17595012
Artificial Intelligence Systems That Understand
From its beginnings, artificial intelligence has borrowed freely from the vocabulary of psychology. The use of the word "intelligence" to label our area of research is a case in point. Other terms referring originally to human mental processes that have consider able currency in AI are "thinking," "comprehending," and, with increasing frequency in the past five years, "understanding." Infact, these terms are probably used more freely in AI than in experimental psychology, where a deep suspicion of "mentalistic" terminology still lingers as a heritage of behaviorism.
6846387
Causality and Model Abstraction
Abstract Much of science and engineering is concerned with characterizing processes by equations describing the relations that hold among parameters of objects and govern their behavior over time. In formal descriptions of processes in terms of parameters and equations, the notion of causality is rarely made explicit. Formal treatments of the foundations of sciences have avoided discussions of causation and spoken only of functional relations among variables. Nevertheless, the notion of causality plays an important role in our understanding of phenomena. Even when we describe the behavior of a system formally in terms of acausal, mathematical relations, we often give an informal, intuitive explanation of why the system behaves the way it does in terms of cause-effect relations. In this paper, we will present an operational definition of causal ordering. The definition allows us to extract causal dependency relations among variables implicit in a model of a system, when a model is represented as a set of acausal, mathematical relations. Our approach is based on the theory of causal ordering first presented by Simon [22]. The paper shows how to use the theory and its extension in reasoning about physical systems. Further, the paper studies the relation of the theory to the problems of model aggregation.
16243913
Experiments with a Heuristic Compiler
This report describes some experiments in constructing a compiler that makes use of heuristic problem~solving techniques such as those incorporated in the General Problem Solver (GPS) [1]. The experiments were aimed at the dual objectives of throwing light on some of the problems of constructing more powerful programming languages and compilers, and of testing whether the task of writing a computer program can be regarded as a "problem" in the sense in which that term is used in GPS. The present paper is concerned primarily with the second objective--with analyzing some of the problem-solving processes that are involved in writing computer programs. At the present stage of their development, no claims will be made for the heuristic programming procedures described here as practical approaches to the construction of compilers. Their interest lies in what they teach us about the nature of the programming task.
5581562
Computer science as empirical inquiry: symbols and search
Computer science is the study of the phenomena surrounding computers. The founders of this society understood this very well when they called themselves the Association for Computing Machinery. The machine—not just the hardware, but the programmed, living machine—is the organism we study.
37804897
Directions for Qualitative Reasoning
Sacks & Doyle provide an excellent overview of the fundamental limitations of the SPQR representations for reasoning about the qualitative properties of dynamic systems. We take this opportunity to outline some new directions for qualitative reasoning. In this paper, we provide a rigorous mathematical characterization for the term “qualitative property” in the context of static and dynamic systems. Based on these characterizations, we show that interval representations are well suited for reasoning about the qualitative properties of static systems such as qualitative comparative statics and qualitative stability. Moreover, we also show that symbolic computations help in the derivation of useful global properties of dynamic systems which can be used to guide numerical sampling of differential equations. The integration of symbolic and numeric methods provides a powerful approach for automating the qualitative analysis of differential equations.
10043574
Scientific Model-Building as Search in Matrix Spaces
Many reported discovery systems build discrete models of hidden structure, properties, or processes in the diverse fields of biology, chemistry, and physics. We show that the search spaces underlying many well-known systems are remarkably similar when re-interpreted as search in matrix spaces. A small number of matrix types are used to represent the input data and output models. Most of the constraints can be represented as matrix constraints; most notably, conservation laws and their analogues can be represented as matrix equations. Typically, one or more matrix dimensions grow as these systems consider more complex models after simpler models fail, and we introduce a notation to express this. The novel framework of matrix-space search serves to unify previous systems and suggests how at least two of them can be integrated. Our analysis constitutes an advance toward a generalized account of model-building in science.
16971158
Laboratory Replication of Scientific Discovery Processes
Fourteen subjects were tape-recorded while they undertook to find a law to summarize numerical doto they were given. The source of the data was not identified, nor were the variables lobeled semantically. Unknown to the subjects. the data were measurements of the distances of the planets from the sun and the periods of their revolutions obout it-equivalent to the doto used by Johonnes Kepler to discover his third low of planetary motion. Four of the 14 subjects discovered the some low as Kepler did (the period varies OS the 3/2 power of the distance), and a fifth come very close to the answer. The subiects’ protocols provide a detatled picture of the problem-solving searches they engoged in, which were mainly, but not exclusively, in the space of possible functions for fitting the data. and provide explanations OS to why some succeeded and the others foiled. The scorch heuristics used by the subjects ore similar to those embodied in the BACON program, a computer simulation of certain scientific discovery processes. The experiment demonstrates the feasibility of examining some of the processes of scientific discovery by recreating, in the laboratory, discovery situations of substontiol historical relevance. It demonstrates also. that under conditions rather similar to those of the originol discoverer, a low can be rediscovered by persons of ordinory intelligence (i.e., the intelligence needed for academic success in a good university). The data for the successful subjects reveal no “creative” processes in this kind of o discovery situation different from those that are regulorly observed in all kinds of problem-solving settings.
8638136
The marketplace of high-performance computing
In this paper we analyze the major trends and changes in the High-Performance Computing (HPC) market place since the beginning of the journal ‘Parallel Computing’. The initial success of vector computers in the 1970s was driven by raw performance. The introduction of this type of computer systems started the area of ‘Supercomputing’. In the 1980s the availability of standard development environments and of application software packages became more important. Next to performance these factors determined the success of MP vector systems, especially at industrial customers. MPPs became successful in the early 1990s due to their better price/performance ratios, which was made possible by the attack of the ‘killer-micros’. In the lower and medium market segments the MPPs were replaced by microprocessor based symmetrical multiprocessor (SMP) systems in the middle of the 1990s. There success formed the basis for the use of new cluster concepts for very high-end systems. In the last few years only the companies which have entered the emerging markets for massive parallel database servers and financial applications attract enough business volume to be able to support the hardware development for the numerical high-end computing market as well. Success in the traditional floating point intensive engineering applications seems to be no longer suAcient for survival in the market. ” 1999 Elsevier Science B.V. All rights reserved.
30978174
Whether software engineering needs to be artificially intelligent
The author discusses the roles that humans now play versus the roles that could be taken over by artificial intelligence in developing computer systems. Also discussed is how the intelligent part of the automatic system can communicate effectively with humans. Topics covered include an artificial intelligence overview; weak methods; the heuristic search; the problem space; the knowledge base; expert systems; and conclusions drawn from a description of the general artificial intelligence paradigm.
36271721
Industrial Application Areas of High-Performance Computing
In 1993, a list of the top 500 supercomputer sites worldwide was made available for the first time. Since then, the Top500 list has been published twice a year. The list allows a detailed and well-founded analysis of the state of high-performance computing (HPC). This article summarizes the recent trends in application areas of HPC systems, focusing on the increase in industrial installations and applications.
145761361
CaMeRa: A computational model of multiple representations
This research aims to clarify, by constructing and testing a computer simulation, the use of multiple representations in problem solving, focusing on their role in visual reasoning. The model is motivated by extensive experimental evidence in the literature for the features it incorporates, but this article focuses on the system's structure. We illustrate the model's behavior by simulating the cognitive and perceptual processes of an economics expert as he teaches some well-learned economics principles while drawing a graph on a blackboard. Data in the experimental literature and concurrent verbal protocols were used to guide construction of a linked production system and parallel network, CaMeRa (Computation with Multiple Representations), that employs a “Mind's Eye” representation for pictorial information, consisting of a bitmap and associated node-link structures. Propositional list structures are used to represent verbal information and reasoning. Small individual pieces from the different representations are linked on a sequential and temporary basis to form a reasoning and inferencing chain, using visually encoded information recalled to the Mind's Eye from long-term memory and from cues recognized on an external display. CaMeRa, like the expert, uses the diagrammatic and verbal representations to complement one another, thus exploiting the unique advantages of each.