This article is a summary of the Workshop on Explanation held during the 1988 National Conference on Artificial Intelligence in St. Paul, Minnesota. The purpose of the workshop was to identify key research issues in the rapidly emerging area of expert system explanation.

Phoenix is a real-time, adaptive planner that manages forest fires in a simulated environment. Alternatively, Phoenix is a search for functional relationships between the designs of agents, their behaviors, and the environments in which they work. In fact, both characterizations are appropriate and together exemplify a research methodology that emphasizes complex, dynamic environments and complete, autonomous agents. This article describes the underlying methodology and illustrates the architecture and behavior of Phoenix agents.

Advances in computer hardware and software and engineering methodologies in the 1960s and 1970s led to an increased use of computers by engineers. AI techniques, in particular the knowledge-based system (KBS) technology, offer a methodology to solve these ill-structured design problems. In this article, we describe several research projects that utilize KBS techniques for design automation. These projects are (1) the Criteria Yielding, Consistent Labeling with Optimization and Precedents-Based System (CYCLOPS), which generates innovative designs by using a three-stage process: normal search, exploration, and adaptation; (2) the Concept Generator (CONGEN), which is a domain independent framework for conceptual or preliminary design; (3) Constraint Manager (CONMAN), which is a constraint-management system that performs the evaluation and consistency maintenance of constraints arising in design; (4) the distributed and integrated environment for computer-aided engineering (DICE), which facilitates coordination, communication, and control during the entire design and construction/manu-facturing phases; and (5) DESIGN-KIT, which can be envisioned as a new generation of computer-aided engineering environment for process-engineering applications.

Problem-solving techniques such as modeling, simulation, optimization, and network analysis have been used extensively to help agricultural scientists and practitioners understand and control biological systems. Many of the models and simulations that have been developed lack a user interface which enables people other than the developer to use them. As a result, several scientists are integrating knowledge-based- system (KBS) technology with conventional problem-solving techniques to increase the robustness and usability of their systems. Part of the AAAI Applied Workshop Series, the meeting was intended to bring together researchers and practitioners active in applying AI concepts to agricultural problems.

This article records the results of an experiment in which a survey of AI and expert systems (ES) literature was attempted using Science Citation Indexes. The survey identified a sample of authors and institutions that have had a significant impact on the historical development of AI and ES. However, it also identified several glaring problems with using Science Citation Indexes as a method of comprehensively studying a body of scientific research. Accordingly, the reader is cautioned against using the results presented here to conclude that author A is a better or worse AI researcher than author B.

Review of Machine Translation: Past, Present, Future

The jacket notes to this book by James Allen say it is the most comprehensive, in-depth book to date covering all major aspects of natural language processing. This claim is probably realistic. The jacket notes to this book by James Allen say it is the most comprehensive, in-depth book to date covering all major aspects of natural language processing. This claim is probably realistic.

The contributions to this workshop indicate substantial advances in the technical foundations of the field. They also show that it is time to evaluate the existing approaches to commonsense reasoning problems.

The specific motivations underlying this research include the following propositions: (1) Reasoning from experimental evidence contained in the clinical literature is central to the decisions physicians make in patient care. Furthermore, the model can help us better understand the general principles of reasoning from experimental evidence both in medicine and other domains. Roundsman is a developmental computer system that draws on structured representations of the clinical literature to critique plans for the management of primary breast cancer. Roundsman is able to produce patient-specific analyses of breast cancer-management options based on the 24 clinical studies currently encoded in its knowledge base.