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Volume 15, Issue 3
Also check out the PC AI Article Summary List for the past 16 years
May/Jun 2001
Theme: Intelligent Business Rules & Fuzzy Logic

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Fuzzy Logic and the Measures of Certainty in eCommerce Expert Systems Earl Cox examines the organization of business rule systems and how, through the use of Fuzzy Logic, rules should be organized and written to address the uncertainty and imprecision of business decisions.
AI@Work Prolog Knowledge Base Supplies Embedded and Web Remote Control: Intelligence for Microscopes; Automated Knowledge Discovery with Data-Driven Knowledge Engineering: Fuzzy Logic Based Approach - Simulates Naval replenishment maneuvers at sea.
The Rise of Knowledge for Sale Dan Rasmus discusses how Internet knowledge markets might facilitate the discovery of those that have knowledge and match them with those that need knowledge.
Genetic Algorithms and Intelligent Agents Team Up: Techniques for Data Assembly, Preprocessing, Modeling, and Decision Optimization Larry M. Deschaine, Jennifer McCormack, Dorian Pyle, Frank Francone discuss a set of techniques for optimal real-time decision making from distributed, heterogeneous information found in financial, industrial, and scientific data.
Simulation of Petri Nets in Prolog: Modeling Dynamic System Behavior Girish Keshav Palshikar illustrates how symbolic logic programming can model dynamic, concurrent, asynchronous distributed or stochastic behavior found in protocols, office automation, industrial control, and real-time embedded systems.
"Ruling" the World of Web Services: Application Servers from Java Beans Tom Ronk and Jeff Weyer describe how Enterprise Java Beans and Component-Based application design is becoming the foundation of the next generation B2B application integration.

AI and the Net - Research in Intelligent Technology - Research projects that may lead to future intelligent applications  
The Book Zone - SQL for Smarties - Obtaining the most from a database  
Product Updates -------------------------------> 25 late breaking product announcements from around the world in the fields of:
  Business Forecasting Business Rules
  Data Mining Decision Support
  Expert System Development Tools Intelligent Tools
  Knowledge Based Systems Modeling and Simulation
  Natural Language Processing Voice and Speech Recognition
  Announcements Conferences
Product Service Guide - Provides access to information on an entire category of products    
PC AI Blackboard - AI advertisers bulletin board    

Advertiser List for 15.3
AAAI Exsys Recruiting QMC
AI Developers Frontline Systems Rule Automation
Amzi! Inc IJCAI Rule Machines Corporation
And Corporation IJCNN Salesmation
Arcanum Logic Programming Associates Ltd StatSoft
Attar Software MathWorks The Modeling Agency
Babylon Interactive Megaputer WizSoft Inc.
BioComp NeuroDimension  
Cyber Squire PC AI Back Issues  
DCI PC AI Banner Ad  
dtSearch PC AI Reprints  
  Prolog Development Center  
Exsys Production System Technologies  

Artificial Intelligence, a sub-domain of computer science, focues on the development and deployment of intelligent software and hardware systems that emulate human reasoning techniques and capabilities. Knowledge base systems, a sub-field of AI, emulates the human decision-making process by employing different information representation and manipulation schemes:
  Rules-Based systems, which capture knoweldge in if-tehn statements or rules, are an intuitive and wide spread knowledge representation. Knowledge related to conditions or limits encodes easily, as does knowlege that must match patterns. (see Prolog Knowledge Base Supplies Embedded and Web Remote Control: Intelligence for Microscopes)
  Artificial neural networks, derived from computer-based reserach on modeling human brain behavior, are excellent at associative problems - the type of progblem where finding an existing input set that is similar provides an acceptable solution. That is, relevant concepts, represented by the neural network, are compared against the new problem data. The key advantage here, is that example data sets train the neural network versus developing a program comes at a price - this knowledge is not in human readable form.
  Fuzzy logic, with its roots in set theory, was developed to handle situations where set membership is not clearly defined. An exapmle would be the thermometer vlues for the terms "hot", "cold" and "warm" - at what temperature does boiling water become warm or cold, as it cools. This can also vary with the membership designer. (see Fuzzy Logic and the Measures of Certainty in eCommerce Expert Systems)
  Decision trees, which capture the structure of the decision-making processes simplify development of configuration and diagnostic applications. When knowledge is structured into a series of steps and decision points, this can be a very efiicient representation. Since decision trees are developed before their use, they may lack the flexibility of other representation.
  Model-Based Reasoning, which uses a mathematical model to emulate real processes or catpure knowledge, was initially created to support industrial processes (such as refinery or manufacturing). Since changes are applied to the model and the results verified, this representation can predict the effect of future activities - such as valve or environmental changes. (see Automated Knowledge Discovery with Data-Driven Knowldge Engineering: Fuzzy Logic Based Apporach as well as Simulation of Petri Nets in Prolog: Modeling Dynamic System Behavior)
  Each approach has its strengths and weaknesses but when combined they improve their efficiency and overcome inherent weakness of the standalone represenation. Many of the articles in this issue illustrate this hybrid approach to using AI technolgy to solve real world problems (see Genetic Algorithms and Intelligent Agents Team Up: Techniques for Data Assembly, Preprocessing, Modeling, and Optimizing Decisions)
  Models can be constructed from these, and a number of other knowledge representations, capturing the crucial part of business processes. By manipulating these models, it is possible to predict the effects of various actions. Built as part of a knowlege base system, the models can predict outcomes based on different business scenarios. This type of reasoning is very useful as part of a sophisticated decision support system.
  Once these systems are in place, it is possible to collect valuable information and knowledge. It may now be possible to buy and sell that knowledge on the Internet (seeThe Rise of Knowlege For Sale)
  We find that the term business rules, referring to the knowlege that is important to operating a business, is frequently confused with rule-based systems (a type of knowlege representation). We hope that after reading some of the articles in this issue, this distinction will be clear. (see "Ruling" the World of Web Services: Application Servers from Java Beans).
  Terry Hengl

Volume 15-----------------------> Issue 1 (Jan/Feb 2001)   Volume 15 Index (2001)
  Issue 2 (Mar/Apr 2001)   Volume 14 Index (2000)
Issue 3 (May/Jun 2001)   Volume 13 Index (1999)
Issue 4 (Jul/Aug 2001)   Volume 12 Index (1998)
Issue 5 (Sep/Oct 2001)   Volume 11 Index (1997)
Issue 6 (Nov/Dec 2001)   Volume 10 Index (1996)
      Volume 9 Index (1995)
      Volume 8 Index (1994)

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