Volume 13, Issue 4
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Volume 13, Issue 4
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| Jul/Aug 1999 | |
| Theme: Knowledge Based Systems |
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To Volume 13, Issue 3
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To Volume 13, Issue 5
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Features
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| Managed Business Rules: A Repository Based Approach Henry Seiler explores rules as a concept, practice, methodology, and as a requirements technique to optimize the software development process. |
| Successful Knowledge Management Systems: An Expert Systems Approach Srinivas Krovvidy describes the successful medium-to-large-size knowledge management project based on past experience. |
| AI@Work Intelligent Tutoring System on the Internet: A Case-Based Approach; Modeling Marketing Response for Communications Clients; Wireless Gets the Message From Object Oriented Technology. |
| IVAN: An Expert System for Pain Control and Symptom Relief in Advanced Cancer Jonathan Thompson discusses a case-based knowledge system that can improve the quality of life by helping to control pain. |
| Fuzzy Logic in Knowledge Engineering: Effectively Articulating Rules Daniel J. Fonseca uses fuzzy logic to overcome difficulties encountered during the knowledge acquisition process. |
| What Does Your Company Really Do? Data Fusion in the Era of Knowledge Management Earl Cox points out how machine learning technologies provide the core understanding of how a business process actually works. |
| Striving for Imprecision: Fuzzy Knowledge Bases for Business Process Modeling Earl Cox describes Fuzzy Knowledge Bases, combining both the semantics of information with the imprecision found in nearly all real-world problems. |
| Regulars | ||
| Editorial | ||
| Secret Agent Man - Surrounded and Outnumbered - As Things Start to Think | by Don Barker | |
| Intelligence Files - On-line Trading: The New National Pastime | by David Blanchard | |
| AI and the Net - Agents in Space - Reporting Via the Web | by Mary Kroening | |
| The Book Zone - Feature Extraction, Construction and Selection and Statistical Analysis of Categorical Data | by Will Dwinnell | |
| Product Updates ----------------------------> | 16 late breaking product announcements from around the world in the fields of: | |
| Announcements | Business Rules | |
| Data Mining | Intelligent Agents | |
| Knowledge Based Systems | Rule-Based Systems | |
| Simulation and Modeling | Tools | |
| Web | ||
| Product Service Guide - Provides access to information on an entire category of products | ||
| PC AI Blackboard - AI advertisers bulletin board | ||
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Advertiser List for 13.4
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| AAAI | Gensym | Soft Warehouse Inc |
| AI Developers Inc. | KnowledgeBroker Inc | Sonalysts Inc |
| Amzi! Inc | Logic Programming Associates Ltd | StatSoft |
| AND Corporation | Megaputer Intelligence | System Dynamics International Inc |
| Applied Logic Systems | Metus | The Haley Enterprise Inc |
| ATTAR Software USA | Morgan Kaufman | Ward Systems Group Inc |
| BitStar International | Multilogic | WizSoft Inc |
| BotSpot | NAU/CDI | |
| CHI Systems Inc | Neuron Data | |
| California Scientific Software | PC AI | |
| DCI | Production Systems Technology | |
| DTSoftware | Prolog Development Center | |
| Emerald Intelligence | Salford Systems | |
| Frontier GlobalCenter | SHAI |
| Firing up the search engine, access to 32,936 Knowledge Management sites was instantly available. If you think about it, developing a process for knowledge management of just these sites would be a daunting task. Can you imagine the effort to try and manage the basic knowledge we consider common sense? Well one of the sites that came up on this search, www.cyc.com , (Cycorp, Inc.) founded by Doug Lenat (a past contributor to PC AI), is trying to do just that. His goal is to build an immense multi-contextual knowledge base (Cyc), built upon a core of over 1,000,000 hand-entered assertions (or "rules") designed to capture a large portion of what we normally consider consensus knowledge about the world. It does this by using an inference engine, a set of interface tools, and a number of special-purpose application modules running on Unix, Windows NT, and other platforms. For example, Cyc understands that trees are usually outdoors, people stop buying things once they die, and containers of liquid should be carried rightside-up. How do you use this information you might ask? Let's look at an example with a database containing two tables. The first table, containing personnel, consists of three fields: the person's name, job title, and employer. The employer table has two fields: the employer's name and the state where the employer is located. | |
| Now let's use this information to determine who holds an advanced degree and lives in New England. Notice that the tables don't say anythign about degrees people hold or where they live, and they don't mention New England. But Cyc knows that doctors, lawyers, and professors hold advanced degrees, that people generally live near their place of work, and that New England is comprised of six specific states, so it converts this query into one for doctors, lawyers, and professors whose employer is located in one of those six sates. For another example, Cyc can find the match between a user's query for "pictures of strong, adventurous people" and an image whose caption reads simply "a man climbing a cliff." | |
| Today's computer databases provide immediate access to the data that has been placed in them but the knowledge that resides there is still pretty much untouched. This issue focuses on the AI technologies that are assisting in this knowledge retrieval. Henry Seiler leads off with a look at business rules, an efficient approach to software development in "Managed Business Rules." In "Successful Knowledge Management Systems," Srinivas Krovvidy uses a wealth of expert system experience to outline the development of a successful medium-to-large size knowledge management system. Daniel Fonseca and Earl Cox both look at the use of fuzzy logic in knowledge engineering while examining knowledge acquisition and business process modeling. | |
| To round out this issue, we have articles on an expert system developed for pain control, an intelligent tutoring system on the Internet and case-based modeling marketing. We also have our creative regulars showing up with their usual feast of great articles including on-line stock trading, intelligent agents in space, data mining, and more. | |
| Enjoy! | |
| Terry Hengl |
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