Dynamics Uncertainty And Reasoning

This volume collects selected papers presented at the Second Chinese Conference on Logic and Argumentation in 2018 held in Hangzhou, China. The papers presented reflect recent advances in logic and argumentation, as well as the connections between the two, and also include invited papers contributed by leading experts in these fields. The book covers a wide variety of topics related to dynamics, uncertainty and reasoning. It continues discussions on the interplay between logic and argumentation which has a long history from Aristotle’s ancient logic to very recent formal argumentation in AI.

The dynamics of systems have proven to be very powerful tools in understanding the behavior of different natural phenomena throughout the last two centuries. However, the attributes of natural systems are observed to deviate from their classical states due to the effect of different types of uncertainties. Actually, randomness and impreciseness are the two major sources of uncertainties in natural systems. Randomness is modeled by different stochastic processes and impreciseness could be modeled by fuzzy sets, rough sets, Dempster–Shafer theory, etc.

This volume, the 6th volume in the DRUMS Handbook series, is part of the after math of the successful ESPRIT project DRUMS (Defeasible Reasoning and Un certainty Management Systems) which took place in two stages from 1989-1996. In the second stage (1993-1996) a work package was introduced devoted to the topics Reasoning and Dynamics, covering both the topics of 'Dynamics of Rea soning', where reasoning is viewed as a process, and 'Reasoning about Dynamics', which must be understood as pertaining to how both designers of and agents within dynamic systems may reason about these systems. The present volume presents work done in this context. This work has an emphasis on modelling and formal techniques in the investigation of the topic "Reasoning and Dynamics", but it is not mere theory that occupied us. Rather research was aimed at bridging the gap between theory and practice. Therefore also real-life applications of the modelling techniques were considered, and we hope this also shows in this volume, which is focused on the dynamics of reasoning processes. In order to give the book a broader perspective, we have invited a number of well-known researchers outside the project but working on similar topics to contribute as well. We have very pleasant recollections of the project, with its lively workshops and other meetings, with the many sites and researchers involved, both within and outside our own work package.

Uncertainty exists almost everywhere, except in the most idealized situations; it is not only an inevitable and ubiquitous phenomenon, but also a fundamental sci- ti?c principle. Furthermore, uncertainty is an attribute of information and, usually, decision-relevant information is uncertain and/or imprecise, therefore the abilities to handle uncertain information and to reason from incomplete knowledge are c- cial features of intelligent behaviour in complex and dynamic environments. By carefully exploiting our tolerance for imprecision and approximation we can often achieve tractability, robustness, and better descriptions of reality than traditional - ductive methods would allow us to obtain. In conclusion, as we move further into the ageofmachineintelligence,theproblemofreasoningunderuncertainty,in other words, drawing conclusions from partial knowledge, has become a major research theme. Not surprisingly,the rigoroustreatment of uncertaintyrequiressophisticated - chinery, and the present volume is conceived as a contribution to a better und- standing of the foundations of information processing and decision-making in an environment of uncertainty, imprecision and partiality of truth. This volume draws on papers presented at the 2008 Conference on Information Processing and Management of Uncertainty (IPMU), held in Malaga, ́ Spain, or- nized by the University of Mal ́ aga. The conference brought together some of the world’s leading experts in the study of uncertainty.

In this study we will build an uncertainty logic by using the concept of probability, with those of plausibility, credibility and possibility. We will provide several models which treats uncertainty information and allow to perform more reliable forecasts. After that, we will prove the models reliability through a final simulation on the Biometrics and Sport fields using one of the models; these simulation are fully replicabile for each field and for each of the provided models.

Cross-border flows of goods, services, capital, knowledge, and ideas have substantially increased. This book focuses on how the interface between firm-specific advantages, liability of foreignness, and location-specific advantages are spelled out in the more global world.

Fluid flows are characterized by uncertain inputs such as random initial data, material and flux coefficients, and boundary conditions. The current volume addresses the pertinent issue of efficiently computing the flow uncertainty, given this initial randomness. It collects seven original review articles that cover improved versions of the Monte Carlo method (the so-called multi-level Monte Carlo method (MLMC)), moment-based stochastic Galerkin methods and modified versions of the stochastic collocation methods that use adaptive stencil selection of the ENO-WENO type in both physical and stochastic space. The methods are also complemented by concrete applications such as flows around aerofoils and rockets, problems of aeroelasticity (fluid-structure interactions), and shallow water flows for propagating water waves. The wealth of numerical examples provide evidence on the suitability of each proposed method as well as comparisons of different approaches.

This book constitutes the refereed proceedings of the 9th European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty, ECSQARU 2007, held in Hammammet, Tunisia, Oktober 31 - November 2, 2007. The 78 revised full papers presented together with 3 invited papers were carefully reviewed and selected from over hundret submissions for inclusion in the book. The papers are organized in topical sections on Bayesian networks, graphical models, learning causal networks, planning, causality and independence, preference modelling and decision, argumentation systems, inconsistency handling, belief revision and merging, belief functions, fuzzy models, many-valued logical systems, uncertainty logics, probabilistic reasoning, reasoning models under uncertainty, uncertainty measures, probabilistic classifiers, classification and clustering, and industrial applications.