Antony J. (Tony) Morgan (born c. 1944) is a British computer scientist, data modeling consultant, and Professor in computer science at INTI International University. He is known for his work on (2002) "Business rules and information systems," [1] and the 2010 "Information modeling and relational databases," co-authored with Terry Halpin. [2]
Morgan obtained his BA in Earth Sciences from The Open University, his BSc in Computer Systems Engineering from Coventry University, where in 1984 he also obtained his MSc in Control Engineering. In 1988 he obtained his PhD, Computer Science from University of Cambridge with a thesis on automated decision-making using qualitative reasoning. [3]
Morgan started his career in industry and worked on "projects in Europe and the US with companies such as Unisys and EDS (now part of Hewlett-Packard) across diverse industries such as Government, Transportation, Aerospace and Financial Services." [4] In the late 1990s he worked for SD-Scicon PLC, which was affiliated with the National Computing Centre in Manchester. In 1988 he co-edited "Blackboard Systems. The Insight Series in Artificial Intelligence" with Robert S. Engelmore (1935-2003), and in the early 1990s he published several articles on artificial intelligence and simulation. [5] [6] From 1997 to 2002 he was Senior Consultant at Unisys.
In 2003 he was appointed Professor in computer science and vice president of Enterprise Informatics at Neumont University, and in 2010 he moved to the INTI International University in Malaysia, where he is appointed Professor of computer science. [3]
Morgans research interests focus on "business rules and business processes and the rapid development of high-quality information systems." [4]
In the 2002 "Business Rules and Information Systems," Morgan argued that "Information systems often fail because their requirements are poorly defined." [7] The work is intended to show "IT professionals how to specify more precisely and more effectively what their systems need to do. The key lies in the discovery and application of what are called business rules." [7]
A business rule is defined by Morgan as a "compact and simple statement that represents some important aspect of a business. By capturing the rules for your business--the logic that governs its operation--you will gain the ability to create systems fully aligned with your business needs." [7]
In "Business Rules and Information Systems," Morgan (2002) described business architecture as "a way of describing businesses and what they do or intend to do in the future. The building blocks available represent various aspects of the business. No single aspect is the most important; all are necessary to give a balanced picture of what a business is all about. We can use the business architecture to produce business models: descriptions of specific businesses, couched in a consistent and well-defined vocabulary. The business architecture corresponds reasonably to the Conceptual viewpoint in the Zachman Framework." [8]
Articles, a selection: [9]
Douglas Bruce Lenat is the CEO of Cycorp, Inc. of Austin, Texas, and has been a prominent researcher in artificial intelligence; he was awarded the biannual IJCAI Computers and Thought Award in 1976 for creating the machine learning program, AM. He has worked on machine learning, knowledge representation, "cognitive economy", blackboard systems, and what he dubbed in 1984 "ontological engineering". He has also worked in military simulations, and numerous projects for US government, military, intelligence, and scientific organizations. In 1980, he published a critique of conventional random-mutation Darwinism. He authored a series of articles in the Journal of Artificial Intelligence exploring the nature of heuristic rules.
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Terence Aidan (Terry) Halpin is an Australian computer scientist who is known for his formalization of the Object Role Modeling notation.
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Clive Finkelstein is an Australian computer scientist, known as the "Father" of information engineering methodology.
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Gerardus Maria "Sjir" Nijssen is a Dutch computer scientist, former professor of computer science at the University of Queensland, consultant, and author. Nijssen is considered the founder of verbalization in computer science, and one of the founders of business modeling and information analysis based on natural language.
Gennady Simeonovich Osipov was a Russian scientist, holding a Ph.D. and a Dr. Sci. in theoretical computer science, information technologies and artificial intelligence. He was the vice-president of the Institute for Systems Analysis of the Russian Academy of Sciences, professor at the Moscow Institute of Physics and Technology, and at Bauman Moscow State Technical University. Osipov has contributed to the Theory of Dynamic Intelligent Systems and heterogeneous semantic networks used in applied intelligent systems.
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The following is provided as an overview of and topical guide to databases:
Henderik Alex (Erik) Proper is a Dutch computer scientist, an FNR PEARL Laureate, and a senior research manager within the Computer Science (ITIS) department of the Luxembourg Institute of Science and Technology (LIST). He is also adjunct professor in data and knowledge engineering at the University of Luxembourg. He is known for work on conceptual modeling, enterprise architecture and enterprise engineering.
Ryszard S. Michalski was a Polish-American computer scientist. Michalski was Professor at George Mason University and a pioneer in the field of machine learning.
This glossary of artificial intelligence is a list of definitions of terms and concepts relevant to the study of artificial intelligence, its sub-disciplines, and related fields. Related glossaries include Glossary of computer science, Glossary of robotics, and Glossary of machine vision.
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Neuro-symbolic AI integrates neural and symbolic AI architectures to address complementary strengths and weaknesses of each, providing a robust AI capable of reasoning, learning, and cognitive modeling. As argued by Valiant and many others, the effective construction of rich computational cognitive models demands the combination of sound symbolic reasoning and efficient machine learning models. Gary Marcus, argues that: "We cannot construct rich cognitive models in an adequate, automated way without the triumvirate of hybrid architecture, rich prior knowledge, and sophisticated techniques for reasoning.". Further, "To build a robust, knowledge-driven approach to AI we must have the machinery of symbol-manipulation in our toolkit. Too much of useful knowledge is abstract to make do without tools that represent and manipulate abstraction, and to date, the only machinery that we know of that can manipulate such abstract knowledge reliably is the apparatus of symbol-manipulation."