I should like to lodge a complaint about your editorial standards in the article "An Assessment of Tools for Building Large KB Systems," by William Mettrey, in the winter 1987 [volume 9 number As a primary architect of CRL-Ops and a former KnowledgeCraft class instructor, I had to deal with the general public's misconceptions about forward versus backward chaining systems. Mr. Mettrey's article, in my opinion, is the type which generates the confusion that forward chaining rule systems cannot "backwards chain." This nonsensical view was held by the vast majority of our customers in the KC class. The section on Rule-Based inference implies that backward chaining is done only by Prolog in KC with its statement "by contrast, Knowledge-Craft implements backward chaining by supporting a version of Prolog." Any forward chaining rules system can efficiently implement constrained backward chaining by simply using a goal structure to search for the required knowledge.

Reactions to Darden Editor: We are sympathetic to Lindley Darden's intellectual program. But the various conceptions of abstraction which she discusses are, individually and collectively, inadequate. There are two problems, one concerning the basic nature of abstraction and the other concerning a mechanism by which abstract terms can be related to their definitional base. Darden comes close to the basic nature of abstraction when she asserts that "In difficult cases, forming an abstraction can involve more than merely dropping parts or replacing constants with variables. New, abstract semantic concepts might have to be introduced."

However, if it is incorrect, there must be some relationship between the two that allows them to live harmoniously within a single theory. The nature of this relationship is the focus of this article. In passing we note that the theory of weak problem solvers has been well-developed for over a decade; see Kilsson (1971) for example. Some aspects of MYCIN don't fit the problem reduction For example, a THE AI MAGAZINE Summer 1983 25 production whose action part is a conjunction of atomic formulae corresponds to a separate operator for each atomic formula in the conjunction. MYCIN's search strategy effectively applies such operators in a group.

In 1980, Digital Equipment Corporation began to use a rule-based system called Rl by some and XCON by others to configure VAX-11 computer systems In the intervening years, Rl's knowledge has increased substantially and its usefulness to Digital continues to grow. This article describes what is involved in extending Rl's knowledge base and evaluates Rl's performance during the four year period. "Rl: the formative years" described how a A large number of people have played critical roles in Rl's development. Among those who deserve special mention are John Barnwell, Dick Caruso, Ken Gilbert, Keith Jensen, Allan Kent, Dave Kiernan, Arnold K&t, Dennis O'Connor, and Ed Orciuch. We want to thank Allen Newell, Dennis O'Connor, and Ed Orciuch for their helpful comments on earlier drafts of this article Briefly, given a customer's purchase order, Rl determines what, if any, substitutions and additions have to be made to the order to make it consistent, complete, and produce a number of diagrams showing the spatial and logical relationships among the 50 to 150 components that typically constitute a system.

This article provides a historical background on how AAAI came into existence. It provides a rationale for why we needed our own society. It provides a list of the founding members of the community that came together to establish AAAI. Starting a new society comes with a whole range of issues and problems: What will it be called? How will it be financed?

This article gives a detailed description of True Knowledge: a commercial, open-domain question-answering platform. The system combines a large and growing structured knowledge base of commonsense, factual, and lexical knowledge; a natural language translation system that turns user questions into internal language-independent queries; and an inference system that can answer those queries using both directly represented and inferred knowledge. The system is live and answers millions of questions per month asked by Internet users. Behind the platform is a large and growing knowledge base of the world's knowledge in structured form combining commonsense, factual, and lexical knowledge. Natural language questions are answered by first translating the question into a language-independent query and then executing the query using both knowledge in the knowledge base and additional knowledge generated by a general inference system.

We present a deployed AI system, Calvin, for cosmogenic isotope dating, a domain that is fraught with these difficult issues. Calvin solves these problems using an argumentation framework and a system of confidence that uses twodimensional vectors to express the quality of heuristics and the applicability of evidence. The arguments it produces are strikingly similar to published expert arguments. Calvin is in daily use by isotope dating experts. An automated tool can do boring and repetitive reasoning, freeing experts to do more difficult and creative work.

In contrast to an explicit definition of each individual item, configurable products such as computers, financial service portfolios, and cars are repre sented in the form of a configuration knowledge base that de - scribes the properties of allowed instances. Although the knowledge representation used is different compared to nonconfi gurable products, the decision support requirements remain the same: users have to be supported in finding a solution that fits their wishes and needs. In this article we show how recommendation technologies can be applied for supporting the configuration of products. In addition to existing approaches we discuss relevant issues for future research. Similar to knowledge-based recommendation (Burke 2000) configuration is a process where users specify (and often adapt) their requirements and the configuration system provides feedback. Requirements specifications range from feature value definitions to textual queries specified on an informal level. Feedback is provided, for example, in terms of further questions that need to be answered, solutions (configurations), explanations of solutions, and proposals for relaxations of the user requirements in situations where no solution can be found. A major difference between configuration systems and recommender systems in general is the way in which product knowledge is represented. Configuration systems are operating on a configuration knowledge base (Stumptner 1997), which describes the properties of all allowed instances. In contrast to configuration systems, recommender systems are operating on the basis of an assortment of explicitly defined solution alternatives. The reason for using a configuration knowledge base is the large number of solution alternatives (possible configurations), which make an explicit representation infeasible. Although the used knowledge representations are different, the decision support goal is quite the same for both types of systems: users have to be proactively supported in finding a solution that fits their wishes and needs. Configuration systems often achieve this goal only partially since the amount and complexity of options presented by the configurator outstrip the capability of a user to identify an appropriate solution (configuration). Users are unable to find the features they would like to specify, they are unsure about their preferences regarding complex technical product properties, and they do not know how best to adapt their requirements in the case of inconsistencies (if no solution can be identified).

Some people believe that the expert system field is dead, yet others believe it is alive and well. To gain a better insight into these possible views, the first three world congresses on expert systems (which typically attract representatives from some 45-50 countries) are used to determine the health of the global expert system field in terms of applied technologies, applications, and management. This article highlights some of these findings. An excellent way to gain a global perspective on expert system technology, applications, and management is to examine the world congresses on expert systems (sponsored by the International Society for Intelligent Systems in Rockville, Maryland). The World Congress on Expert Systems was established to bridge the gap between the academician and the practitioner and concentrate on expert system work being performed throughout the world.

I survey four viewpoints about what AI is. I describe a program exhibiting AI as one that can change as a result of interactions with the user. Such a program would have to process hundreds or thousands of examples as opposed to a handful. Because AI is a machine's attempt to explain the behavior of the (human) system it is trying to model, the ability of a program design to scale up is critical. Researchers need to face the complexities of scaling up to programs that actually serve a purpose.