organisation innovating more effectively. Finally, it is important to note the impact of a strategy centred on innovation upon the performance of Apple. It has astounded commentators with one perplexed writer asking: ‘who knew [Apple] could build a . . . [successful] company on the strength of a portable jukebox and a computer with a single-digit market share? (Elmer-DeWitt, 2008). Indeed, the company has been monetarily hugely successful as a result of the innovation it has pioneered. In the 5 years ending in March 2008, sales of Apple wares tripled to $24 billion; and profits rose to $3.5 billion, from a mere $42 million only five years before. Morris (2008) sums up the position of Apple thus: [It] set the gold standard for corporate America with an entirely new business model: creating a brand, morphing it, and reincarnating it to thrive in a disruptive age. . . Apple has demonstrated how to create real, breathtaking growth by dreaming up products so new and ingenious that they have upended one industry after another: consumer electronics, the record industry, the movie industry, video and music production. Thus innovation can play a key role in an organisations strategy, and it can often be effectively promoted by following the theory of Drucker and the practices of Apple. Nevertheless, it is important to note that there are limitations on the role of innovation in strategy. First, ‘strategic drift may not be such a bad thing after all. This is a view outlined by John Kay (2009) in his article History vindicates the science of muddling through. He contrasts the views of the American political scientist Charles Lindblom (published in 1959) with those of Dr H. Igor Ansoff. Lindblom supported a view of incremental adaptation by organisations to changes in their environment; Ansoff proposed a design-orientated, purposive approach to strategy. However, Kay then points that in terms of the organisational case-studies used to support each view Saint-Gobain for Lindbolm; the US conglomerates TRW and Litton for Ansoff the clear winner emerges as Saint-Gobain, a company which adopted a q uasi-strategic drift approach to their strategy, which is still going strong while the other companies have suffered catastrophic failure. Thus, it seems that sometimes simply ‘muddling through can constitute an effective strategy perhaps a firm commitment to innovation is not necessary after all. Moreover, innovation is not the sole component of an effective strategy, and it never can be. Organisations must consider a range of other issues. For instance, business organisations ought to consider issues highlighted by Michael Porters ‘Five Forces model. This shows how the strategic situation of a company can be established by investigating the power of suppliers, the power of buyers, the threat of substitution, the threat of new entrants, as well as the degree of competitive rivalry between the industrys firms. An organisation must consider innovation if it is to ensure that it continues to have an effective strategy in the medium to long term, but it must also pay attention to these other aspects of strategy innovation is necessary, but it is not sufficient. Thus innovation is a necessary component of a successful strategy in that it is able to generate a sustainable competitive advantage for a business. However, it is not sufficient: an organisation must consider other issues as well as innovation if it is to develop an effective strategy. Nevertheless, by following the theory of Drucker and learning from the practices of Apple, management can promote innovation in organisations. And if this is done effectively, innovation can play a key role in what every business organisation seeks: a competitive strategy which adds real value. References: Drucker, P. (1964). Managing for results: economic tasks and risk-taking decisions. California: Harper Row. Drucker, P. (1992). The age of discontinuity: guidelines to our changing society. 2nd ed. New Jersey: Transaction Publishers. Elmer-DeWitt, P. (2008, March 3). Americas Most Admired Companies 2008. Retrieved November 24, 2009, from Fortune Web site: http://money.cnn.com/galleries/2008/fortune/0802/gallery.mostadmired_top20.fortune/index.html Fisher, A. (2008, March 3 ). Innovation Rules. Retrieved November 24, 2009, from Fortune Web site: http://money.cnn.com/2008/02/29/news/companies/fisher_amac.fortune/index.htm 2008 Gabriel, Y. (2008). Organizing Words: A Critical Thesaurus for Social and Organization Studies. Oxford: Oxford University Press. Grant, R. M. (2005). Contemporary strategy analysis. 5th ed. London: Wiley-Blackwell. Henry, A. (2008). Understanding Strategic Management. Oxford: Oxford University Press. Hindle, T. (2008). Guide to Management Ideas and Gurus. London: Profile Books. IMD. (2008). Tesco: Keeping the Hard Discounters at Bay? Switzerland: IMD International. Johnson, G., Scholes, K., Whittington, R. (2008). Exploring corporate strategy: text cases. 8th ed. Harlow: Pearson Education. Kay, J. (1995). Foundations of corporate success: how business strategies add value. Oxford: Oxford University Press. Kay, J. (2009, March 15). History vindicates the science of muddling through. Retrieved December 13, 2009, from John Kay Web sit: http://www.johnkay.com/in_action/604 Kotler, P., Armstrong, G. (2008). Principles of Marketing. 13th ed. London: Pearson Education Ltd. Morris, B. (2008, March 17). What makes Apple Golden? Retrieved October 27, 2009, from Fortune Web site: http://money.cnn.com/2008/02/29/news/companies/amac_apple.fortune/index.htm?postversion=2008030309 Thompson, P., McHugh, D. (2002). Work Organisations. 3rd ed. London: Palgrave.

Intelligent Software Agent

Intelligent Software Agent Chapter 1 Intelligent Software Agent 1.1 Intelligent Agent An Agent can be defined as follows: â€Å"An Agent is a software thing that knows how to do things that you could probably do yourself if you had the time† (Ted Seller of IBM Almaden Research Centre). Another definition is: â€Å"A piece of software which performs a given task using information gleaned from its environment to act in a suitable manner so as to complete the task successfully. The software should be able to adapt itself based on changes occurring in its environment, so that a change in circumstances will still yield the intended results† (G.W.Lecky Thompson). [1] [2] [3] [4] An Intelligent Agent can be divided into weak and strong notations. Table 1.1 shows the properties for both the notations. Weak notation Strong notation Autonomy Mobility Social ability Benevolence Reactivity Proactivity Rationality Temporal continuity Adaptivity Goal oriented Collaboration Table 1.1 1.1.1 Intelligency Intelligence refers to the ability of the agent to capture and apply domain specific knowledge and processing to solve problems. An Intelligent Agent uses knowledge, information and reasoning to take reasonable actions in pursuit of a goal. It must be able to recognise events, determine the meaning of those events and then take actions on behalf of a user. One central element of intelligent behaviour is the ability to adopt or learn from experience. Any Agent that can learn has an advantage over one that cannot. Adding learning or adaptive behaviour to an intelligent agent elevates it to a higher level of ability. In order to construct an Intelligent Agent, we have to use the following topics of Artificial Intelligence: Knowledge Representation Reasoning Learning [5] 1.1.2 Operation The functionality of a mobile agent is illustrated in 1.1. Computer A and Computer B are connected via a network. In step 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 network with its state and code. In step 3 this mobile Agent will reach to its destination, computer B, which will resume its execution. [7] 1.1.3 Strengths and Weaknesses Many researchers are now developing methods for improving the technology, with more standardisation and better programming environments that may allow mobile agents to be used in products. It is obvious that the more an application gets intelligent, the more it also gets unpredictable and uncontrollable. The main drawback of mobile agents is the security risk involved in using them. [8] [9] The following table shows the major strengths and weaknesses of Agent technology: Strengths Weakness Overcoming Network Latency Security Reducing Network traffic Performance Asynchronous Execution and Autonomy Lack of Applications Operating in Heterogeneous Environments Limited Exposure Robust and Fault-tolerant Behavior Standardization Table 1.2 1.2 Applications The followings are the major and most widely applicable areas of Mobile Agent: Distributed Computing: Mobile Agents can be applied in a network using free resources for their own computations. Collecting data: A mobile Agent travels around the net. On each computer it processes the data and sends 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 maintenance 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 range of 10 m and 100 m with increased power. [38] Mobile agents as Pets: Mobile agents are the ideal pets. Imagine something like creatures. What if you could have some pets wandering around the internet, choosing where they want to go, leaving you if you dont care about them or coming to you if you handle them nicely? People would buy such things wont they? [38] Mobile agents and offline tasks: 1. Mobile agents could be used for offline tasks in the following way: a- An Agent is sent out over the internet to do some task. b- The Agent performs its task while the home computer is offline. c- The Agent returns with its results. 2. Mobile agents could be used to simulate a factory: a- Machines in factory are agent driven. b- Agents provide realistic data for a simulation, e.g. uptimes and efficiencies. c- Simulation results are used to improve real performance or to plan better production lines. [10[ [11] [12] 1.3 Life Cycle An intelligent and autonomous Agent has properties like Perception, Reasoning  and Action which form the life cycle of an Agent as shown in 1.2. [6] The agent perceives the state of its environment, integrates the perception in its knowledge base that is used to derive the next action which is then executed. This generic cycle is a useful abstraction as it provides a black-box view on the Agent and encapsulates specific aspects. The first step is the Agent initialisation. The Agent will then start to operate and may stop and start again depending upon the environment and the tasks that it tried to accomplish. After 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 Step Description Initialize Performs one-time setup activities. Start Start its job or task. Stop Stops jobs, save intermediate results, joins all threads and stops. Complete Performs one-time termination activities. Table 1.3 1.4 Agent Oriented Programming (AOP) It is a programming technique which deals with objects, which have independent thread of control and can be initiated. We will elaborate on the three main components of the AOP. a- Object: Grouping data and computation together in a single structural unit called an ‘Object. Every 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] [17] 1.5 Network paradigms This section illustrates the traditional distributed computing paradigms like Simple Network Management Protocol (SNMP) and Remote Procedure Call (RPC). 1.5.1 SNMP Simple Network Management Protocol is a standard for gathering statistical data about network traffic and the behavior of network components. It is an application layer protocol that sits above TCP/IP stack. It is a set of protocols for managing complex networks. It enables network administrators to manage network performance, find and solve network problems and plan for network growth. It is basically a request or response type of protocol, communicating management information between two types of SNMP entities: Manager (Applications) and Agents. [18] Agents: They are compliant devices; they store data about themselves in Management Information Base (MIB) (Each agent in SNMP maintain a local database of information relevant to network management is known as the Management Information Base) and return this data to the SNMP requesters. An agent has properties like: Implements full 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 messages 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 Data Protocol (UDP, a simple protocol enabling an application to send individual message to other applications. Delivery is not guaranteed, and messages need not be delivered in the same order as they were sent) to carry its messages. Finally, there is one application that enables end user to control the man ager software and view network information. [19] Table 1.4 comprises the Strengths and Weaknesses of SNMP. Strengths Weaknesses Its design and implementation are simple. It may not be suitable for the management of truly large 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 well suited for retrieving large volumes of data, such as an entire routing table. All major vendors of internetwork hardware, such as bridges and routers, design their products to support SNMP, making it very easy to implement. Its traps are unacknowledged and most probably not delivered. Not applicable It provides only trivial authentication. Not applicable It does not support explicit actions. Not applicable Its MIB model is limited (does not support management queries based on object types or values). Not applicable It does not support manager-to-manager communications. Not applicable The information it deals with neither detailed nor well-organized enough to deal with the expanding modern networking requirements. Not applicable It 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 1.4 1.5.2 RPC A remote procedure call (RPC) is a protocol that allows a computer program running on one host to cause code to be executed on another host without the programmer needing to explicitly code for this. When the code in question is written using object-oriented principles, RPC is sometimes referred to as remote invocation or remote 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 exist 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 avoid the details of the interface with the network. The transport independence of RPC isolates the application from the physical and logical elements of the data communications mechanism and allows the application to use a variety 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 happens: Packages the name of the procedure and arguments to the call and transmits them over network to the remote machine where the RPC server id running. It is called â€Å"Marshalling†. [20] RPC decodes the name of the procedure and the parameters. It makes actual procedure call on server (remote) machine. It packages returned value and output parameters and then transmits it over network back to the machine that made the call. It is called â€Å"Unmarshalling†. [20] 1.6 Comparison between Agent technology and network paradigms Conventional Network Management is based on SNMP and often run in a centralised manner. Although the centralised management approach gives network administrators a flexibility of managing the whole network from a single place, it is prone to information bottleneck and excessive processing load on the manager and heavy usage of network bandwidth. Intelligent Agents for network management tends to monitor and control networked devices on site 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 comparison between the intelligent agent and its contemporary technologies: Property RPC SNMP Intelligent Agent Communication Synchronous Asynchronous Asynchronous Processing Power Less Autonomy More Autonomous but less than Agent More Autonomous Network support Distributed Centralised Heterogeneous Network Load Management Heavy usage of Network Bandwidth Load on Network traffic and heavy usage of bandwidth Reduce Network traffic and latency Transport Protocol TCP UDP TCP Packet size Network Only address can be sent for request and data on reply Only address can be sent for request and data on reply Code and execution state can be moved around network. (only code in case of weak mobility) Network Monitoring This is not for this purpose Network delays and information bottle neck at centralised management station It gives flexibility to analyse the managed nodes locally Table 1.5 Indeed, Agents, mobile or intelligent, by providing a new paradigm of computer interactions, give new options for developers to design application based on computer connectivity. 20 Chapter 2 Learning Paradigms 2.1 Knowledge Discovery in Databases (KDD) and Information Retrieval (IR) KDD is defined as â€Å"the nontrivial process of identifying valid, novel, potentially useful and ultimately understandable patterns in data† (Fayyad, Piatetsky-Shapiro and Smith (1996)). A closely related process of IR is defined as â€Å"the methods and processes for searching relevant information out of information systems that contain extremely 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 include 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] Two major research areas have been developed in response to this problem: * Data warehousing: It is defined as: â€Å"Collecting and ‘cleaning transactional data to make it available for online analysis and decision support†. (Fayyad 2001, p.30)  · Data Mining: It is defined as: â€Å"The application of specific algorithms to a data set for purpose of extracting data patterns†. (Fayyad p. 28) 2.2 Data Mining Data mining is a statistical term. In Information Technology it is defined as a discovery of useful summaries of data. 2.2.1 Applications of Data Mining The following are examples of the use of data mining technology: Pattern of traveller behavior mined: Manage the sale of discounted seats in planes, rooms in hotels. Diapers and beer: Observation those customers who buy diapers are more likely to buy beer than average allowed supermarkets to place beer and diapers nearby, knowing many customers would walk between them. Placing potato chips between increased sales of all three items. Skycat and Sloan Sky Survey: 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 case of diabetes. This sort of mining will become much more important as the human genome is constructed. [22] [23] [24] 2.2.2 Communities of Data Mining As data mining has become recognised as a powerful tool, several different communities have laid claim to the subject: Statistics Artificial Intelligence (AI) where it is called â€Å"Machine Learning† Researchers in clustering algorithms Visualisation researchers Databases: When data is large and the computations is very complex, in this context, data mining can be thought of as algorithms for executing very complex queries on non-main-memory data. 2.2.3 Stages of data mining process The following are the different stages of data mining process, sometimes called as a life cycle of data mining as shown in 2.1: Data gathering: Data warehousing, web crawling. Data cleansing: Eliminate errors and/or bogus data e.g. Patients fever = 125oC. 3- Feature extraction: Obtaining only the interesting attributes of the data e.g. â€Å"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 â€Å"data mining† and is where we shall concentrate our efforts. 5- Visualisation of the data: 6- Evaluation of results: Not every discovered fact is useful, or even true! Judgment is necessary before following the softwares conclusions. [22] [23] [24] 2.3 Machine Learning There are five major techniques of machine learning in Artificial Intelligence (AI), which are discussed in the following sections. 2.3.1 Supervised Learning It relies on a teacher that provides the input data as well as the desired solution. The learning agent is trained by showing it examples of the problem state or attributes along with the desired output 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, sensor logs or trace logs is often used as training or example data. The example of supervised learning algorithm is the ‘Decision Tree, where there is a pre-specified target variable. [25] [5] 2.3.2 Unsupervised Learning It 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 groups. This process continues until the Agents place the same group 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 â€Å"the K-Means Clustering algorithm†. [25] [5] 2.3.3 Reinforcement Learning It 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] [5] 2.3.4 On-line and Off-line Learning On-line learning means that the agent is adapting while it is working. Off-line involves saving data while the agent is working and using the data later to train the agent. [25] [5] In 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 training data set. Once this database is ready it can be used to modify the behaviour 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 test LIAgents on Iris dataset, Vote dataset about the polls in USA and two medical datasets namely Breast and Diabetes. [26] See Appendix A for all these four datasets. 2.4 Supervised Learning (Decision Tree ID3) Decision trees and decision 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. [40] In 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 days 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 amount 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. [40] A particularly efficient method for producing classifiers from data is to generate a decision tree. The decision-tree representation is the most widely used logic method. There is a large number of decision-tree induction algorithms described primarily in the machine-learning and applied-statistics literature. They are supervised learning methods that construct decision trees from a set of input-output samples. A typical decision-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 outcomes of the test at the node. [40] Decision trees are used in information theory to determine where to split 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 subsequent 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. [40] 2.4.1 Decision Tree Decision trees are powerful and popular tools for classification and prediction. The attractiveness 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 humans 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 either: Leaf 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 fixed 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 training cases. A decision tree is constructed by looking for regularities in data. [27] [5] 2.4.2 ID3 Algorithm J. Ross Quinlan originally developed ID3 at the University of Sydney. He first presented ID3 in 1975 in a book, Machine Learning, vol. 1, no. 1. ID3 is based on the Concept Learning System (CLS) algorithm. [28] function ID3 Input: (R: a set of non-target attributes, C: the target attribute, 2.4.3 Functionality of ID3 ID3 searches through the attributes of the training instances and extracts the attribute that best separates the given examples. If the attribute perfectly 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 earlier 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 measure of homogeneity of the set of examples. [5] Entropy(S) = pplog2 pp pnlog2 pn (1) (2) 2.4.4 Decision Tree Representation A decision tree is an arrangement of tests that prescribes an appropriate 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 descending 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. [29][5] 2.4.5 Challenges in decision tree Following are the issues in learning decision trees: Determining how deeply to grow the decision tree. Handling continuous attributes. Choosing an appropriate attribute selection measure. Handling training data with missing attribute values. Handling attributes with differing costs and Improving computational efficiency. 2.4.6 Strengths and Weaknesses Following are the strengths and weaknesses in decision tree: Strengths Weaknesses It 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 field 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 indication 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 classification boxes that may not correspond well with the actual distribution of records in the decision space. Table 2.1 2.4.7 Applications Decision tree is generally suited to problems with the following characteristics: a. 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 disjoint 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 setting is less common. f. Decision tree methods can be used even when some training examples have unknown values (e.g., humidity is known for only a fraction of the examples). [30] Learned 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

Timothy Findley :: essays research papers

Biography of Author Timothy Findley Timothy Findley is a native of Toronto, Ontario. He was born in 1930 and grew up in the Rosedale district of Toronto. Growing up, Timothy Findley knew that he wanted to be an artist of some form. He studied dance and later acting, which had more success. While acting, he met one of his current life long friends; actress Ruth Gordon. Gordon convinced Findley that writing was his real talent and that he should pursue it further with more concentration. So findley gave up acting after his first short story was published in The Tamarack Review to concentrate on his writings. Findley had problems receiving recognition from his first two books, The Last of the Crazy People (1967) and The Butterfly Plague (1969). It was The Wars that gave Findley the recognition that he deserved; he received the Governor General’s Literary Award for this novel. In his early years of his writing career, Findley also wrote scripts for television, radio, and film. The most success of his film career cam e from the television series The Whiteoaks of Jalna, and The National Dream; for which he received an ACTRA award for co-writing with his partner, William Whitehead. After The Wars, Findley came out with six other popular novels, two collections of short stories and Inside Memory: Pages from a Writer’s Workbook (1990), a collection of articles, journal entries, and reminiscences. Findley has been very active in the writing community; he has helped to found the Writer’s Union of Canada and has served as its chairperson. He has also been President of the Canadian chapter of P.E.N. International, and is also active in Artists Against racism. In addition to this Findley has won many awards including the Canadian Authors Association Award, The Order of Ontario, The Ontario Trillium Award, and he has been appointed an Officer of the Order of Canada.