2,795 research outputs found

    A Theory of Factfinding: The Logic for Processing Evidence

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    Academics have never agreed on a theory of proof. The darkest corner of anyoneā€™s theory has concerned how legal decisionmakers logically should find facts. This Article pries open that cognitive black box. It does so by employing multivalent logic, which enables it to overcome the traditional probability problems that impeded all prior attempts. The result is the first-ever exposure of the proper logic for finding a fact or a caseā€™s facts. The focus will be on the evidential processing phase, rather than the application of the standard of proof as tracked in my prior work. Processing evidence involves (1) reasoning inferentially from a piece of evidence to a degree of belief and of disbelief in the element to be proved, (2) aggregating pieces of evidence that all bear to some degree on one element in order to form a composite degree of belief and of disbelief in the element, and (3) considering the series of elemental beliefs and disbeliefs to reach a decision. Zeroing in, the factfinder in step #1 should connect each item of evidence to an element to be proved by constructing a chain of inferences, employing multivalent logicā€™s usual rules for conjunction and disjunction to form a belief function that reflects the belief and the disbelief in the element and also the uncommitted belief reflecting uncertainty. The factfinder in step #2 should aggregate, by weighted arithmetic averaging, the belief functions resulting from all the items of evidence that bear on any one element, creating a composite belief function for the element. The factfinder in step #3 does not need to combine elements, but instead should directly move to testing whether the degree of belief from each elementā€™s composite belief function sufficiently exceeds the corresponding degree of disbelief. In sum, the factfinder should construct a chain of inferences to produce a belief function for each item of evidence bearing on an element, and then average them to produce for each element a composite belief function ready for the element-by-element standard of proof. This Article performs the task of mapping normatively how to reason from legal evidence to a decision on facts. More significantly, it constitutes a further demonstration of how embedded the multivalent-belief model is in our law

    Aleatoric and Epistemic Uncertainty in Machine Learning: An Introduction to Concepts and Methods

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    The notion of uncertainty is of major importance in machine learning and constitutes a key element of machine learning methodology. In line with the statistical tradition, uncertainty has long been perceived as almost synonymous with standard probability and probabilistic predictions. Yet, due to the steadily increasing relevance of machine learning for practical applications and related issues such as safety requirements, new problems and challenges have recently been identified by machine learning scholars, and these problems may call for new methodological developments. In particular, this includes the importance of distinguishing between (at least) two different types of uncertainty, often referred to as aleatoric and epistemic. In this paper, we provide an introduction to the topic of uncertainty in machine learning as well as an overview of attempts so far at handling uncertainty in general and formalizing this distinction in particular.Comment: 59 page

    Uncertain Multi-Criteria Optimization Problems

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    Most real-world search and optimization problems naturally involve multiple criteria as objectives. Generally, symmetry, asymmetry, and anti-symmetry are basic characteristics of binary relationships used when modeling optimization problems. Moreover, the notion of symmetry has appeared in many articles about uncertainty theories that are employed in multi-criteria problems. Different solutions may produce trade-offs (conflicting scenarios) among different objectives. A better solution with respect to one objective may compromise other objectives. There are various factors that need to be considered to address the problems in multidisciplinary research, which is critical for the overall sustainability of human development and activity. In this regard, in recent decades, decision-making theory has been the subject of intense research activities due to its wide applications in different areas. The decision-making theory approach has become an important means to provide real-time solutions to uncertainty problems. Theories such as probability theory, fuzzy set theory, type-2 fuzzy set theory, rough set, and uncertainty theory, available in the existing literature, deal with such uncertainties. Nevertheless, the uncertain multi-criteria characteristics in such problems have not yet been explored in depth, and there is much left to be achieved in this direction. Hence, different mathematical models of real-life multi-criteria optimization problems can be developed in various uncertain frameworks with special emphasis on optimization problems

    Multi-Objective and Multi-Attribute Optimisation for Sustainable Development Decision Aiding

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    Optimization is considered as a decision-making process for getting the most out of available resources for the best attainable results. Many real-world problems are multi-objective or multi-attribute problems that naturally involve several competing objectives that need to be optimized simultaneously, while respecting some constraints or involving selection among feasible discrete alternatives. In this Reprint of the Special Issue, 19 research papers co-authored by 88 researchers from 14 different countries explore aspects of multi-objective or multi-attribute modeling and optimization in crisp or uncertain environments by suggesting multiple-attribute decision-making (MADM) and multi-objective decision-making (MODM) approaches. The papers elaborate upon the approaches of state-of-the-art case studies in selected areas of applications related to sustainable development decision aiding in engineering and management, including construction, transportation, infrastructure development, production, and organization management

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    Standpoint Logic: A Logic for Handling Semantic Variability, with Applications to Forestry Information

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    It is widely accepted that most natural language expressions do not have precise universally agreed definitions that fix their meanings. Except in the case of certain technical terminology, humans use terms in a variety of ways that are adapted to different contexts and perspectives. Hence, even when conversation participants share the same vocabulary and agree on fundamental taxonomic relationships (such as subsumption and mutual exclusivity), their view on the specific meaning of terms may differ significantly. Moreover, even individuals themselves may not hold permanent points of view, but rather adopt different semantics depending on the particular features of the situation and what they wish to communicate. In this thesis, we analyse logical and representational aspects of the semantic variability of natural language terms. In particular, we aim to provide a formal language adequate for reasoning in settings where different agents may adopt particular standpoints or perspectives, thereby narrowing the semantic variability of the vague language predicates in different ways. For that purpose, we present standpoint logic, a framework for interpreting languages in the presence of semantic variability. We build on supervaluationist accounts of vagueness, which explain linguistic indeterminacy in terms of a collection of possible interpretations of the terms of the language (precisifications). This is extended by adding the notion of standpoint, which intuitively corresponds to a particular point of view on how to interpret vague terminology, and may be taken by a person or institution in a relevant context. A standpoint is modelled by sets of precisifications compatible with that point of view and does not need to be fully precise. In this way, standpoint logic allows one to articulate finely grained and structured stipulations of the varieties of interpretation that can be given to a vague concept or a set of related concepts and also provides means to express relationships between different systems of interpretation. After the specification of precisifications and standpoints and the consideration of the relevant notions of truth and validity, a multi-modal logic language for describing standpoints is presented. The language includes a modal operator for each standpoint, such that \standb{s}\phi means that a proposition Ļ•\phi is unequivocally true according to the standpoint ss --- i.e.\ Ļ•\phi is true at all precisifications compatible with ss. We provide the logic with a Kripke semantics and examine the characteristics of its intended models. Furthermore, we prove the soundness, completeness and decidability of standpoint logic with an underlying propositional language, and show that the satisfiability problem is NP-complete. We subsequently illustrate how this language can be used to represent logical properties and connections between alternative partial models of a domain and different accounts of the semantics of terms. As proof of concept, we explore the application of our formal framework to the domain of forestry, and in particular, we focus on the semantic variability of `forest'. In this scenario, the problematic arising of the assignation of different meanings has been repeatedly reported in the literature, and it is especially relevant in the context of the unprecedented scale of publicly available geographic data, where information and databases, even when ostensibly linked to ontologies, may present substantial semantic variation, which obstructs interoperability and confounds knowledge exchange

    Enforcement of the law in the People's Republic of China - with focus on international civil litigation and arbitration

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    Includes bibliographical references.The main aspect of the paper is the investigation of the enforcement of law of foreign (and domestic) judgments as well as arbitral awards in PeopleĀ“s Republic of China (PRC). The focus lies on international civil litigation and arbitration. For this purpose it is essential to elaborate on the judicial structures and its impact on the enforcement of laws in the PRC. The court system as well as its size and performance, the prosecution system, the lawyer system, the jurisdiction and the arbitration system will be briefly discussed. Thereafter, the study focuses on the recognition and enforcement of civil judgements and arbitral awards in the PRC. The organization of the enforcement and its procedure, laws and regulations in general will be addressed before the enforcement of civil judgements and arbitral awards will be investigated in detail. The investigation of the enforcement of judgments in the peopleā€™s courts of China is separated in the enforcement of domestic judgments and foreign judgments. While examining the recognition and enforcement of arbitral awards it is important to consider the different categories of awards. In the following the challenges and obstacles facing the Chinese judicial system will be determined. The legal education, the lack of professionalism, local protectionism and the lack of judicial independence are just some of them. The progress China has made in the last decades will also be mentioned. Especially the judicial reforms from 1999 to 2014 and the efforts made to improve the enforcement of law. In addition the practical side will be determined, therefore, important or recent cases will be considered. The goal of the paper is to give an overview of the current social and economic environment of law enforcement and the measures which should be taken to improve the law enforcement in the PRC. Due to the lack of official statistics in regard of law enforcement in the PRC, the study is based on collected information from different sources

    Decision Support Model For Construction Crew Reassignments

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    The reassignment of crews on a construction project in response to changes occurs on a frequent basis. The factors that affect the crew reassignment decision can be myriad and most are not known with certainty. This research addresses the need for a decision support model to assist construction managers with the crew reassignment problem. The model design makes use of certainty factors in a decision tree structure. The research helped to determine the elements in the decision tree, the appropriate combination rules to use with the certainty factors, and the method for combining the certainty factors and costs to develop a measure of cost for each decision option. The research employed surveys, group meetings, and individual interviews of experienced construction managers and superintendents to investigate the current methods used by decision makers to identify and evaluate the key elements of the construction crew reassignment decision. The initial research indicated that the use of certainty factors was preferred over probabilities for representing the uncertainties. Since certainty factors have not been used in a traditional decision tree context, a contribution of the research is the development and testing of techniques for combining certainty factors, durations, and costs in order to represent the uncertainty and to emulate the decision process of the experts interviewed. The developed model provides the decision maker with an estimate of upper and lower bounds of costs for each crew reassignment option. The model was applied contemporaneously to six changes on three ongoing construction projects to test the model and assess its usefulness. The model provides a previously unavailable tool for the prospective identification and estimation of productivity losses and potential costs that emanate from changes. The users indicated the model process resulted in concise and complete compilations of the elements of the crew reassignment decision and that the model outputs were consistent with the users\u27 expectations

    A technique for determining viable military logistics support alternatives

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    A look at today's US military will see them operating much beyond the scope of protecting and defending the United States. These operations now consist of, but are not limited to humanitarian aid, disaster relief, and conflict resolution. This broad spectrum of operational environments has necessitated a transformation of the individual military services into a hybrid force that can leverage the inherent and emerging capabilities from the strengths of those under the umbrella of the Department of Defense (DOD), this concept has been coined Joint Operations. Supporting Joint Operations requires a new approach to determining a viable military logistics support system. The logistics architecture for these operations has to accommodate scale, time, varied mission objectives, and imperfect information. Compounding the problem is the human in the loop (HITL) decision maker (DM) who is a necessary component for quickly assessing and planning logistics support activities. Past outcomes are not necessarily good indicators of future results, but they can provide a reasonable starting point for planning and prediction of specific needs for future requirements. Adequately forecasting the necessary logistical support structure and commodities needed for any resource intensive environment has progressed well beyond stable demand assumptions to one in which dynamic and nonlinear environments can be captured with some degree of fidelity and accuracy. While these advances are important, a holistic approach that allows exploration of the operational environment or design space does not exist to guide the military logistician in a methodical way to support military forecasting activities. To bridge this capability gap, a method called A Technique for Logistics Architecture Selection (ATLAS) has been developed. This thesis describes and applies the ATLAS method to a notional military scenario that involves the Navy concept of Seabasing and the Marine Corps concept of Distributed Operations applied to a platoon sized element. This work uses modeling and simulation to incorporate expert opinion and knowledge of military operations, dynamic reasoning methods, and certainty analysis to create a decisions support system (DSS) that can be used to provide the DM an enhanced view of the logistics environment and variables that impact specific measures of effectiveness.Ph.D.Committee Chair: Mavris, Dimitri; Committee Member: Fahringer, Philip; Committee Member: Nixon, Janel; Committee Member: Schrage, Daniel; Committee Member: Soban, Danielle; Committee Member: Vachtsevanos, Georg
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