We propose a new definition of actual causes, using structural equations to model counterfactuals. We show that the definition yields a plausible and elegant account of causation that handles well examples which have caused problems for other definitions

Knowledge about the rationale for a design---how and why a device is designed as it is---can be valuable, but is difficult to capture in reusable form. This paper presents a view of design knowledge capture and the use of design knowledge for design rationale. We define design rationales as explanations in response to questions about the design. These explanations are generated from knowledge of artifacts and design activities. We characterize design activity in terms of observable changes to design descriptions, and present a theory of design knowledge in the form of an ontology of concepts about design descriptions and operations on them. The theory unifies artifact description and decision-making views of design. Based on the theory, we characterize different methods of acquiring design knowledge and design rationale in the context of integrated design support environments. We then analyze in depth two design knowledge capture techniques: a semiformal representation tool and a model...

As probabilistic systems gain popularity and are coming into wider use, the need for a mechanism that explains the system's findings and recommendations becomes more critical. The system will also need a mechanism for ordering competing explanations. We examine two representative approaches to explanation in the literature--- one due to G ardenfors and one due to Pearl---and show that both suffer from significant problems. We propose an approach to defining a notion of "better explanation" that combines some of the features of both together with more recent work by Pearl and others on causality. 1 INTRODUCTION Probabilistic inference is often hard for humans to understand. Even a simple inference in a small domain may seem counterintuitive and surprising; the situation only gets worse for large and complex domains. Thus, a system doing probabilistic inference must be able to explain its findings and recommendations to evoke confidence on the part of the user. Indeed, in experiments wi...

The unclear distinction between data, information, and knowledge has impaired their combination and utilization for the development of integrated systems. There is need for a unified definitional model of data, information, and knowledge based on their roles in computational and cognitive information processing. An attempt to clarify these basic notions is made, and a conceptual framework for integration is suggested by focusing on their different roles and frames of reference within a decision-making process. On this basis, ways of integrating the functionalities of databases, information systems and knowledge-based systems are discussed by taking a knowledge level perspective to the analysis and modeling of systems behaviour. Motivated by recent work in the area of case-based reasoning related to decision support systems, it is further shown that a specific problem solving episode, or case, may be viewed as data, information, or knowledge, depending on its role in decision making and...

page 14 Declaration - page 15 Notes of copyright and the ownership of intellectual property rights - page 15 The Author - page 16 Acknowledgements - page 16 1 - Introduction - page 17 1.1 - Background - page 17 1.2 - The Style of Approach - page 18 1.3 - Motivation - page 19 1.4 - Style of Presentation - page 20 1.5 - Outline of the Thesis - page 21 2 - Models and Modelling - page 23 2.1 - Some Types of Models - page 25 2.2 - Combinations of Models - page 28 2.3 - Parts of the Modelling Apparatus - page 33 2.4 - Models in Machine Learning - page 38 2.5 - The Philosophical Background to the Rest of this Thesis - page 41 Syntactic Measures of Complexity - page 3 - 3 - Problems and Properties - page 44 3.1 - Examples of Common Usage - page 44 3.1.1 - A case of nails - page 44 3.1.2 - Writing a thesis - page 44 3.1.3 - Mathematics - page 44 3.1.4 - A gas - page 44 3.1.5 - An ant hill - page 45 3.1.6 - A car engine - page 45 3.1.7 - A cell as part of an organism -...

A source of intellectual overhead periodically encountered by scientists is the call to be "hard," to insure good science by imposing severe methodological strictures. Newell and Card (1985) have undertaken to impose such strictures on the psychology of humancomputer interaction. Although their discussion contributes to theoretical debate in humancomputer interaction by setting a reference point, their specific argument fails. Their program is unmotivated, is severely limited, and suffers from these limitations in principle. A top priority for the psychology of human-computer interaction should be the articulation of an alternative explanatory program, one that takes as its starting point the need to understand the real problems involved in providing better computer tools for people to use. 1. Newell and Card on Being Hard Newell and Card (1985) have presented a program for psychological research in humancomputer interaction couched as an analysis of how psychology can avoid being ...

This paper describes abduction as `inference to the best explanation' and argues that "smart" inductive generalizations are a special case of abductions. Along the way it argues that some good explanations are not proofs and some proofs are not explanations, concluding that explanations are not deductive proofs in any particularly interesting sense. An attractive alternative is that explanations are assignments of causal responsibility. Smart inductive generalizations can then be seen to be abductions wherein the frequency in a statistical sample is best explained by a frequency in a parent population along with the method of drawing the sample. A distinctive pattern of inference To postpone entanglements with the abundant confusions surrounding various uses of the term "abduction," for which Peirce himself seems largely to be responsible, and to proceed as directly as possible to engage the basic logical and computational issues, let us begin by examining a pattern of inference I ...

A biochemical pathway is a sequence of chemical reactions in a biological organism. Such pathways specify mechanisms that explain how cells carry out their major functions by means of molecules and reactions that produce regular changes. Many diseases can be explained by defects in pathways, and new treatments often involve finding drugs that correct those defects. This paper presents explanation schemas and treatment strategies that characterize how thinking about pathways contributes to biomedical discovery. It discusses the significance of pathways for understanding the nature of diseases, explanations, and theories.

this paper has strong connections with belief change, as originally defined by Gardenfors [10] through the operations of expansion and revision.

This paper develops a means-ends analysis of an inductive problem that arises in particle physics: how to infer from observed reactions conservation principles that govern all reactions among elementary particles. I show that there is a reliable inference procedure that is guaranteed to arrive at an empirically adequate set of conservation principles as more and more evidence is obtained. An interesting feature of reliable procedures for finding conservation principles is that in certain precisely defined circumstances they must introduce hidden particles. Among the reliable inductive methods there is a unique procedure that minimizes convergence time as well as the number of times that the method revises its conservation principles. Thus the aims of reliable, fast and steady convergence to an empirically adequate theory single out a unique optimal inference for a given set of observed reactions–including prescriptions for when exactly to introduce hidden particles.