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The Development of Web Based Expert System for
The Development of Web Based Expert System for

... ABSTRACT : Children are still developing their immune system, so they are very fragile with the threat virus and bacteria. Thus, the sudden health drops are common, yet very often still causing panic and frustration for parents. Mostly parents will go to the doctor practitioner to consult. However, ...
"It`s Going to Kill Us!" and Other Myths About the Future of
"It`s Going to Kill Us!" and Other Myths About the Future of

... deep knowledge representation required for domain-specific tasks such as cardiology, accounting, or law; c) reasoning, which comes in several varieties such as deductive, inductive, temporal, probabilistic, and quantitative; and d) interaction, with people or other machines to collaboratively perfor ...
IOSR Journal Of Humanities And Social Science (IOSR-JHSS)
IOSR Journal Of Humanities And Social Science (IOSR-JHSS)

... Rule Based Expert System Application for Crime against Women Law in Indian judicial system techniques is used to create. „Automated Legal Reasoning Systems.‟ Computer systems that reasons with and apply the law to solve problems. 2.2) Application of Legal Expert System The authors during the study ...
It`s Going to Kill Us! - Information Technology and Innovation
It`s Going to Kill Us! - Information Technology and Innovation

... deep knowledge representation required for domain-specific tasks such as cardiology, accounting, or law; c) reasoning, which comes in several varieties such as deductive, inductive, temporal, probabilistic, and quantitative; and d) interaction, with people or other machines to collaboratively perfor ...
Specialized Business Information Systems
Specialized Business Information Systems

... Principles and Learning Objectives • Artificial intelligence systems form a broad and diverse set of systems that can replicate human decision making for certain types of well-defined problems. – Define the term artificial intelligence and state the objective of developing artificial intelligence s ...
full list of publications - American University of Beirut
full list of publications - American University of Beirut

... 3. Connectionism vs. Symbolic AI: Why Fodor and Pylyshyn are Right, SAINT 2000, Seminars on Artificial Intelligence, Windsor, Ontario, Canada, 2000. 4. Inductive Data Types, Recursion and Complexity Analysis Made Easy (2-hour Tutorial/Lecture), Minnesota State University at Mankato, Minnesota, USA, ...
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Building a Legal Expert System
Building a Legal Expert System

... There is an urgent need to develop a legal expert system for the people to solve their legal troubles in order reduce problems to the people for frequently arising legal solutions or legal queries, there is a need of legal lawyer is mandatory, so there is an urgent need for the use of practical AI s ...
A Successful Interdisciplinary Course on Computational Intelligence
A Successful Interdisciplinary Course on Computational Intelligence

... topics covered include particle network training. swarm optimization [14, 15] , ant colony optimization [16] and bacteria foraging [17]. These The CI course is implemented and taught as a semester algorithms are introduced and their numerous applications are course, usually within sixteen weeks. The ...
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ch10

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Curriculum Vitae, September 2008

... 4. CS101 Topics in Computing, 2004-2007. Algorithms and Problem Solving. I designed and taught six 3-hour classes for this course. Each class used a newly designed one-hour lecture and included 2 hours of problem-based groupwork. The lectures were designed to engage the students’ interest, asking th ...
Classification Problem Solving
Classification Problem Solving

... of problems in terms of shared features have not been entirely satisfactory: Applications-oriented descriptions like “diagnosis” are too general (e.g., the program might not use a device model), and technological terms like “rule-based” don’t describe what problem is being solved [18, 191. Logic has ...
Artificial Intelligence and Environmental Decision Support Systems
Artificial Intelligence and Environmental Decision Support Systems

... The problem of global change is complex in nature and may be represented by various interactions that operate on different spatio-temporal scales. Addressing these issues demands an integrated consideration of relevant interactions between humans and the environment [5]. Information technologies hav ...
An Adoptive Medical Diagnosis System Using Expert System with
An Adoptive Medical Diagnosis System Using Expert System with

... It is moral responsibility of a country to provide the good medical services to their country people because healthy people make healthy country. Medical science field is an enormous field that includes a large number of expertise, knowledge and information [1]. In this paper, a model of Adoptive Me ...
native rendition
native rendition

... that can reason, gather knowledge, plan intelligently, learn, communicate, perceive and manipulate objects. AI also allows users of big data to automate and enhance complex descriptive and predictive analytical tasks that, when performed by humans, would be extremely labour intensive and time consum ...
Music, Intelligence and Artificiality
Music, Intelligence and Artificiality

... clear expression of knowledge which is susceptible to direct implementation by computer. Furthermore, this knowledge is, in principle at least, expressed abstractly without any reference to its application in any particular task. KL-ONE is used to provide the symbolic layer of HARP, a hybrid system ...
Business Process Innovation with Artificial Intelligence
Business Process Innovation with Artificial Intelligence

... preferences of rational agents and determine the actions than an agent should perform when maximizing its utility function. Decision theory has its roots in economics and investigates situations where agents have to deal with uncertainty about the current state of the world and the future (because t ...
A Virtual Archive for the History of AI
A Virtual Archive for the History of AI

... listed in the shaded boxes on the four pages that follow represent many of the major threads and conceptual advances that professionals in AI regard as significant. Many others are listed under the “Classics” link on AITopics. Philosophers writing up to the time of Charles Babbage and Ada Lovelace u ...
chapter 3 slides
chapter 3 slides

Alan Turing and the development of Artificial Intelligence
Alan Turing and the development of Artificial Intelligence

... During the 1970s the success of the expert systems movement (see Section 3.2) became increasingly stifled by the cost of involving experts in the development and maintenance of large rule-based systems. This problem became known as “Feigenbaum’s bottleneck” [10]. However, early experiments with Meta ...
Springer CC Volume proposal
Springer CC Volume proposal

... (vii) Unique Selling Points: Computational Creativity is a relatively new discipline that draws into sharp focus those aspects of computer science that have been present, if often implicitly so, in Artificial Intelligence research ...
Call for Participation - Association for the Advancement of Artificial
Call for Participation - Association for the Advancement of Artificial

... in a diverse, often ill-structured environment and what, if any, organizational constraints must exist to be able to successfully manage knowledge. Issues involved with adding context and other meta-data along with using ontologies to promote the sharing of relevant information are important topics ...
Special Track on Uncertain Reasoning
Special Track on Uncertain Reasoning

... Proceedings of the Twenty-Seventh International Florida Artificial Intelligence Research Society Conference ...
Managing Knowledge for the Digital Firm
Managing Knowledge for the Digital Firm

... Managing data, such as on employees, customers, and vendors. ...
Rule Based Expert System for Medical Diagnosis-A Review
Rule Based Expert System for Medical Diagnosis-A Review

... The purpose of the inference engine is to seek information and relationships from the knowledge base and to provide answers, predictions, and suggestions in the way a human expert would. The inference engine must find the right facts, interpretations, and rules and assemble them correctly. Two types ...
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AI winter

In the history of artificial intelligence, an AI winter is a period of reduced funding and interest in artificial intelligence research. The term was coined by analogy to the idea of a nuclear winter. The field has experienced several hype cycles, followed by disappointment and criticism, followed by funding cuts, followed by renewed interest years or decades later. There were two major winters in 1974–80 and 1987–93 and several smaller episodes, including: 1966: the failure of machine translation, 1970: the abandonment of connectionism, 1971–75: DARPA's frustration with the Speech Understanding Research program at Carnegie Mellon University, 1973: the large decrease in AI research in the United Kingdom in response to the Lighthill report, 1973–74: DARPA's cutbacks to academic AI research in general, 1987: the collapse of the Lisp machine market, 1988: the cancellation of new spending on AI by the Strategic Computing Initiative, 1993: expert systems slowly reaching the bottom, and 1990s: the quiet disappearance of the fifth-generation computer project's original goals.The term first appeared in 1984 as the topic of a public debate at the annual meeting of AAAI (then called the ""American Association of Artificial Intelligence""). It is a chain reaction that begins with pessimism in the AI community, followed by pessimism in the press, followed by a severe cutback in funding, followed by the end of serious research. At the meeting, Roger Schank and Marvin Minsky—two leading AI researchers who had survived the ""winter"" of the 1970s—warned the business community that enthusiasm for AI had spiraled out of control in the '80s and that disappointment would certainly follow. Three years later, the billion-dollar AI industry began to collapse.Hypes are common in many emerging technologies, such as the railway mania or the dot-com bubble. An AI winter is primarily a collapse in the perception of AI by government bureaucrats and venture capitalists. Despite the rise and fall of AI's reputation, it has continued to develop new and successful technologies. AI researcher Rodney Brooks would complain in 2002 that ""there's this stupid myth out there that AI has failed, but AI is around you every second of the day."" In 2005, Ray Kurzweil agreed: ""Many observers still think that the AI winter was the end of the story and that nothing since has come of the AI field. Yet today many thousands of AI applications are deeply embedded in the infrastructure of every industry."" He added: ""the AI winter is long since over.""
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