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.
14801431
Causality in Bayesian Belief Networks
We address the problem of causal interpretation of the graphical structure of Bayesian belief networks (BBNs). We review the concept of causality explicated in the domain of structural equations models and show that it is applicable to BBNs. In this view, which we call mechanism-based, causality is defined within models and causal asymmetries arise when mechanisms are placed in the context of a system. We lay the link between structural equations models and BBNs models and formulate the conditions under which the latter can be given causal interpretation.
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.
21852626
Search and Reasoning in Problem Solving
In the course of the extensive research that has been done on problem solving in AI , several rather distinct ways have emerged for representing and thinking about problem solving tasks. One way to view problem solving is as search. We postulate some kind of space in which treasures are hidden. We build symbol structures (nodes) that model this space, and 'move ' operators that alter these symbol structures, taking us from one node to another. In this metaphor, solving a problem consists in searching the model of the space (selectively), moving from one node to another along links that connect them until a treasure is encountered. A second way of viewing problem solving is as reasoning. We postulate a system of logic that allows us to deduce new statements from axioms and previously deduced statements. We represent a problem by a set of axioms in the formal language of our logic. In this metaphor, solving the problem consists in accumulating more and more information (more and more statements) by inference until the answer to the problem has been found. A third way of viewing problem solving is as constraint satisfaction. We postulate a set of objects and various subsets defined by the constraints they satisfy. In this metaphor, solving a problem consists in narrowing down the original set to a subset or unique object that satisfies all of the constraints. In no sense are the metaphors mutually exclusive; metaphors seldom are. The same problem-solving algorithm can be viewed, now as search, now as reasoning, now as constraint satisfaction. Consider, for example, a simple theorem-proving program that works forward from a set of axioms, applying its rules of inference to these to obtain new expressions that can be added to the
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.
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.
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 .
5520100
The Theory of Problem Solving
It is now about fifteen years since the first computer programs were written and tested that used the method of heuristic search to solve problems. Dozens of such programs, some designed for specific task domains, others claiming various degrees of generality, have now been described in the literature, and many experiments with their performance have been reported and analysed. It is an appropriate time to ask what has been learned from these experiments about the general theory of problem solving, and to try to summarize the present state of that theory.
17561186
Situated Action: A Symbolic Interpretation
The congeries of theoretical views collectively referred to as “situated action” (SA) claim that humans and their interactions with the world cannot be understood using symbol-system models and methodology, but only by observing them within real-world contexts or building nonsymbolic models of them. SA claims also that rapid, real-time interaction with a dynamically changing environment is not amenable to symbolic interpretation of the sort espoused by the cognitive science of recent decades. Planning and representation, central to symbolic theories, are claimed to be irrelevant in everyday human activity. 
 
We will contest these claims, as well as their proponents' characterizations of the symbol-system viewpoint. We will show that a number of existing symbolic systems perform well in temporally demanding tasks embedded in complex environments, whereas the systems usually regarded as exemplifying SA are thoroughly symbolic (and representational), and, to the extent that they are limited in these respects, have doubtful prospects for extension to complex tasks. As our title suggests, we propose that the goals set forth by the proponents of SA can be attained only within the framework of symbolic systems. The main body of empirical evidence supporting our view resides in the numerous symbol systems constructed in the past 35 years that have successfully simulated broad areas of human cognition.
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.
965932
Editors' Introduction
Not since the publication of Rachel Carson's Silent Spring (1962) has a book about the environment provoked such controversy as Bjorn Lomborg's The Skeptical Environmen-talist (2001). In the four decades since Silent Spring a popular environmental awareness and suspicion of ''chemicals'' and other aspects of modern industrial life has increasingly challenged and, in some cases, displaced scientific and technological optimism as an orthodox view throughout most of the industrialized world. A proliferation of nongovern-mental organizations and scientific bodies repeatedly invoke science to warn us of the potential for irreversible and, possibly, catastrophic environmental decline, unless people on the planet mend their ways. Powerful interests resist this message, increasingly by attacking the science that informs it. In this context, it may seem unsurprising that Lomborg's challenge to contemporary catastrophic views of inexorable global environmental decline has itself attracted criticism from environmentalists. Perhaps what is surprising are the amount of attention, the degree of opprobrium, and the ad hominem nature of the criticism that has been launched at Lomborg for a book that builds upon a tradition of similar arguments. Indeed the central theme of TSE, that some combination of business-as-usual and incremental change will be sufficient for children born today to ''get more food, a better education, a higher standard of living, more leisure time and far more possibi-lities—without the global environment being destroyed'', seems a relatively modest assertion of the cornucopian world view by comparison with some of Lomborg's predecessors, such as deconstructions of environmental concerns over the preceding decade including Ron Bailey's Ecoscam (1993) and Aaron Wildavsky's But is it True? (1995) provoked few serious rebuttals from scientists or environmental activists. The controversy over TSE serves to remind us of the anthropological observation that science is the trump card that we play in disputes about values. In our contemporary society, science has become the medium through which we understand nature. However, science has also become the medium through which we wage political battles not just on the environment, but also over many other contested issues. Hence, the debate over TSE provides a rich case study through which to examine issues associated with the relationships among science, policy, and politics, which observers on the right (e.g., Gough, 2003) and left (e.g., UCS, 2004) of the US political spectrum agree is a critical area for governance. The five papers that comprise this special issue were originally solicited for a symposium titled …