Dynamic Web content provides us with time-sensitive and continuously changing data. To glean up-to-date information, users need to regularly browse, collect and analyze this Web content. Without proper tool support this information management task is tedious, time-consuming and error prone, especially when the quantity of the dynamic Web content is large, when many information management services are needed to analyze it, and when underlying services/network are not completely reliable. This paper describes a multi-level, lifecycle (design-time and run-time) coordination mechanism that enables rapid, efficient development and execution of information management applications that are especially useful for processing dynamic Web content. Such a coordination mechanism brings dynamism to coordinating independent, distributed information management services. Dynamic parallelism spawns/merges multiple execution service branches based on available data, and dynamic run-time reconfiguration coordinates service execution to overcome faulty services and bottlenecks. These features enable information management applications to be more efficient in handling content and format changes in Web resources, and enable the applications to be evolved and adapted to process dynamic Web content.

Model-based interface design can save substantial effort in building help systems for interactive applications by generating help automatically from the model used to implement the interface, and by providing a framework for developers to easily refine the automatically-generated help texts.This paper describes a system that generates hypertext-based help about data presented in application displays, commands to manipulate data, and interaction techniques to invoke commands.The refinement component provides several levels of customization, including programming-by-example techniques to let developers edit directly help windows that the system produces, and the possibility to refine help generation rules.

HUMANOID is a user interface design tool that lets designers express abstract conceptualizations of an interface in an executable form, allowing designers to experiment with scenarios and dialogues even before the application model is completely worked out. Three properties of the HUMANOID approach allow it to do so: a modularization of design issues into independent dimensions, support for multiple levels of specificity in mapping application models to user interface constructs, and mechanisms for constructing executable default user interface implementations from whatever level of specificity has been provided by the designer.

The general architecture and an implementation of a classification-based production system (CLASP) are presented. The main objective is to extend the benefits of classification capabilities in frame systems to the developers of rule-based systems. Two major processes of CLASP, a semantic pattern matcher and a pattern classifier, are described. The semantic pattern matcher extends the pattern matching capabilities of rule-based systems through the use of terminological knowledge. The pattern classifier enables the system to compute a rule's specificity, which is useful for conflict resolution, based on the semantics of its left-hand side. The paradigm not only enhances the reasoning capabilities of rule-based systems, but also helps to reduce the cost of maintaining such systems because definitional knowledge is explicitly represented in a form that facilitates sharing and minimizes duplication of effort.>

Two tools that support cooperation are described: one for the construction of consistent and principled human-computer interfaces and the other for the construction of AI knowledge bases. These tools provide a central repository for design knowledge that otherwise would not be easily shared among users. The AI knowledge representation technology upon which the tools are founded is first described. A knowledge-based approach to interface construction is discussed, and how that approach applies to detecting design conflicts and inconsistencies stemming from two different kinds of team communication failure is illustrated. Next, a knowledge acquisition aid that is utilized within the interface construction paradigm and that also illustrates the same approach to supporting cooperative work is described. Finally, four sources of difficulty in team design efforts, which this approach seeks to address, are reviewed.

The purpose of this symposium is three-fold:

This paper describes two pieces of work on support tools: one for the construction of consistent and principled human-computer interfaces, and the other for the construction of AI knowledge bases. These tools support co-operation by providing a central repository for design knowledge that otherwise would not be easily shared between users. The paper first describes the AI knowledge representation technology upon which the tools are founded. It then discusses a knowledge-based approach to interface construction and illustrates how that approach would apply to detecting design conflicts and inconsistencies stemming from two different kinds of team communication failure. Next, we discuss a knowledge acquisition aid utilized within the interface construction paradigm, which also illustrates the same approach to supporting co-operative work. We conclude by reviewing four sources of difficulty in team design efforts which this approach seeks to address.

Principled development techniques could greatly enhance the understandability of expert systems for both users and system developers. Current systems have limited explanatory capabilities and present maintenance problems because of a failure to explicitly represent the knowledge and reasoning that went into their design. This paper describes a paradigm for constructing expert systems which attempts to identify that tacit knowledge, provide means for capturing it in the knowledge bases of expert systems, and, apply it towards more perspicuous machine-generated explanations and more consistent and maintainable system organization.

We are deeply indebted to the members of ISI Software Division who developed the basic technology and approach for our effort. We especially wish to acknowledge the contributions of our colleagues, Dave Dyer and Matt Morgenstern, to the conceptualization and design of the system.