RE: Description of Semantic Web (was Re: Is it just me...)
-----Original Message----- From: Simon St.Laurent [mailto:simonstl@s...] >In the first part, the Web becomes a much more powerful means >for collaboration between people. [...] > >In the second part of the dream, collaborations extend to computers. >Machines become capable of analyzing all the data on the Web - the >content, links, and transactions between people and computers. >A "Semantic Web," which should make this possible, has yet to emerge, >but when it does, the day-to-day mechanisms of trade, bureaucracy, >and our daily lives will be handled by machines talking to machines, >leaving humans to provide the inspiration and inuition. The intelligent >"agents" people have touted for ages will finally materialize. This >machine-understandable Web will come about through the implementation >of a series of technical advancements and social agreements that are >now beginning (and which I describe in the next chapter.) Ok, have there been some advancements in inference engine technology that will improve expert systems on distributed networks? I confess to being unaware of them. Collaboration between people over the network is the first and last reason we use it. Extending it to machines looks logical but beware the real time system problems of superstitious acquisition and cascading. Don't bet the farm on a potentially noisy system. Chaos is chapter 1 in real time control. Machines are very bad when faced with uncertainty. October 1960: the early warning systems in Thule were screaming to launch. The operator turned off the system and found out they were getting return radar echoes from the moon. If it's going to get windy, let it blow AI: From Beyond the Book Metaphor (1991(, 1.6 : The Secret of the Devil and The Deep Blue Sea. "Episodic memory is typically implemented as scripts and rule sets which are invoked when a set of data inputs for an event match a stored set of parametric values in a system model data base. These model sets are knowledge bases operated on by a decision support system which by means of an engine employing techniques such as Demptster-Shafer evidential reasoning and with knowledge of the application, operations and organizational domains can be used to map requirements to capabiliteis and provide best fit solutions." The trick was iteration and a system that learned to detect and remediate chaos inducing feedback. Episodic memory is used to flag known problems that occur when applying the frame-base. To reduce destabilization, tests are performed to ensure the process has executed correctly and to proof for known error sources. Testing is a scheduled event. The system becomes noise-tolerant, not noiseless. Tests can slow it down and that becomes a latency issue (perhaps worse if the comm protocol is stateless but that is an issue to think about). Latency in the hierarchy of control processes is a potential source of chaos. Business objects that are ACID-conformant typically isolate the transactions such that rollback is always possible. However, that means you have to beware of processes with timing issues or hidden couplers if the process is new and or mission critical. The principle of least commitment or just in time binding is applied. Analysis of historical data is used to refine the knowledge base to offset the effects of inadequate rules or noisy signals, that is, reasoning with uncertainty. These were often called "learning systems" and a lot was made of the power of neural network modelds for predicting failures prior to their occurrence such that damping could be applied prior to runaway cascades. The collaboration model Tim is dreaming about used to be called a Type C production system (see Automated Design of Displays for Technical Data; " Westfold, et al, Kestrel Institute under contract to AFHRL, AF Systems Command, Brooks AF Base, TX. Sept 1990). These systems generate displays using relations in the technical data. A generator creates a space of possible displays and a selector prunes this space using rule-based criteria for possible dislays. Similar to today's DHTML systems, one could talk about a tactical interface: created for the particular use based on particular conditions. This is very useful when considering fielding systems that have variant workflows. A navigation method for locating data during production and when fielded is provided. This might be configured with a component hierarchy with cross references. The model based reasoning component can be used to analyse symptoms, diagnose faults, and present tests in the optimum order, then incorporates user feedback or sensor feedback to determine further tests. Most of this doesn't work for so-called, wicked problems. These are unknown-unknowns. However, so we might get back to the services thread, the basic components for the knowledge base were (based on David Hu's work, C/C+ for Expert Systems): o Access controller - decomposes queries, routes subqueries to local data bases, processes data and forwards replies (Similar to workflow layer in current architectures) o Inference engine - backward and forward chaining, demon objects, blackboard communications, meta-strategy execution and control o Weights and weight propagation for fuzzy diagnosis and confidence values for handling uncertainty o Frame or object based representations and rule language including default values for incomplete knowledge reasoning o Editors o Explanation interface (advisor) to help clarify why and how a goal was achieved o Pattern classifier for categorization and incremental update of episodic memory and the system synthesis model And so on and so forth... Len Bullard Intergraph Public Safety clbullar@i... http://www.mp3.com/LenBullard Ekam sat.h, Vipraah bahudhaa vadanti. Daamyata. Datta. Dayadhvam.h
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