Recent general AI conferences show a decline in both the number and the quality of vision papers, but there is tremendous growth in, and specialization of, computer vision conferences. Hence, one might conclude that computer vision is parting or has parted company with AI. This article proposes that the divorce of computer vision and AI suggested here is actually an open marriage: Although computer vision is developing through its own research agenda, there are many shared areas of interest, and many of the key goals, assumptions, and characteristics of computer vision are also clearly found in AI.

The principal investigator (PI)-IN-A-BOX knowledge based system helps astronauts perform science experiments in space. This environment suggests the use of advanced techniques for data collection, analysis, and decision making to maximize the value of the research performed. PI-IN-A-BOX aids astronauts with quick-look data collection, reduction, and analysis as well as equipment diagnosis and troubleshooting, procedural reminders, and suggestions for high-value departures from the preplanned experiment protocol. The system is in use on the ground for mission training and was used in flight during the October 1993 space life sciences 2 (SLS-2) shuttle mission.

The Association for the Advancement of Artificial Intelligence sponsored a number of workshops in conjunction with the Eleventh National Conference on Artificial Intelligence held 11-15 July 1993 in Washington, D.C. This article contains reports of four of the workshops that were conducted: AI Models for System Engineering, Case-Based Reasoning, Reasoning about Function, and Validation and Verification of Knowledge Based Systems.

The Association for the Advancement of Artificial Intelligence sponsored a number of workshops in conjunction with the Eleventh National Conference on Artificial Intelligence held 11-15 July 1993 in Washington, D.C. This article contains reports of four of the workshops that were conducted: AI Models for System Engineering, Case-Based Reasoning, Reasoning about Function, and Validation and Verification of Knowledge Based Systems.

The principal investigator (PI)-IN-A-BOX knowledge based system helps astronauts perform science experiments in space. These experiments are typically costly to devise and build and often are difficult to perform. Further, the space laboratory environment is unique; ever changing; hectic; and, therefore, stressful. The environment requires quick, correct reactions to events over a wide range of experiments and disciplines, including ones distant from an astronaut's main science specialty. This environment suggests the use of advanced techniques for data collection, analysis, and decision making to maximize the value of the research performed. PI-IN-A-BOX aids astronauts with quick-look data collection, reduction, and analysis as well as equipment diagnosis and troubleshooting, procedural reminders, and suggestions for high-value departures from the preplanned experiment protocol. The astronauts have direct access to the system, which is hosted on a portable computer in the Space Lab module. The system is in use on the ground for mission training and was used in flight during the October 1993 space life sciences 2 (SLS-2) shuttle mission.

The theory revision problem is the problem of how best to go about revising a deficient domain theory using information contained in examples that expose inaccuracies. In this paper we present our approach to the theory revision problem for propositional domain theories. The approach described here, called PTR, uses probabilities associated with domain theory elements to numerically track the ``flow'' of proof through the theory. This allows us to measure the precise role of a clause or literal in allowing or preventing a (desired or undesired) derivation for a given example. This information is used to efficiently locate and repair flawed elements of the theory. PTR is proved to converge to a theory which correctly classifies all examples, and shown experimentally to be fast and accurate even for deep theories.

We propose a model of the development of geometric reasoning in children that explicitly involves learning. The model uses a neural network that is initialized with an understanding of geometry similar to that of second-grade children. Through the presentation of a series of examples, the model is shown to develop an understanding of geometry similar to that of fifth-grade children who were trained using similar materials.

Recently, both connectionist and symbolic methods have been developed for biasing learning with prior knowledge lFu, 1989; Towell et a/., 1990; Ourston and Mooney, 1990]. Most ofthese methods revise an imperfect knowledge base (usually obtained from a domain expert) to fit a set of empirical data. Some of these methods have been successfully applied to real-world tasks, such as recognizing promoter sequences in DNA [Towell et ai., 1990; Ourston and Mooney, 1990]. The results demonstrate that revising an expert-given knowledge base produces more accurate results than learning from training data alone.

We propose a model of the development of geometric reasoning in children that explicitly involves learning. The model uses a neural network that is initialized with an understanding of geometry similar to that of second-grade children. Through the presentation of a series of examples, the model is shown to develop an understanding of geometry similar to that of fifth-grade children who were trained using similar materials.

Recently, both connectionist and symbolic methods have been developed for biasing learning with prior knowledge lFu, 1989; Towell et a/., 1990; Ourston and Mooney, 1990]. Most ofthese methods revise an imperfect knowledge base (usually obtained from a domain expert) to fit a set of empirical data. Some of these methods have been successfully applied to real-world tasks, such as recognizing promoter sequences in DNA [Towell et ai., 1990; Ourston and Mooney, 1990]. The results demonstrate that revising an expert-given knowledge base produces more accurate results than learning from training data alone.