91 research outputs found
Three Highly Parallel Computer Architectures and Their Suitability for Three Representative Artificial Intelligence Problems
Virtually all current Artificial Intelligence (AI) applications are designed to run on sequential (von Neumann) computer architectures. As a result, current systems do not scale up. As knowledge is added to these systems, a point is reached where their performance quickly degrades. The performance of a von Neumann machine is limited by the bandwidth between memory and processor (the von Neumann bottleneck). The bottleneck is avoided by distributing the processing power across the memory of the computer. In this scheme the memory becomes the processor (a smart memory ).
This paper highlights the relationship between three representative AI application domains, namely knowledge representation, rule-based expert systems, and vision, and their parallel hardware realizations. Three machines, covering a wide range of fundamental properties of parallel processors, namely module granularity, concurrency control, and communication geometry, are reviewed: the Connection Machine (a fine-grained SIMD hypercube), DADO (a medium-grained MIMD/SIMD/MSIMD tree-machine), and the Butterfly (a coarse-grained MIMD Butterflyswitch machine)
A knowledge-based approach for the extraction of machining features from solid models
Computer understanding of machining features such as holes and pockets is
essential for bridging the communication gap between Computer Aided Design and
Computer Aided Manufacture. This thesis describes a prototype machining feature
extraction system that is implemented by integrating the VAX-OPS5 rule-based
artificial intelligence environment with the PADL-2 solid modeller. Specification of
original stock and finished part geometry within the solid modeller is followed by
determination of the nominal surface boundary of the corresponding cavity volume
model by means of Boolean subtraction and boundary evaluation. The boundary model
of the cavity volume is managed by using winged-edge and frame-based data
structures. Machining features are extracted using two methods : (1) automatic feature
recognition, and (2) machine learning of features for subsequent recognition. [Continues.
The 1988 Goddard Conference on Space Applications of Artificial Intelligence
This publication comprises the papers presented at the 1988 Goddard Conference on Space Applications of Artificial Intelligence held at the NASA/Goddard Space Flight Center, Greenbelt, Maryland on May 24, 1988. The purpose of this annual conference is to provide a forum in which current research and development directed at space applications of artificial intelligence can be presented and discussed. The papers in these proceedings fall into the following areas: mission operations support, planning and scheduling; fault isolation/diagnosis; image processing and machine vision; data management; modeling and simulation; and development tools/methodologies
Third Conference on Artificial Intelligence for Space Applications, part 1
The application of artificial intelligence to spacecraft and aerospace systems is discussed. Expert systems, robotics, space station automation, fault diagnostics, parallel processing, knowledge representation, scheduling, man-machine interfaces and neural nets are among the topics discussed
Knowledge Based Systems: A Critical Survey of Major Concepts, Issues, and Techniques
This Working Paper Series entry presents a detailed survey of knowledge based systems. After being in a relatively dormant state for many years, only recently is Artificial Intelligence (AI) - that branch of computer science that attempts to have machines emulate intelligent behavior - accomplishing practical results. Most of these results can be attributed to the design and use of Knowledge-Based Systems, KBSs (or ecpert systems) - problem solving computer programs that can reach a level of performance comparable to that of a human expert in some specialized problem domain. These systems can act as a consultant for various requirements like medical diagnosis, military threat analysis, project risk assessment, etc. These systems possess knowledge to enable them to make intelligent desisions. They are, however, not meant to replace the human specialists in any particular domain. A critical survey of recent work in interactive KBSs is reported. A case study (MYCIN) of a KBS, a list of existing KBSs, and an introduction to the Japanese Fifth Generation Computer Project are provided as appendices. Finally, an extensive set of KBS-related references is provided at the end of the report
The PSEIKI Report—Version 2. Evidence Accumulation and Flow of Control in a Hierarchical Spatial Reasoning System
A fundamental goal of computer vision is the development of systems capable of carrying out scene interpretation while taking into account all the available knowledge. In this report, we have focused on how the interpretation task may be aided by expected-scene information which, in most cases, would not be in registration with the perceived scene. In this report, we describe PSEIKI, a framework for expectation-driven interpretation of image data. PSEIKI builds abstraction hierarchies in image data using, for cues, supplied abstraction hierarchies in a scene expectation map. Hypothesized abstractions in the image data are geometrically compared with the known abstractions in the expected scene; the metrics used for these comparisons translate into belief values. The Dempster-Shafer formalism is used to accumulate beliefs for the synthesized abstractions in the image data. For accumulating belief values, a computationally efficient variation of Dempster’s rule of combination is developed to enable the system to deal with the overwhelming amount of information present in most images. This variation of Dempster’s rule allows the reasoning process to be embedded into the abstraction hierarchy by allowing for the propagation of belief values between elements at different levels of abstraction. The system has been implemented as a 2- panel, 5-level blackboard in OPS 83. This report also discusses the control aspects of the blackboard, achieved via a distributed monitor using the OPS83 demons and a scheduler. Various knowledge sources for forming groupings in the image data and for labeling such groupings with abstractions from the scene expectation map are also discussed
Second CLIPS Conference Proceedings, volume 2
Papers presented at the 2nd C Language Integrated Production System (CLIPS) Conference held at the Lyndon B. Johnson Space Center (JSC) on 23-25 September 1991 are documented in these proceedings. CLIPS is an expert system tool developed by the Software Technology Branch at NASA JSC and is used at over 4000 sites by government, industry, and business. During the three days of the conference, over 40 papers were presented by experts from NASA, Department of Defense, other government agencies, universities, and industry
HybridAugment++: Unified Frequency Spectra Perturbations for Model Robustness
Convolutional Neural Networks (CNN) are known to exhibit poor generalization
performance under distribution shifts. Their generalization have been studied
extensively, and one line of work approaches the problem from a
frequency-centric perspective. These studies highlight the fact that humans and
CNNs might focus on different frequency components of an image. First, inspired
by these observations, we propose a simple yet effective data augmentation
method HybridAugment that reduces the reliance of CNNs on high-frequency
components, and thus improves their robustness while keeping their clean
accuracy high. Second, we propose HybridAugment++, which is a hierarchical
augmentation method that attempts to unify various frequency-spectrum
augmentations. HybridAugment++ builds on HybridAugment, and also reduces the
reliance of CNNs on the amplitude component of images, and promotes phase
information instead. This unification results in competitive to or better than
state-of-the-art results on clean accuracy (CIFAR-10/100 and ImageNet),
corruption benchmarks (ImageNet-C, CIFAR-10-C and CIFAR-100-C), adversarial
robustness on CIFAR-10 and out-of-distribution detection on various datasets.
HybridAugment and HybridAugment++ are implemented in a few lines of code, does
not require extra data, ensemble models or additional networks.Comment: Accepted to ICCV 202
The 1995 Goddard Conference on Space Applications of Artificial Intelligence and Emerging Information Technologies
This publication comprises the papers presented at the 1995 Goddard Conference on Space Applications of Artificial Intelligence and Emerging Information Technologies held at the NASA/Goddard Space Flight Center, Greenbelt, Maryland, on May 9-11, 1995. The purpose of this annual conference is to provide a forum in which current research and development directed at space applications of artificial intelligence can be presented and discussed
The 1991 Goddard Conference on Space Applications of Artificial Intelligence
The purpose of this annual conference is to provide a forum in which current research and development directed at space applications of artificial intelligence can be presented and discussed. The papers in this proceeding fall into the following areas: Planning and scheduling, fault monitoring/diagnosis/recovery, machine vision, robotics, system development, information management, knowledge acquisition and representation, distributed systems, tools, neural networks, and miscellaneous applications
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