William R. Swartout

Enabling technology for knowledge sharing

Building new knowledge-based systems today usually entails constructing new knowledge bases from scratch. It could instead be done by assembling reusable components. System developers would then only need to worry about creating the specialized knowledge and reasoners new to the specific task of their system. This new system would interoperate with existing systems, using them to perform some of its reasoning. In this way, declarative knowledge, problem- solving techniques, and reasoning services could all be shared among systems. This approach would facilitate building bigger and better systems cheaply. The infrastructure to support such sharing and reuse would lead to greater ubiquity of these systems, potentially transforming the knowledge industry. This article presents a vision of the future in which knowledge-based system development and operation is facilitated by infrastructure and technology for knowledge sharing. It describes an initiative currently under way to develop these ideas and suggests steps that must be taken in the future to try to realize this vision.

On the inevitable intertwining of specification and implementation

Contrary to recent claims that specification should be completed before implementation begins, this paper presents two arguments that the two processes must be intertwined. First, limitations of available implementation technology may force a specification change. For example, deciding to implement a stack as an array (rather than as a linked list) may impose a fixed limit on the depth of the stack. Second, implementation choices may suggest augmentations to the original specification. For example, deciding to use an existing pattern-match routine to implement the search command in an editor may lead to incorporating some of the routines features into the specification, such as the ability to include wild cards in the search key. This paper elaborates these points and illustrates how they arise in the specification of a controller for a package router.

Toward the holodeck: integrating graphics, sound, character and story

We describe an initial prototype of a holodeck- like environment that we have created for the Mission Rehearsal Exercise Project. The goal of the project is to create an experience learning system where the participants are immersed in an environment where they can encounter the sights, sounds, and circumstances of real-world scenarios. Virtual humans act as characters and coaches in an interactive story with pedagogical goals.

XPLAIN: a system for creating and explaining expert consulting programs

Traditional methods for explaining programs provide explanations by converting the code of the program or traces of its execution to English. While such methods can sometimes adequately explain program behavior, they typically cannot provide justifications for that behavior. That is, such systems cannot tell why what the system is doing is a reasonable thing to be doing. The problem is that the knowledge required to provide these justifications was used to produce the program but is itself not recorded as part of the code, and hence is unavailable. The XPLAIN system uses an automatic programmer to generate a consulting program by refinement from abstract goals. The automatic programmer uses a domain model, consisting of descriptive facts about the application domain, and a set of domain principles which prescribe behavior and drive the refinement process forward. By examining the refinement structure created by the automatic programmer, XPLAIN provides justifications of the code. XPLAIN has been used to re-implement major portions of a Digitalis Therapy Advisor and provides superior explanations of its behavior.

Toward a new generation of virtual humans for interactive experiences

Virtual humans - autonomous agents that support face-to-face interaction in a variety of roles - can enrich interactive virtual worlds. Toward that end, the Mission Rehearsal Exercise project involves an ambitious integration of core technologies centered on a common representation of task knowledge.

Toward virtual humans

This article describes the virtual humans developed as part of the Mission Rehearsal Exercise project, a virtual reality-based training system. This project is an ambitious exercise in integration, both in the sense of integrating technology with entertainment industry content, but also in that we have joined a number of component technologies that have not been integrated before. This integration has not only raised new research issues, but it has also suggested some new approaches to difficult problems. We describe the key capabilities of the virtual humans, including task representation and reasoning, natural language dialogue, and emotion reasoning, and show how these capabilities are integrated to provide more human-level intelligence than would otherwise be possible.

Enhanced Maintenance and Explanation of Expert Systems Through Explicit Models of Their Development

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.

Ada and grace: toward realistic and engaging virtual museum guides

To increase the interest and engagement of middle school students in science and technology, the InterFaces project has created virtual museum guides that are in use at the Museum of Science, Boston. The characters use natural language interaction and have near photoreal appearance to increase and presents reports from museum staff on visitor reaction.

Explanations in knowledge systems: design for explainable expert systems

The explainable expert systems framework (EES), in which the focus is on capturing those design aspects that are important for producing good explanations, including justifications of the systems actions, explications of general problem-solving strategies, and descriptions of the systems terminology, is discussed. EES was developed as part of the Strategic Computing Initiative of the US Dept. of Defenses Defense Advanced Research Projects Agency (DARPA). both the general principles from which the system was derived and how the system was derived from those principles can be represented in EES. The Program Enhancement Advisor, which is the main prototype on which the explanation work has been developed and tested, is presented. PEA is an advice system that helps users improve their Common Lisp programs by recommending transformations that enhance the users code. How EES produces better explanations is shown.<<ETX>>

Natural Language Generation in Artificial Intelligence and Computational Linguistics

One of the aims of Natural Language Processing is to facilitate .the use of computers by allowing their users to communicate in natural language. There are two important aspects to person-machine communication: understanding and generating. While natural language understanding has been a major focus of research, natural language generation is a relatively new and increasingly active field of research. This book presents an overview of the state of the art in natural language generation, describing both new results and directions for new research. The principal emphasis of natural language generation is not only to facili tate the use of computers but also to develop a computational theory of human language ability. In doing so, it is a tool for extending, clarifying and verifying theories that have been put forth in linguistics, psychology and sociology about how people communicate. A natural language generator will typically have access to a large body of knowledge from which to select information to present to users as well as numer of expressing it. Generating a text can thus be seen as a problem of ous ways decision-making under multiple constraints: constraints from the propositional knowledge at hand, from the linguistic tools available, from the communicative goals and intentions to be achieved, from the audience the text is aimed at and from the situation and past discourse. Researchers in generation try to identify the factors involved in this process and determine how best to represent the factors and their dependencies.