Coherent probability from incoherent judgment.

Knowledge Engineering for Industrial Expert Systems

In the evaluation of experiments often the problem arises of how to compare the predictive success of competing probabilistic theories. The quadratic scoring rule can be used for this purpose. Originally, this rule was proposed as an incentive compatible elicitation method for probabilistic expert judgments. It is shown that up to a positive linear transformation, the quadratic scoring rule is characterized by four desirable properties.

Based on the interpretation on the evolution of theoretical basis of the construction of legal expert system as the clue, this article mainly analyzes and points out the dilemma faced by symbolism and connectionism as the algorithm on construction of legal expert system. Through defeasible reasoning, a contextual-based legal expert system could be realized. The legal expert system should be constructed with the judge as the center character, in which the personality of the judge could not necessarily be eliminated. As a result, the article provides a model of a legal expert system based on dual processing, including factual argumentation and legal argumentation.

Building Expert Systems: A Structured Bibliography

Event-driven Process Chain (EPC) is a known business process modeling language which is primarily renowned for efficient modeling of business requirements. It is a semi-formal modeling language which is commonly verified though various formal mechanism for simple atomic events. However, the complexity of business requirements cannot be modeled through EPC atomic events alone especially in case of real time systems. Therefore, the complex events patterns like Event Time, Event location, Event Cardinality, Event Sequence, Event Exclusion, Event Trend and Data dependency are introduced in EPC from the novel area of complex event processing. However, to the best of our knowledge, there is no mechanism available in EPC to model and verify such complex events. Therefore, in this paper, we investigate the verification possibilities of both (atomic and complex) events in the context of Timed-Automata (TA) and Time Petri Nets (TPN). Particularly, we try to map the semantics of simple and complex events with the corresponding concepts of TA and TPN. It is analyzed that the simple EPC events can be formalized through TA and TPN but limited support is available for complex events. The restaurant process case study has been used for further investigation of complex events. It has been concluded that TA formalism is more suitable than TPN for the verification of complex events in EPC.

The following commentaries on the chapters in this volume start with a few humble premises. The realization is inescapable that the enterprise of psychology is vast, has a long history, and is deeply divided into often non-communicating subfields. The enterprise of artificial intelligence (AI) is much younger than psychology and yet is also divided into areas that barely communicate with each other. The divisions in AI often parallel those in psychology and often seem to be motivated by similar theoretical and methodological concerns. In addition, the enterprise of expert system construction, and in particular knowledge acquisition (KA), is but one tiny corner of AI, yet the activities focused on expertise are expanding beyond the narrow confines of computer science. It is as if a new field concerning itself with expertise, knowledge, and expert system construction—a field as yet unnamed—is trying to be born. It is an event exciting to witness, but it leaves the poor commentator with no fixed or generally agreed-upon platform—no firm Newtonian coordinate system from which to survey and summarize multidisciplinary research.

Proper scoring rules serve to measure subjective degrees of belief. Traditional proper scoring rules are based on the assumption of expected value maximization. There are, however, many deviations from expected value, primarily due to risk aversion. Correcting techniques have been proposed in the literature for deviations due to nonlinear utility. These techniques still assumed expected utility maximization. More recently, corrections for deviations from expected utility have been proposed. The latter concerned, however, only the quadratic scoring rule, and could handle only half of the domain of subjective beliefs. Further, beliefs close to 0.5 could not be discriminated. This paper generalizes the correcting techniques to all bounded binary proper scoring rules, covers the whole domain of beliefs and, in particular, can discriminate between all degrees of belief. Thus, we fully extend the properness requirement (in the sense of identifying all degrees of subjective beliefs) to virtually all models that deviate from expected value. Copyright © 2011 John Wiley & Sons, Ltd.

Focuses on Korean garment manufacturing and the feasibility and benefits of introducing an expert system. Reviews previous literature on knowledge‐based system and describes in detail the stages followed in developing, constructing, testing, evaluating, integrating, implementing and maintaining the expert system. Concludes that an expert system could be used to solve various garment manufacturing problems and would contribute to garment quality improvement through a standardized apparel production process.

A source of Bayesian priors

The authors examine the construction of a connectionist expert system without specifying the network structure before training. The generated connectionist expert system consists of many features, such as operation of forward and backward inference based on partial input information, online learning, noisy data handling, generalization, and the explanation ability. Two sample problems, the Knowledge Base Evaluator 1 and Treatment of Posiboost, are considered in order to illustrate the workings of the connectionist expert system. The training algorithm, which has network generating ability, is presented to build the knowledge base of the connectionist expert system. It provides the abilities needed to realize the described features of the connectionist expert system. This proposed system can be easily used to build expert systems quickly, and the inferencing in the developed systems will be fast. >

A legal expert system expresses as a set of formal rules the norms found in the provisions of a statute or regulation, in case law or other legal texts. The process of constructing the system involves interpreting these legal texts and recasting each of them into one or more formal legal rules. The primary sources of law or law-formulations, to use Susskind’s term [Susskind 87, 36-37, 124], must be transformed into law-statements—statements about what the content of the law is—and these in turn must be translated into a formal language as rules of inference which Susskind terms legal productions. These transformations take place outside the context of specific cases, that is without reference to concrete legal problems those rules are designed to solve. The construction of a legal expert system involves two conceptual steps: to identify the legal norms that a statute conveys and to express these norms as formal rules. Several strategies have been proposed for this transformation process. Some consist of a rather straightforward transposition of law texts into formal language at the expense of a substantial loss of meaning of the legal concepts involved; others call for a subtler and more complex legal analysis. In all cases, however, what is at stake is the interpretation of legal documents. The problem of interpreting legal documents is well known to lawyers. In legal usage, the term interpretation is employed when the meaning of a legal text, typically a statutory provision, has to be assessed in a concrete situation. In constructing expert systems, one must interpret legal documents ahead of such concrete applications. This interpretation can only be provisional. The best one can do in constructing the knowledge base is to foreclose as few as possible of the meanings for particular provisions one may ultimately want to consider in concrete situations. Conversely, when using the system in a particular situation, one may want to consider different interpretations and their

Autonomous machines such as a planetary explorer must learn from the real universe, which can be seen as a single event of infinite complexity or as an infinite set of trivial events. Current inductive learning systems generalize from a finite set of events provided by a human teacher or a software environment. Even a constrained universe is difficult to represent as a finite set of events comprising various types of useful information. Cues used by humans to perform this representation task include temporal or spatial relationships among environmental data. We discuss a method for identifying meaningful complex events in the universe of an autonomous learning robot. When no temporal concept is known to link two descriptors, temporal proximity of events is used to pair simple descriptor events into complex events for inductive learning. When a temporal concept is known to link two descriptors, that concept is used to guide the pairing of descriptor events. We discuss the efficiency improvements which arise from using temporal concepts in this manner. The method is embedded in GPAL 1.3, a general purpose autonomous learner that uses a knowledge-based learning and control strategy which models the scientific method.

This paper reviews the stages of the human decision-making process with an emphasis on evaluation strategies. A comparison between the components utilized in human and computer decision-making is outlined. The role of biases and risk in human decision-making is discussed. Finally, the issue of the impact of human evaluation strategies and biases in the construction and use of expert systems is considered.

Importance-weighted conditional adversarial network for unsupervised domain adaptation

The paper deals with a number of modern methods and algorithms for image processing, namely: Kirsch filter, nonlinear filters Robert and Sobel, Wallace and SUSAN methods, as well as software for application of these methods on the example of analysis of tomographic images of macular changes of the retina. The most informative for image segmentation is the Kirsch filter based method and the Sobel nonlinear filter method. In contrast to the so-called deterministic distortions, which are often described by elemental functional transformations of the input image, models of additive, impulse and multiplicative noise are used to describe the random effects. As a rule, Kirsch, Roberts, Sobel algorithms are used to improve the quality of biomedical image processing. Features of construction of high-performance expert systems and devices of image processing and recognition by creation of neural-like methods and algorithms of parallel-hierarchical transformation, network methods of image pre-processing, development and creation of hardware specialized image computers based on them, which are combined with the eye, are considered. video processing (for example, the use of PCF, Q-conversion, parallel-hierarchical image encoding method, space-time ve matrix model of computing processes, parallel algorithms and structures that they reflect for parallel imaging) is a fairly urgent task. Thus, in contrast to the so-called deterministic distortions, which are often described by elemental functional transformations of the input image, it is advisable to use additive, pulse, and multiplicative noise models to describe the random effects. It is shown that, as a rule, the Kirsch, Roberts, Sobel algorithms are used to improve the quality of biomedical image processing.