29911339
Artificial Intelligence: An Empirical Science
My initial tasks in this paper are, first, to delimit the boundaries of artificial intelligence, then, to justify calling it a science: is AI science, or is it engineering, or some combination of these? After arguing that it is (at least) a science, I will consider how it is best pursued: in particular, the respective roles for experiment and theory in developing AI. I will rely more on history than on speculation, for our actual experience in advancing the field has much to tell us about how we can continue and accelerate that advance. Many of my examples will be drawn from work with which I have been associated, for I can speak with greater confidence about what motivated that work and its methods (and about its defects) than I can about the work of others. My goal, however, is not to give you a trip through history, but to make definite proposals for our future priorities, using history, where relevant, as evidence for my views.
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.
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.
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.
8839735
Internal representation and rule development in object-oriented design
This article proposes a cognitive framework describing the software development process in object-oriented design (OOD) as building internal representations and developing rules. Rule development (method construction) is performed in two problem spaces: a rule space and an instance space. Rules are generated, refined, and evaluated in the rule space by using three main cognitive operations: Infer, Derive, and Evoke. Cognitive activities in the instance space are called mental simulations and are used in conjunction with the Infer operation in the rule space. In an empirical study with college students, we induced different representations to the same problem by using problem isomorphs. Initially, subjects built a representation based on the problem description. As rule development proceeded, the initial internal representation and designed objects were refined, or changed if necessary, to correspond to knowledge gained during rule development. Differences in rule development processes among groups created final designs that are radically different in terms of their level of abstraction and potential reusability. The article concludes by discussing the implications of these results for object-oriented design.
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.
15281282
Fast multilevel implementation of recursive spectral bisection for partitioning unstructured problems
SUMMARY If problems involving unstructured meshes are to be solved efficiently on distributed-memory parallel computers, the meshes must be partitioned and distributed across processors in a way that balances the computational load and minimizes communication. The recursive spectral bisection method (RSB) has been shown to be very effective for such partitioning problems compared to alternative methods, but RSB in its simplest form is expensive. Here a multilevel version of RSB is introduced that attains about an order-of-magnitude improvement in run time on typical examples. 1. INTRODUCTION Unstructured meshes are used in several large-scale scientific and engineering problems, including finite-volume methods for computational fluid dynamics and finite-element methods for structural analysis. If unstructured problems such as these are to be solved on distributed-memory parallel computers, their data structures must be partitioned and distributed across processors; if they are to be solved efficiently, the partitioning must niaximize load balance and minimize interprocessor communication. Recently, the recursive spectral bisection method (RSB)[l] has been shown to be very effective for such partitioning problems compared to alternative methods. Unfortunately, RSB in its simplest form is expensive. We shall describe a multilevel version of RSB that attains about im order-of-magnitude improvement in run time on typical examples.
9115069
Causality in Device Behavior
Abstract This paper shows how formal characterizations of causality and of the method of comparative statics, long used in econometrics, thermodynamics and other domains, can be applied to clarify and make rigorous the qualitative causal calculus recently proposed by de Kleer and Brown [2]. The formalization shows exactly what assumptions are required to carry out causal analysis of a system of interdependent variables in equilibrium and to propagate disturbances through such a system. The intuitive concepts of causality captured by de Kleer and Brown provide a rough approximation to the standard analytic techniques that are used in the treatment of simultaneous algebraic and differential equations.
17442679
Inner Product Spaces for Bayesian Networks
Bayesian networks have become one of the major models used for statistical inference. We study the question whether the decisions computed by a Bayesian network can be represented within a low-dimensional inner product space. We focus on two-label classification tasks over the Boolean domain. As main results we establish upper and lower bounds on the dimension of the inner product space for Bayesian networks with an explicitly given (full or reduced) parameter collection. In particular, these bounds are tight up to a factor of 2. For some nontrivial cases of Bayesian networks we even determine the exact values of this dimension. We further consider logistic autoregressive Bayesian networks and show that every sufficiently expressive inner product space must have dimension at least Ω(n2), where n is the number of network nodes. We also derive the bound 2Ω(n) for an artificial variant of this network, thereby demonstrating the limits of our approach and raising an interesting open question. As a major technical contribution, this work reveals combinatorial and algebraic structures within Bayesian networks such that known methods for the derivation of lower bounds on the dimension of inner product spaces can be brought into play.
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.
42465992
History of Artificial Intelligence
Though our discussion is e n t i t l e d "The History o f A r t i f i c i a l I n te l l i gence" , i n fac t we are focusing here on one b r i e f but h igh ly s i gn i f i can t moment in that h i s t o r y , the moment when a r t metamorphosed i t s e l f i n to science, from wish and dream to something l i k e r e a l i t y . As you w i l l learn from each of the discussants, th is metamorphosis took place at several locat ions during the ear ly to mid-1950s, and i t s ca ta lys t was the recogni t ion that the computer was the most promising medium yet in which to rea l i ze what had been a human dream since ea r l i es t t imes, the creat ion of man-made, rather than begotten i n te l l i gence .