MAPCon II is the second generation (Muralidhar and Irish, IEEE Journal on Selected Areas in Commumcctions 6(5), 869–873, 1988) of an expert system that interactively guides a user in performing off-line configuration for local area networks that use MAP, the manufacturing automation protocol. This paper describes the configuration task in general and MAPCon in particular.

Though MAPCon's purpose is off-line configuration, its problem domain requires that it accomplish other reasoning objectives in addition to those commonly associated with configuration. It is in the process of being expanded into an on-line network supervisor. We develop a taxonomy of reasoning objectives and show how MAPCon combines two different kinds of reasoning to accomplish its objectives. Our experience confirms that of other researchers, and suggests that building robust, practical systems will require us to understand more clearly the interfaces among different reasoning objectives.

The paper has four parts: 1. a definition of configuration and other reasoning objectives; 2. a summary of the problem domain in which MAPCon operates; 3. a description of MAPCon as the user sees it; 4. a look ‘under the hood’ to see how MAPCon combines different objectives.

A spatial reasoning system must be able to represent and manipulate the location of objects in its world model. There are two general schemes for doing this—absolute symbolic schemes based on an independent coordinate system, and relative schemes which reference objects in relation to other objects of known location. This paper is concerned strictly with the latter.

Given a relative scheme, there are several possible strategies for segmenting space. Two such strategies are identified and discussed. The first, called the situation-specific strategy is the one currently being explored and employed in most spatial reasoning systems. The second, here referred to as the general-purpose or cognitive strategy is the one used by the human cognitive system. It is suggested that while both strategies have outstanding strengths and weakness, the latter holds greater potential for achieving maximal coverage with minimal resources. The paper then proceeds to describe the structure of cognitive models of locative space and to specify how such models can be built from 3-D geometric models.

This description is based on a cognitively motivated implementation called SEE-TELL. SEE-TELL takes as its input a 3-D geometric model and outputs a proposition of the form locative (referent, relatum). The function which maps the input to the output is a two part heuristic procedure. The first part determines the referent and relatum and the second part assigns the locative predicate. The system can assign the predicates on, right-of/left-of, and front-of/back-of. On and right-of/left-of are, respectively, illustrative of invariant and variant locatives. Front-of/back-of allows for a conflict between an ego/observer and a canonical object. These three situations are thought to cover the different classes of problems that can arise in assigning any locative.

The paper concludes by summarizing the findings, identifying the shortcomings and limitations, and making suggestions for future work.

In this report the architecture of an intelligent design machine capable of performing routine design in different design domains is described. This machine is crafted to operate as a part of a larger system driven by a human designer. Notable features are its use of best-first search strategies for problem-solving control and its ability to adjust problem-solving control strategies, perform automated redesign following specification changes, and resolve constraint violations using domain knowledge. The claims made for this machine are analyzed and it is argued that these claims are founded on established principles of design for intelligent systems. An implementation of this architecture in a rule-based system named Proteus is discussed and its operation is examined using as an example the domain of register-transfer-level computer design.

The design of electronic power transformers is an activity that requires the application of well-established algorithms from electromagnetic theory and heuristic, judgmental techniques derived from experience in the design and manufacturing of these devices. This paper describes an object-oriented knowledge-based system, Encore, that was developed at Houston Downhole Sensors (a division of Schlumberger Well Services). Encore combines object-oriented, rule-based, and procedural programming techniques to design 60 Hz power transformers. The system uses a heuristic search strategy to generate design alternatives, and then selects the “best” design based on size and efficiency considerations The heuristics are represented and applied as rules; the electromagnetic components are modelled as frame-like objects. The object-oriented nature of this system facilitated enhancements; by specializing some of the objects comprising a power transformer, a power inductor model was quickly developed. Encore reduces design time from a couple of days to less than an hour; it is being used to design the transformers and inductors of power supplies in new Schlumberger well-logging tools. The system was implemented on a Xerox interlisp Workstation using an object-oriented environment called STROBE.