634 research outputs found
ON THE RATIONAL SCOPE OF PROBABILISTIC RULE-BASED INFERENCE SYSTEMS
Belief updating schemes in artificial intelligence may be viewed as three
dimensional languages, consisting of a syntax (e.g. probabilities or certainty
factors), a calculus (e.g. Bayesian or CF combination rules), and a semantics
(i.e. cognitive interpretations of competing formalisms). This paper studies
the rational scope of those languages on the syntax and calculus grounds. In
particular, the paper presents an endomorphism theorem which highlights
the limitations imposed by the conditional independence assumptions
implicit in the CF calculus. Implications of the theorem to the relationship
between the CF and the Bayesian languages and the Dempster-Shafer theory
of evidence are presented. The paper concludes with a discussion of some
implications on rule-based knowledge engineering in uncertain domains.Information Systems Working Papers Serie
On Cognitive Preferences and the Plausibility of Rule-based Models
It is conventional wisdom in machine learning and data mining that logical
models such as rule sets are more interpretable than other models, and that
among such rule-based models, simpler models are more interpretable than more
complex ones. In this position paper, we question this latter assumption by
focusing on one particular aspect of interpretability, namely the plausibility
of models. Roughly speaking, we equate the plausibility of a model with the
likeliness that a user accepts it as an explanation for a prediction. In
particular, we argue that, all other things being equal, longer explanations
may be more convincing than shorter ones, and that the predominant bias for
shorter models, which is typically necessary for learning powerful
discriminative models, may not be suitable when it comes to user acceptance of
the learned models. To that end, we first recapitulate evidence for and against
this postulate, and then report the results of an evaluation in a
crowd-sourcing study based on about 3.000 judgments. The results do not reveal
a strong preference for simple rules, whereas we can observe a weak preference
for longer rules in some domains. We then relate these results to well-known
cognitive biases such as the conjunction fallacy, the representative heuristic,
or the recogition heuristic, and investigate their relation to rule length and
plausibility.Comment: V4: Another rewrite of section on interpretability to clarify focus
on plausibility and relation to interpretability, comprehensibility, and
justifiabilit
Knowledge-Based Systems. Overview and Selected Examples
The Advanced Computer Applications (ACA) project builds on IIASA's traditional strength in the methodological foundations of operations research and applied systems analysis, and its rich experience in numerous application areas including the environment, technology and risk. The ACA group draws on this infrastructure and combines it with elements of AI and advanced information and computer technology to create expert systems that have practical applications.
By emphasizing a directly understandable problem representation, based on symbolic simulation and dynamic color graphics, and the user interface as a key element of interactive decision support systems, models of complex processes are made understandable and available to non-technical users.
Several completely externally-funded research and development projects in the field of model-based decision support and applied Artificial Intelligence (AI) are currently under way, e.g., "Expert Systems for Integrated Development: A Case Study of Shanxi Province, The People's Republic of China."
This paper gives an overview of some of the expert systems that have been considered, compared or assessed during the course of our research, and a brief introduction to some of our related in-house research topics
Mathematics and Statistics in the Social Sciences
Over the years, mathematics and statistics have become increasingly important in the
social sciences1
. A look at history quickly confirms this claim. At the beginning of the
20th century most theories in the social sciences were formulated in qualitative terms
while quantitative methods did not play a substantial role in their formulation and
establishment. Moreover, many practitioners considered mathematical methods to be
inappropriate and simply unsuited to foster our understanding of the social domain.
Notably, the famous Methodenstreit also concerned the role of mathematics in the
social sciences. Here, mathematics was considered to be the method of the natural
sciences from which the social sciences had to be separated during the period of
maturation of these disciplines.
All this changed by the end of the century. By then, mathematical, and especially
statistical, methods were standardly used, and their value in the social sciences
became relatively uncontested. The use of mathematical and statistical methods is
now ubiquitous: Almost all social sciences rely on statistical methods to analyze data
and form hypotheses, and almost all of them use (to a greater or lesser extent) a range
of mathematical methods to help us understand the social world.
Additional indication for the increasing importance of mathematical and statistical
methods in the social sciences is the formation of new subdisciplines, and the
establishment of specialized journals and societies. Indeed, subdisciplines such as
Mathematical Psychology and Mathematical Sociology emerged, and corresponding
journals such as The Journal of Mathematical Psychology (since 1964), The Journal
of Mathematical Sociology (since 1976), Mathematical Social Sciences (since 1980)
as well as the online journals Journal of Artificial Societies and Social Simulation
(since 1998) and Mathematical Anthropology and Cultural Theory (since 2000) were
established. What is more, societies such as the Society for Mathematical Psychology
(since 1976) and the Mathematical Sociology Section of the American Sociological
Association (since 1996) were founded. Similar developments can be observed in
other countries.
The mathematization of economics set in somewhat earlier (Vazquez 1995;
Weintraub 2002). However, the use of mathematical methods in economics started
booming only in the second half of the last century (Debreu 1991). Contemporary
economics is dominated by the mathematical approach, although a certain style of doing economics became more and more under attack in the last decade or so. Recent
developments in behavioral economics and experimental economics can also be
understood as a reaction against the dominance (and limitations) of an overly
mathematical approach to economics. There are similar debates in other social
sciences. It is, however, important to stress that problems of one method (such as
axiomatization or the use of set theory) can hardly be taken as a sign of bankruptcy of
mathematical methods in the social sciences tout court.
This chapter surveys mathematical and statistical methods used in the social sciences
and discusses some of the philosophical questions they raise. It is divided into two
parts. Sections 1 and 2 are devoted to mathematical methods, and Sections 3 to 7 to
statistical methods. As several other chapters in this handbook provide detailed
accounts of various mathematical methods, our remarks about the latter will be rather
short and general. Statistical methods, on the other hand, will be discussed in-depth
ARTIFICIAL INTELLIGENCE DIALECTS OF THE BAYESIAN BELIEF REVISION LANGUAGE
Rule-based expert systems must deal with uncertain data,
subjective expert opinions, and inaccurate decision rules. Computer scientists
and psychologists have proposed and implemented a number of belief languages widely used in applied systems, and their normative validity is clearly an important question, both on practical as well on theoretical grounds. Several well-know belief languages are reviewed, and both previous work and new insights into their Bayesian interpretations are presented. In
particular, the authors focus on three alternative belief-update models the
certainty factors calculus, Dempster-Shafer simple support functions, and
the descriptive contrast/inertia model. Important "dialectsâ of these
languages are shown to be isomorphic to each other and to a special case of
Bayesian inference. Parts of this analysis were carried out by other authors; these results were extended and consolidated using an analytic technique designed to study the kinship of belief languages in general.Information Systems Working Papers Serie
Recommended from our members
Building expert systems: cognitive emulation.
Chapter 1 briefly introduces the concept of cognitive emulation, and outlines its current status. Chapter 2 reviews psychological research on human expert thinking. First, the study of expert thinking is placed in the context of modern cognitive psychology. Next, the principal methods and techniques employed by psychologists examining expert cognition are examined. The remainder of the chapter is given over to a review of the published literature on the nature and development of human expertise. Chapter 3 reviews the main arguments for and against cognitive emulation in expert system design. The tentative conclusion reached is that a significant degree of emulation is inevitable, but that a pure, unselective strategy of emulation is neither realistic nor desirable. Chapter 4 examines the prospects for cognitive emulation from a more pragmatic angle. Several factors are identified that represent constraints on the usefulness of a cognitive approach. However, a second set of factors is identified which should facilitate an emulation strategy - especially in the longer term. Some guidance is given on when to seriously consider adopting an emulation strategy. Chapter 5 presents a critical survey of expert system research that has already addressed the emulation issue. Six basic approaches to cognitive emulation are distinguished and evaluated. This helps draw out in more detail the implications of an emulation strategy for knowledge acquisition, knowledge representation and system architecture. The chapter concludes by discussing the issues that arise when different approaches to emulation are combined. Some guidance is offered on how this might be achieved. Chapter 6 summarizes the main themes and issues to have emerged, the design advice contained in the thesis, and the original contributions made by the thesis
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