I have presented a qualitative approach to the problem of recovering object structure from motion information and discussed some of its computational, psychophysical and implementational aspects. The computation of qualitative shape, as represented by the sign of the Gaussian curvature, can be performed by a field of simple operators, in parallel over the entire image. The performance of a qualitative shape detection module, implemented by an artificial neural network, appears to be similar to the performance of human subjects in an identical task.

I have presented a qualitative approach to the problem of recovering object structure from motion information and discussed some of its computational, psychophysical and implementational aspects. The computation of qualitative shape, as represented bythe sign of the Gaussian curvature, can be performed by a field of simple operators, in parallel over the entire image. The performance of a qualitative shape detection module, implemented by an artificial neural network, appears to be similar to the performance of human subjects in an identical task.

The AAAI-90 Workshop on Qualitative Vision was held on Sunday, 29 July 1990. Over 50 researchers from North America, Europe, and Japan attended the workshop. This article contains a report of the workshop presentations and discussions.

The AAAI-90 Workshop on Qualitative Vision was held on Sunday, 29 July 1990. Over 50 researchers from North America, Europe, and Japan attended the workshop. This article contains a report of the workshop presentations and discussions.

Artificial Intelligence 51, pp. 95-143

Artificial Intelligence 51 (1991) 417-471, Spatial reasoning is ubiquitous in human problem solving. Significantly, many aspects of it appear to be qualitative. This paper describes a general framework for qualitative spatial reasoning and demonstrates how it can be used to understand complex mechanical systems, such as clocks. The framework is organized around three ideas.

Hypothesis Formation and Qualitative Reasoning in Molecular Biology

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Jim Saveland For a fire in that fuel complex to Research Forester The Phoenix project ("Trial by Fire: grow to the size indicated in the time Associate Editor, AI Application in Understanding the Design Requirements indicated would require a midflame Natural Resource Management for Agents in Complex Environments." Agriculture 3) presents very interesting work in The authors go on to state, "Firefighting Forest Service forest fire simulation. I am especially objects are also accurately Southern Forest Fire Laboratory glad to see recognition that the "realtime, simulated; for example, bulldozers Route 1, Box 182A spatially distributed, multiagent, move at a maximum speed of... 0.5 Dry Branch, GA 31020 dynamic, and unpredictable fire kph when cutting a fireline." In reality, environment" provides an excellent sustained fireline production for Editor: opportunity to explore a variety of AI bulldozers is variable (0.1 - 2.0 kph) issues, such as how complex environments depending on steepness of the slope, Mr. Saveland's letter focuses our constrain the design of intelligent vegetation, and size of the bulldozer. I hope more AI researchers Furthermore, although bulldozers are between accuracy and realism.

The goal of intelligent computer-aided engineering (ICAE) is to construct computer programs that capture a significant fraction of an engineer's knowledge. Today, ICAE systems are a goal, not a reality. This article attempts to refine that goal and suggest how to get there. We begin by examining several scenarios of what ICAE systems could be like. Next we describe why ICAE won't evolve directly from current applications of expert system technology to engineering problems. I focus on qualitative physics as a critical area where progress is needed, both in terms of representations and styles of reasoning.