Limitations of Expert Systems and Neural Networks

Expert Systems and Neural Networks

The Development and Limitations of Expert Systems and Neural Networks

The human experience demands a constant series of decisions to survive in a hostile environment. The question of “fight or flight” and similar decisions has been translated into computer-based models by using the now-famous “if-then” programming command that has evolved into the promising field of artificial intelligence. In fact, in their groundbreaking work, Newell and Simon (1972) showed that much human problem solving could be expressed in terms of such “if-then” types of production rules. This discovery helped to launch the field of intelligent computer systems (Coovert & Doorsey 2003). Since that time, a number of expert and other intelligent systems have been used to model, capture, and support human decision making in an increasingly diverse range of disciplines; however, traditional rule-based systems are limited by several fundamental constraints, including the fact that human experts are needed to articulate propositional rules, that the symbolic processing normally used prevents direct application of mathematics, and that traditional rule-based systems require a large number of rules that are not receptive to unique data inputs. This paper provides an examination of the concepts and technologies needed to develop, implement and integrate expert systems and neural networks. The limitations of expert systems and their alternatives are discussed, followed by an analysis of the relevant and scholarly literature covering neural networks. A summary of the research is provided in the conclusion.

Review and Discussion

Background and Overview. Artificial Intelligence (AI) as a formal discipline is certainly not new, having been around for more than 50 years (Gozzi 1997). Nevertheless, AI remains a term that frequently “conjures images of HAL’s refusal to open the pod bay doors or Deep Blue winning the world chess championship. But artificial intelligence (or Al) is not a phenomenon restricted to science fiction movies and chess tournaments; it has rapidly, if silently, become a fixture of daily life” (Gibson 2003:83). In fact, Kapoor (2003) emphasizes that there can be no dispute that machines with greater-than-human intelligence will be built in the next 50 years, and the creation of such AI empowered creations will have far-reaching implications for all aspects of society, science, technology, and the environment.

According to Kapoor, “The likelihood of creating AI within the next 50 years, and when it happens, its deep impacts on science and society, are both assertions that will be accepted by most futurists” (788). Bostrom (2003) covers the phenomenal increases in number-crunching capacity of supercomputers that have followed Moore’s law, including IBM’s biggest and best, Blue Gene that operates at 1 quadrillion operations per second which is scheduled to become operational by the end of 2005. This author notes that he is in agreement with Kapoor concerning “the tragedy of the vast unfair inequalities that exist in today’s world, and also in regard to the fact that there would be considerable risks involved in creating machine intelligence”; however, this author suggests that AI assistive technologies might also serve to reduce certain other kinds of risk.

For instance, Bostrom says:

An assessment of whether machine intelligence would produce a net increase or a net decrease in overall risk is beyond the scope of my original paper or this reply. (Even if it were to be found to increase overall risk, which is very far from obvious, we would still have to weigh that fact against its potential benefits. And if we determined that the risks outweighed the benefits, we would then have to question whether attempting to slow the development of machine intelligence would actually decrease its risks, a hypothesis that is also very far from obvious (902).

While the goals of individual practitioners using AI applications have varied and changed over time, a reasonable characterization of the general field of AI is that it is intended to make computers do things that when done by people are described as having indicated intelligence (Steels 1995); this author characterized the primary goals of AI as both the construction of useful intelligent systems and the understanding of human intelligence. According to Gozzi (1997), “In the 1950s, a group of scientists decided to try to provide the computer with intelligence. Their goal seemed attainable due to a common metaphorical identification of the computer with a brain. From their efforts emerged the field of artificial intelligence, or AI” (219).

This author suggests that the basic, or root metaphors of AI, resembled a classical syllogism:

Major Premise: The computer is a brain.

Minor Premise: Thinking is computing.

Conclusion: If we provide the computer with sophisticated programs, it will develop a mind similar to human minds (220).

In recent years, this has, in fact, been the focus of AI programs. According to Komninou (2003), “The more we progress, the more possessed we become with technology, the more obsessed we become with the very idea of ‘intelligence’, the more we take the images of our desires to be the real thing” (793). According to Boodoo, Bouchard, Boykin et al. (1996):

Individuals differ from one another in their ability to understand complex ideas, to adapt effectively to the environment, to learn from experience, to engage in various forms of reasoning, to overcome obstacles by taking thought. Although these individual differences can be substantial, they are never entirely consistent: A given person’s intellectual performance will vary on different occasions, in different domains, as judged by different criteria. Concepts of “intelligence” are attempts to clarify and organize this complex set of phenomena (77).

As a result, a variety of applications of AI have emerged as an increasingly promising technology that can help users from a variety of fields to structure, guide, and improve information processing for decision-making purposes. For example, today, AI programs provide consultative advice to physicians concerning infectious diseases and their etiologies; such programs help physicists investigate unknown molecules and make predictions about their molecular structures with spectroscopic analysis; they also assist mathematicians in solving complex problems, process credit requests for American Express, hunt submarines for the U.S. Navy, help develop timely advertisements for retailers and evaluate a client’s ability to repay a loan (Jones, Martin, Mcwilliams et al. 1991).

According to Dillon (1993), artificial intelligence is “the branch of computer science devoted to the study of how computers can be used to simulate or duplicate functions of the human brain… [making] it appear as though a computer is thinking, reasoning, making decisions, storing or retrieving knowledge, solving problems, and learning” (74). There are three fundamental differences between AI and other programming languages though:

AI does not use algorithms, or step-by-step procedures, in order to solve problems; rather, it employs symbolic representation such as letters, words or numbers to represent objects (in the form of statements and procedures), processes and their relationships;

The second major area of difference between AI and other programming languages is the manner in which uncertainty is handled. Dillon uses the sentence, “Erin is taller than Esther” as an example of the uncertainty involved in a definition of “tall.” According to the author, “Are you tall at five feet five inches? What about short? Are you short at four feet eleven inches or at five feet? Artificial Intelligence is able to deal with such imprecision through the use of confidence factors and probability” (emphasis added) (Dillon 75).

The final difference between AI and other programming languages concerns the realm of decision-making. According to Dillon, “Conventional software uses precise data and step-by-step instructions for solving a problem, thereby limiting the computer to predetermined solutions. Whereas in AI, the computer is given information (sometimes imprecise) and the ability to make inferences. The computer and the software determine the solution” (76).

A good example, because it is likely known to many people today, of how these imprecise or “fuzzy” conditions play out in an actual setting can be found in the popular computer game, “The Sims” and its many permutations. The characters in these games are governed by a set of “fuzzy” metrics to which they respond (or not, depending on the user preferences). For example, when they become sufficiently hungry, Sims characters will seek out food; when they become sufficiently tired, they will sleep.

In fact, the metrics by which modern people measure intelligence are closer to human experience than might be commonly thought; according to Stevens (1996), “We are already used to dealing with digital, intelligent life in the form of digital representations of other humans” (414). This is echoed in her essay, “Artificial Intelligence and the Real World,” where Jenkins (2003) suggests that the scope and significance of artificial intelligence (AI) make it an important concern today and in the future, perhaps more so than other emerging technologies, particularly “because AI is concerned with replicating and enhancing intelligence, and this concept, related as it to consciousness, is at the heart of human identity” (779).

This connection with “human identity” is at the core of AI assisting technologies. In the past, computer scientists working on AI have largely ignored the social roots of human intelligence; however, in more recent years, there has been an increased interest among these researchers concerning the social aspects of intelligence. According to Bainbridge, Brent, Carley et al. (1994), “Areas such as distributed artificial intelligence, coordination theory, and collaboration technology (all with strong roots in engineering or computer science) have begun to look at social issues” (407). Many early AI programs provided the opportunity for humans and computers to interact through natural-language conversations; unfortunately, the programming challenge has always been to simulate the behavior of a single human actor (Bainbridge et al. 1994). While early AI researchers focused on a variety of schools of thought within psychology, they tended to overlook the sociological considerations that were required to make such assistive technologies more robust.

A number of sociologists, though, maintain that true AI cannot truly achieved without the active participation of sociologist; Allen Newell (1990) makes this point in his seminal book, Unified Theories of Cognition (in Bainbridge et al.). In some sense, all types of AI applications are able to “think”; in other words, the programs are able to “solve problems in a way that would be considered intelligent if done by a human”; as a result, AI applications are being increasingly used in a variety of computer-based applications today such as speech recognition, robotics (machine vision systems), natural language processing, expert systems and neural networks; the latter two extensions of AI technology are discussed further below.

Expert Systems. Expert systems are one of the most popular applications of artificial intelligence today; these are computerized decision-making applications that structure expertise in a specific area and emulate human decision-making (Berry, Berry & Foster 1998). According to Grabinger, Jonessen and Wilson (1990), “Expert systems are practical tools that can serve as intelligent job aids to facilitate on-the-job decision making in tasks such as judging student projects, diagnosing learning problems, identifying and classifying performance problems, or helping consumers to decide among a large number of alternatives” (1). Expert systems are intended to improve human performance; however, like any tool, the effective use of an expert system requires conceptual understanding, practice, and specific development skills and processes (Grabinger et al. 1990). Furthermore, like most instructional development tools, the most crucial design phase occurs during the early stages of development when the analysis of a problem and the subsequent structuring of knowledge into a form that is appropriate for entry into an expert system building tool (Grabinger et al. 1990).

It is easy to become overwhelmed with terminology and the vagaries of scientific whim, though. To keep it simple, then, expert systems are a type of computer program that employs artificial intelligence to solve problems within a specialized domain that has traditionally required the use of human expertise alone (Zwass 2004). The first such expert system was developed by Edward Feigenbaum and Joshua Lederberg of Stanford University in California in 1965. This early expert system, which later became known as U.S. Dendral, was designed to analyze chemical compounds; today, expert systems have a wide range of commercial applications in fields as diverse as medical diagnosis, petroleum engineering, and financial investing (Zwass 2004).

While expert systems may be as diverse as the needs of their users, all expert systems depend on two basic components to accomplish their analyses: 1) a knowledge base and 2) an inference engine. According to Zwass, “A knowledge base is an organized collection of facts about the system’s domain. An inference engine interprets and evaluates the facts in the knowledge base in order to provide an answer” (5). Such rule-based expert systems tend to be deductive, compared to traditional decision tree algorithms that use inductive learning (Gahegan, Harrower, Rhyne, & Wachowicz (2001). Some typical tasks expert systems are being used for today include classification, diagnosis, monitoring, design, scheduling, and planning for specialized tasks. Because expert systems need to be “expert” in some specific area, the knowledge bases for such systems are drawn from laws, regulations, and — increasingly — the unique components of human expertise itself (Berry et al. 1998).

The facts that are required to be incorporated into a knowledge base can be acquired from human experts through interviews and observations which is then represented in the form of “if-then” rules (production rules): “If some condition is true, then the following inference can be made (or some action taken)” (Zwass 2004). Knowledge bases of major expert systems will likely include thousands of such rules. A probability factor is often attached to the conclusion of each production rule, because the conclusion is not a certainty. For example, a system for the diagnosis of eye diseases might indicate, based on information supplied to it, a 90% probability that a person has glaucoma, and it might also list conclusions with lower probabilities. An expert system may be capable of displaying the sequence of rules through which it arrived at its conclusion; tracing this flow helps the user to appraise the credibility of its recommendation and is useful as a learning tool for students (Zwass 2004).

A number of public agencies have used expert systems for well over a decade now, and these systems are becoming increasingly common for a wide variety of other applications; for instance, many social service departments across the country are using expert systems to determine eligibility for food stamps or refugee assistance; law enforcement officials analyze solved and unsolved burglary cases through a computerized network to develop a profile of possible perpetrators of new crimes; and water testing laboratories can apply for state licensure through expert systems (Berry et al. 1998). “Although expert systems began as stand-alone computer programs,” Berry et al. report, “large public agencies are integrating their expert systems into their information data bases, and thus users may not even recognize their expert system as such” (1998:294).

The primary benefits of expert systems relate to the size of the organization or the discipline involved. Previous studies have shown how small business owners use computers to delegate many routine decisions to their employees, thereby allowing owners to focus on managerial activities; by contrast, in larger company, a number of tasks such as training, enforcing procedures, or monitoring and controlling business activities are handled by throwing additional staff or by hiring extra staff with specific abilities.

According to Bradley and Hebert (1993), “The impact of expert systems in small business, therefore, may be greater than in large business since small firms may not have the luxury of alternative solutions” (23). While expert systems are an increasingly common feature in many businesses, another extension of artificial intelligence may represent an ever more important innovation for healthcare and statistical analyses applications; these are discussed further below.

Neural Networks. While an expert system is a software program that resembles a database, neural networks are designed to learn less from predigested data and more through experience (Zarowin 1995). As an emerging AI-based technology, compared with traditional statistical approaches, neural network analyses have been shown to be “of great use in diverse real-life applications”; likewise, researchers have noted that neural network analysis improved Chase Manhattan’s credit card fraud detection rates over the regression model they had been using (Calori, Lubatkin, Tung et al. 2000:223). In their study, “Artificial Neural Networks as a Method of Spatial Interpolation for Digital Elevation Models,” Civco, Cromley and Merwin (2002) report that “Artificial neural networks (ANNs) are highly connected computational models inspired by the neurological structure of the human brain. These networks, which are considered a subset of artificial intelligence, are designed to solve complex computational problems by means of ‘self-learning.’ Learning does not occur in a manner similar to the learning process of the human brain, but rather through a process of training and recall” (100). A study by Cheng, McClain and Kelly (1997) found that such ANNs are quickly being recognized as a powerful tool for investment forecasting and are attracting much attention from potential users based on the impressive results to date. For instance,” Cheng et al. report:

An ANN-based system for investing in the U.S. Treasury Bond market was recently created and used to direct investments in that market for the years 1989-93. Over the five-year period, the ANN system generated a return on investment of 17% versus 14% for the prestigious Lehman Brothers Treasury Bond index over that same period. In another example, an ANN-based system was used to direct the investment of $10,000 in the S& P. 500 index over a 25-month period. Results were spectacular, as the ANN was able to increase the fund to $76,034 over the 25 months (1997:5)

According to Caroli et al., in 1987, Science Applications International Corporation developed a neural network model that remains in use in all major airports around the world that was able to outperform the linear discriminant analysis used previously in predicting the likelihood of a bomb in passenger luggage. According to Feldman (2001), “New systems using artificial intelligence (AI), already in place at a number of money center banks, create significant new marketing opportunities. These systems help reduce the cost of compliance while presenting an opportunity for banks to position themselves as adopting the ‘high road'” (56). Other researchers have reported that neural network analysis have been able to predict the ratings of bonds more effectively than multiple regression. Beyond this technology’s powerful predictive capability, Calori et al. report a number of other uses for neural networks as well, particularly regarding pattern recognition (such as identification of cancerous cells) as well as speech recognition and generation.

A neural network is modeled on the human brain in which there are extensively interconnected units (neurons) that make up a vast network capable of complex pattern recognition. As such, it is comprised of a number of computational elements that operate in parallel and arranged in patterns reminiscent of biological neural nets. The purpose of such emulation is to provide artificial systems that are capable of sophisticated, perhaps intelligent, computation and pattern recognition similar to those that the human brain routinely performs (Caroli et al. 200). According to Caroli et al. (2000), a typical network is comprised of several layers of interconnected neurons that include input neurons (these receive stimuli in the form of inputs, usually the independent variables), output neurons (dependent variable), as well as a layer of “hidden neurons” that can only interact with input and output neurons but can never actually be observed.

According to “Computers as Assistants: A New Generation of Support Systems” by Hoschka (1996), the first substantive result toward the achieving a seamless integration of inputs and outputs in this framework was the Associative Memory Model (ASM), described as “a flexible experimental system that tackles different problems at different levels” (55). The overall organization of the ASM approach is shown in Figure 1 below. Hoschka writes: “The basic level consists of a neural network package that realizes the minimal functionalities needed to build general neural networks. The focus lies here on the requirement for minimality and on taking care not to be completely unrealistic from a biological point-of-view” (55). The next level of the ASM provides a variety of forms of associative memory structures such as object-attribute- value triples or chains of predicates. Functionally, the user has the potential, for example, of inputting triples and retrieving them by using attributes as context-selecting devices (Hoschka 1996).

The important distinction among these structures is that they are all based on the same primitive structures (nodes) and operated on by the same basic “inference” method; in other words, a relaxing, value-passing, spreading-activation mechanism. The results yielded from the system are modeled as a network of mutually reinforcing nodes. According to Hoschka, “At the third level, these structures can be used for retrieval and associative completion. Previously stored (memorized) examples of objects and situations are retrieved on the basis of partial description” (56). The means for computing best matches (such as the intersection of attribute values) are also realized using the same spreading-activation mechanism.

Figure 1. Structure of ASM [Source: Hoschka 1996].

A number of models of neural networks have subsequently been developed based on such different input-output relationships and different learning models (Ye 1997). For instance, Ye reports that a heteroassociative network provides output that is different from the input, but an autoassociative network yields output that is equal to the input. According to Ye: “There are two general categories of learning: supervised and unsupervised (self-organizing). In supervised learning, desired outputs to given inputs are shown to neural networks. Unsupervised learning occurs without the indication of desired outputs to given inputs” (7). The importance of this relationship was noted by Collins and Clark (1993), who reported: “The task of the network is to learn an optimal pattern of interconnections that best captures all of the input/output relationships” (507). The capability of neural networks in learning from examples is useful in recognizing and generalizing user patterns from instances of repeated user actions, and in adjusting the acquired knowledge to dynamic environments (Ye 1997).

The capabilities of neural networks in implicit knowledge representation and parallel processing also provide the support to processing efficiency required by online dynamic user modeling. Furthermore, the degree of robustness of neural networks to noise represents a valuable addition to data analysis in user modeling; consequently, neural networks provide a better alternative for supporting intelligence required in user modeling and intelligent interface (Ye 1997).

According to Zarowin, “Even in its early development stage, the technology is reasonably successful at making ‘decisions’ about data that are incomplete, imprecise and only partly correct – jobs particularly unsuited to conventional software” (56). Neural networks also appear to be most useful in economic forecasting, risk management, financial modeling and establishing credit ratings than previous predictive models; for instance, neural networks can determine whether a lease should be classified as either operating or capital (Zarowin 1995).

In his study, “Thinking Computers,” Zarowin writes: “One task in which they [neural networks] have shown unusual strength is in determining whether a lease should be classified as operating or capital. To build its experience in this area, an accountant would feed a program a number of sample leases that have been classified by the human instructor. Over time, the program distinguishes the patterns that make it either ail operating or a capital lease” (57). Finally, Sharda (1994) compared the performance of neural nets to classical statistical techniques in 42 reported cases and found that the neural network model performed better in 71% of the cases, the statistical techniques performed better in 17% of the cases, and no winner emerged in the remaining 12%.

A noted authority in the field today, Kohonen (1988) defined such a network as “…a parallel interconnected network of simple (usually adaptive) elements and their hierarchical organizations which are intended to interact with the objects of the real world in the same way as biological nervous systems do” (4). More to the point as to human experiential knowledge, Haykin (1994) described a neural network as being “. . . A parallel distributed processor that has a natural propensity for storing experiential knowledge and making it available for use. Knowledge is acquired by the network through a learning process, and interneuron connection strengths, known as synaptic weights, are used to store the knowledge” (2).

For many complex problems, then, neural network models provide greater predictive accuracy than linear statistical methods, presumably because of violations of the requisite assumptions and model impositions of linear statistical methods. While neural networks have been successfully applied to demonstration problems in the social sciences, their effectiveness and ultimate usefulness has not yet been documented in the psychological literature to date (Henley & Mcmillen 2001).

According to Chen (1997), an entire set of neural networks can be used to accurately represent and infer the entire set of the users’ task-related characteristics. “These networks function as associative memories,” Chen says, “that can capture the causal relations among users’ characteristics for the system adaptation” (25). This author suggests that this approach can be expected to overcome some of the inherent problems of the conventional stereotyping approaches in terms of pattern recognition and classification of user characteristics. For instance, a neural network was developed that functioned as an “expert” to help classify adolescents as maladjusted, nominally adjusted, or well-adjusted (Kashani, Nair, Venkatesh & Reid 2001).

Notwithstanding these innovations, others remain unconvinced about the utility of the insights provided by neural networks. For instance, Henley and McMillen point out that in their investigation of the efficacy of neural network over logistic regression models in predicting the incidence of driving while intoxicated (DWI) offenses among a group of students based on past behaviors. In this study, the authors report, “The partitions of connection weights for each predictor variable were inconsistent across neural networks and differences between the largest and weakest partition tended to be rather trivial. Essentially, the appeal of neural networks as classification tools lies in their ability to accommodate the true structure of the data” (3).

While linear statistical methods constrict a model to a linear, or possibly sigmoid, function, neural networks provide a more flexible model; nevertheless, the findings by Henley and Mcmillen suggest that the accuracy gained from releasing the restrictions on a model must be weighed against the additional complexity of the model. According to these authors, “Neural networks may be more difficult to interpret due to their increased number of estimated parameters, nonlinear functions, and complex interactions” (4). Clearly, logistic regression models were more useful to this researcher in providing more useful insights and explanations than neural networks. The authors suggest that the lack of explanation and insight provided was most likely attributable to the relatively large number of predictor variables compared to previous comparable studies.

At any rate, the neural network models used by Henley and Mcmillen did perform well as predictors in classification problems involving noisy data sets: “That is, the relative performance of the neural networks increased as statistically, positively skewed, but theoretically important, variables were added to the analyses” (Henley & Mcmillen 5). In sum, the neural network models were especially accurate in predicting which of the students represented a future high DWI risk, which the authors suggest is perhaps the most important category in terms of preventative programs; however, in their analyses involving less noisy data sets, the neural networks models did not provide sufficient accuracy to justify the additional training time and the inadequate level of explanation such approaches yielded.

Constraints to Effective Use of AI Applications. Perhaps one of the most widely known adages of the computer age is “garbage in, garbage out”; unfortunately, this observation remains entirely relevant to AI systems that are based on experiential — and therefore subjective — components of human knowledge. According to Zarowin (1995), “Computers are great at following orders. They superbly perform repetitive tasks such as sorting, searching and copying. But when it comes to making decisions based on incomplete or contradictory information – let’s can that ‘thinking’ – they are duds” (55). Worse still, though, such applications tend to follow human programming orders so rigidly while blindly acquiescing to obvious programming mistakes that in many cases “they resemble the brooms used by the sorcerer’s apprentice in the Walt Disney film Fantasia, endlessly repeating a process no matter how ridiculous or destructive the outcome” (Zarowin 56). Clearly, though, assisting systems based on AI have the potential to provide timely and effective feedback to practitioners today and in the future; however, some fundamental constraints remain that may not be totally resolvable because of the inherent limitations associated with the knowledge bases upon which they depend for input. According to Hoschka (1996), “It is well-known of existing expert systems that the explanation components actually explain very little. They are usually limited to confronting the user with a more or less flexible presentation, giving the protocol for each of the problem-solving steps executed” (6). In addition, the effective use of such AI applications involves not only making a system more transparent, but implementing pedagogical competence so that the user’s understanding of a problem can be estimated and improved in the course of dialogue as well. Hoschka recommends that such system incorporate intuitive tutorial faculties, rather than being limited to simply showing the formal structures of the knowledge-based system.


The research showed that computer-based intelligence is required to support a wide range of activities in user modeling and intelligent interface today; expert systems and artificial neural networks are the two most popular expressions of artificial intelligence today. Expert systems, though, demand explicit knowledge acquisition and knowledge representation to be effective, and their intelligent capabilities are restricted to what is programmed in their knowledge bases. In the final analysis, AI applications such as expert systems and neural networks will undoubtedly continue to build on human experience and knowledge and will develop processing into an increasingly interactive system that draws from that knowledge, and then presents selected information that will help users solve their unique problems.

Works Cited

Bainbridge, William Sims, Edward E. Brent, Kathleen M. Carley et al. (1994). Artificial Social

Intelligence. Annual Review of Sociology, 20, 407.

Berry, Frances Stokes, William D. Berry and Stephen K. Foster. (1998). The Determinants of Success in Implementing an Expert System in State Government. Public Administration

Review, 58(4):293.

Bostrom, N. (2003). Taking Intelligent Machines Seriously: Reply to My Critics. Futures,


Bradley, John H. And Frederic J. Hebert. (1993). Expert Systems Development in Small

Business: A Managerial Perspective. Journal of Small Business Management, 31(3):23.

Calori, Roland, Michael Lubatkin, Y. Alex Tung et al. (2000). Using Neural Network Analysis

to Uncover the Trace Effects of National Culture. Journal of International Business

Studies, 31(2):223.

Chen, Qiyang. (1997). Modeling a User’s Domain Knowledge with Neural Networks. Human-

Computer Interaction, 9(1):25.

Cheng, Wei, Christopher Kelly and Bruce W. Mcclain. (1997). Artificial Neural Networks

Make Their Mark as a Powerful Tool for Investors. Review of Business, 18(4):4.

Civco, Daniel L., Robert G. Cromley and David A. Merwin. (2002). Artificial Neural Networks

as a Method of Spatial Interpolation for Digital Elevation Models. Cartography and Geographic Information Science, 29(2):99.

Collins, J.M. And M.R. Clark. (1993). An application of the theory of neural computation to the prediction of workplace behavior: An illustration and assessment of network analysis. Personnel Psychology, 46(3):503-24.

Coovert, Michael D. And David W. Dorsey. (2003). Mathematical Modeling of Decision

Making: A Soft and Fuzzy Approach to Capturing Hard Decisions. Human Factors,


Dillon, Richard W. (1993). Introducing Artificial Intelligence into a High School’s Computer

Curriculum THE Journal, 20(8):74.

Feldman, Konrad. (2001). Al Can Squash Money Laundering. ABA Banking Journal, 93(7):56.

Gahegan, Mark, Mark Harrower, Theresa-Marie Rhyne and Monica Wachowicz. The Integration of Geographic Visualization with Knowledge Discovery in Databases and Geocomputation. Cartography and Geographic Information Science, 28(1):29.

Gibson, Keith. (2003). Arguing Artificially: A Rhetorical Analysis of the Debates That Have

Shaped Cognitive Science. Business Communication Quarterly, 66(2):83.

Gozzi, Raymond, Jr. (1997). Artificial Intelligence – Metaphor or Oxymoron? ETC.: A Review

of General Semantics, 54(2):219.

Grabinger, R. Scott, David H. Jonassen and Brent W. Wilson. Building Expert Systems in Training and Education. New York: Praeger Publishers, 1990.

Haykin, S. Neural Networks: A Comprehensive Foundation. New York, NY: Macmillan,

1994 in Calori et al. 2000, 224.

Henley, Tracy and Robert Mcmillen. (2001). Connectionism Isn’t Just for Cognitive Science:

Neural Networks as Methodological Tools. The Psychological Record, 51(1):3.

Hoschka, Peter Russell (Ed). Computers as Assistants: A New Generation of Support Systems.

Mahwah, NJ: Lawrence Erlbaum Associates, 1996.

Jenkins, Anne. (2003). Artificial Intelligence and the Real World. Futures, 35(7):779.

Jones, Warren T., Warren S. Martin, Evan Mcwilliams et al. (1991) Developing Artificial

Intelligence Applications: A Small Business Development Center Case Study. Journal of Small Business Management, 29(4):28.

Kapoor, Rakesh. (2003). When Humans Outsmart Themselves. Futures, 35(7):787.

Kashani, Jyotsna, Satish S. Nair, Venkatesh G. Rao and John C. Reid. (2001). A Neural

Network Approach to Identifying Adolescent Adjustment. Adolescence, 36(141):153.

Kohonen, T. (1988). An introduction to neural computing. Neural Networks, 1(1):3-16.

Komninou, E. (2003). AI: Man, Machines and Love. Futures, 35(7): 793.

Newell, A. And H.A. Simon. Human Problem Solving. Englewood Cliffs, NJ: Prentice Hall,

1972 in Coovert & Dorsey 2003, 118.

Sharda, R. (1994). Neural networks for the MS/OR analyst: An application bibliography.

Interfaces, 24(2): 116-130 in Calori et al. 2000, 225.

Steels, Luc (Ed.). The Artificial Life Route to Artificial Intelligence: Building Embodied,

Situated Agents. Hillsdale, NJ: Lawrence Erlbaum Associates, 1995.

Stevens, Tyler. (1996). “Sinister Fruitiness”: ‘Neuromancer,’ Internet Sexuality and the Turing

Test. Studies in the Novel, 28(3):414.

Ye, Nong. (1997). Neural Networks Approach to User Modeling and Intelligent Interface: a Review and Reappraisal. Human-Computer Interaction, 9(1):6.

Zarowin, Stanley. (1995). Thinking Computers. Journal of Accountancy, 180(5):55.

Zwass, Vladimir. (2004). Expert systems. In Encyclopedia Britannica [premium service].

Get Professional Assignment Help Cheaply

Buy Custom Essay

Are you busy and do not have time to handle your assignment? Are you scared that your paper will not make the grade? Do you have responsibilities that may hinder you from turning in your assignment on time? Are you tired and can barely handle your assignment? Are your grades inconsistent?

Whichever your reason is, it is valid! You can get professional academic help from our service at affordable rates. We have a team of professional academic writers who can handle all your assignments.

Why Choose Our Academic Writing Service?

  • Plagiarism free papers
  • Timely delivery
  • Any deadline
  • Skilled, Experienced Native English Writers
  • Subject-relevant academic writer
  • Adherence to paper instructions
  • Ability to tackle bulk assignments
  • Reasonable prices
  • 24/7 Customer Support
  • Get superb grades consistently

Online Academic Help With Different Subjects


Students barely have time to read. We got you! Have your literature essay or book review written without having the hassle of reading the book. You can get your literature paper custom-written for you by our literature specialists.


Do you struggle with finance? No need to torture yourself if finance is not your cup of tea. You can order your finance paper from our academic writing service and get 100% original work from competent finance experts.

Computer science

Computer science is a tough subject. Fortunately, our computer science experts are up to the match. No need to stress and have sleepless nights. Our academic writers will tackle all your computer science assignments and deliver them on time. Let us handle all your python, java, ruby, JavaScript, php , C+ assignments!


While psychology may be an interesting subject, you may lack sufficient time to handle your assignments. Don’t despair; by using our academic writing service, you can be assured of perfect grades. Moreover, your grades will be consistent.


Engineering is quite a demanding subject. Students face a lot of pressure and barely have enough time to do what they love to do. Our academic writing service got you covered! Our engineering specialists follow the paper instructions and ensure timely delivery of the paper.


In the nursing course, you may have difficulties with literature reviews, annotated bibliographies, critical essays, and other assignments. Our nursing assignment writers will offer you professional nursing paper help at low prices.


Truth be told, sociology papers can be quite exhausting. Our academic writing service relieves you of fatigue, pressure, and stress. You can relax and have peace of mind as our academic writers handle your sociology assignment.


We take pride in having some of the best business writers in the industry. Our business writers have a lot of experience in the field. They are reliable, and you can be assured of a high-grade paper. They are able to handle business papers of any subject, length, deadline, and difficulty!


We boast of having some of the most experienced statistics experts in the industry. Our statistics experts have diverse skills, expertise, and knowledge to handle any kind of assignment. They have access to all kinds of software to get your assignment done.


Writing a law essay may prove to be an insurmountable obstacle, especially when you need to know the peculiarities of the legislative framework. Take advantage of our top-notch law specialists and get superb grades and 100% satisfaction.

What discipline/subjects do you deal in?

We have highlighted some of the most popular subjects we handle above. Those are just a tip of the iceberg. We deal in all academic disciplines since our writers are as diverse. They have been drawn from across all disciplines, and orders are assigned to those writers believed to be the best in the field. In a nutshell, there is no task we cannot handle; all you need to do is place your order with us. As long as your instructions are clear, just trust we shall deliver irrespective of the discipline.

Are your writers competent enough to handle my paper?

Our essay writers are graduates with bachelor's, masters, Ph.D., and doctorate degrees in various subjects. The minimum requirement to be an essay writer with our essay writing service is to have a college degree. All our academic writers have a minimum of two years of academic writing. We have a stringent recruitment process to ensure that we get only the most competent essay writers in the industry. We also ensure that the writers are handsomely compensated for their value. The majority of our writers are native English speakers. As such, the fluency of language and grammar is impeccable.

What if I don’t like the paper?

There is a very low likelihood that you won’t like the paper.

Reasons being:

  • When assigning your order, we match the paper’s discipline with the writer’s field/specialization. Since all our writers are graduates, we match the paper’s subject with the field the writer studied. For instance, if it’s a nursing paper, only a nursing graduate and writer will handle it. Furthermore, all our writers have academic writing experience and top-notch research skills.
  • We have a quality assurance that reviews the paper before it gets to you. As such, we ensure that you get a paper that meets the required standard and will most definitely make the grade.

In the event that you don’t like your paper:

  • The writer will revise the paper up to your pleasing. You have unlimited revisions. You simply need to highlight what specifically you don’t like about the paper, and the writer will make the amendments. The paper will be revised until you are satisfied. Revisions are free of charge
  • We will have a different writer write the paper from scratch.
  • Last resort, if the above does not work, we will refund your money.

Will the professor find out I didn’t write the paper myself?

Not at all. All papers are written from scratch. There is no way your tutor or instructor will realize that you did not write the paper yourself. In fact, we recommend using our assignment help services for consistent results.

What if the paper is plagiarized?

We check all papers for plagiarism before we submit them. We use powerful plagiarism checking software such as SafeAssign, LopesWrite, and Turnitin. We also upload the plagiarism report so that you can review it. We understand that plagiarism is academic suicide. We would not take the risk of submitting plagiarized work and jeopardize your academic journey. Furthermore, we do not sell or use prewritten papers, and each paper is written from scratch.

When will I get my paper?

You determine when you get the paper by setting the deadline when placing the order. All papers are delivered within the deadline. We are well aware that we operate in a time-sensitive industry. As such, we have laid out strategies to ensure that the client receives the paper on time and they never miss the deadline. We understand that papers that are submitted late have some points deducted. We do not want you to miss any points due to late submission. We work on beating deadlines by huge margins in order to ensure that you have ample time to review the paper before you submit it.

Will anyone find out that I used your services?

We have a privacy and confidentiality policy that guides our work. We NEVER share any customer information with third parties. Noone will ever know that you used our assignment help services. It’s only between you and us. We are bound by our policies to protect the customer’s identity and information. All your information, such as your names, phone number, email, order information, and so on, are protected. We have robust security systems that ensure that your data is protected. Hacking our systems is close to impossible, and it has never happened.

How our Assignment  Help Service Works

1.      Place an order

You fill all the paper instructions in the order form. Make sure you include all the helpful materials so that our academic writers can deliver the perfect paper. It will also help to eliminate unnecessary revisions.

2.      Pay for the order

Proceed to pay for the paper so that it can be assigned to one of our expert academic writers. The paper subject is matched with the writer’s area of specialization.

3.      Track the progress

You communicate with the writer and know about the progress of the paper. The client can ask the writer for drafts of the paper. The client can upload extra material and include additional instructions from the lecturer. Receive a paper.

4.      Download the paper

The paper is sent to your email and uploaded to your personal account. You also get a plagiarism report attached to your paper.

smile and order essaysmile and order essay PLACE THIS ORDER OR A SIMILAR ORDER WITH US TODAY AND GET A PERFECT SCORE!!!

order custom essay paper