systems that know and understand and think and learn
Here is a graphic on how various reasoning technologies fit the practical requirements for reasoning discussed below:
This proved surprisingly controversial during correspondence with colleagues from the Vulcan work on SILK and its evolution at http://www.coherentknowledge.com.
The requirements that motivated this were the following: Continue reading “Requirements for Logical Reasoning”
Well this is a blast from the past… I was working on “the next post” about how Watson, Google, and the semantic web threaten the major BRMS systems when I found myself rewriting that what matters about Rete Algorithm-based production rule systems is that the order of the rules does not matter. It occurred to me that I must have written that a dozen times or more in my career so I did a Google and found this old paper has been on-line at the Free Library On-Line since 2006! I recall writing this in the early nineties and the owner of an AI magazine telling me he carried it with him for the better part of a year (and printing it in his magazine, I think). Some of you may enjoy reading it again!
Best,
Paul
1. WHAT IS AN EXPERT SYSTEM?
An expert system is a program that includes expertise.
2. WHAT IS EXPERTISE?
Expertise is knowledge that enables an individual, group or program to perform an intellectual task better than that task can be performed without the expertise.
3. HOW IS EXPERTISE ENCODED WITHIN A PROGRAM?
Expertise, being knowledge, falls into a few general categories.
Some knowledge is declarative knowledge which doesn’t do anything but which represents truth or state. Such knowledge can be stored in a standard database.
Some knowledge is algorithmic and can therefore be flow charted. Such knowledge can be expressed as a routine in any procedural programming language. Not everything a person or organization knows can be easily translated into data structures and flow charts, however.
4. SO FLOW-CHARTABLE ALGORITHMS SHOULD BE CODED PROCEDURALLY?
Yes, if the flow chart is actually produced, if it is not extraordinarily complex, and if there is no reason to believe that the flow chart is incorrect or incomplete. After all, procedural languages and flow charts are isomorphic, anything that can be expressed in one can be expressed using the other. However, if it is hard to produce a flow chart, it will be even more difficult to produce a working program using a procedural language.
I had the pleasure of visiting with some fine folks at Cycorp in Austin, Texas recently. Cycorp is interesting for many reasons, but chiefly because they have expended more effort developing a deeper model of common world knowledge than any other group on the planet. They are different from current semantic web startups. Unlike Metaweb‘s Freebase, for example, Cycorp is defining the common sense logic of the world, not just populating databases (which is an unjust simplification of what Freebase is doing, but is proportionally fair when comparing their ontological schemata to Cyc’s knowledge). Not only does Cyc have the largest and most practical ontology on earth, they have almost incomprehensible numbers of formulas[1] describing the world. Continue reading “Cyc is more than encyclopedic”
I was prompted to post this by request from Mark Proctor and Peter Lin and in response to recent comments on CEP and backward chaining on Paul Vincent’s blog (with an interesting perspective here).
I hope those interested in artificial intelligence enjoy the following paper . I wrote it while Chief Scientist of Inference Corporation. It was published in the International Joint Conference on Artificial Intelligence over twenty years ago.
The bottom line remains:
We implemented this in Inference Corporation’s Automated Reasoning Tool (ART) in 1984. And we implemented it again at Haley a long time ago in a rules langauge we called “Eclipse” years before Java.
Regretably, to the best of my knowledge, ART is no longer available from Inference spin-off Brightware or its further spin-off, Mindbox. To the best of my knowledge, no other business rules engine or Rete Algorithm automatically subgoals, including CLIPS, JESS, TIBCO Business Events (see above), Fair Isaac’s Blaze Advisor, and Ilog Rules/JRules. After reading the paper, you may understand that the resulting lack of robust logical reasoning capabilities is one of the reasons that business rules has not matured to a robust knowledge management capability, as discussed elsewhere in this blog. Continue reading “Goals and backward chaining using the Rete Algorithm”
The ART syntax lives on in yet another product!
JBOSS Rules (formerly Drools) just described its imminent support for rules expressed in the CLIPS syntax here.
NASA derived CLIPS from the syntax of Inference Corporation’s Automated Reasoning Tool (ART) in the mid-80s. I designed and implemented the ART syntax with Chuck Williams on a team with Brad Allen and Mark Wright.
CLIPS didn’t have many of the features of ART (including an ATMS or backward chaining, for example), but it Continue reading “Haley / ART syntax lives on in open-source Java rules”