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.

In the study of forms of legal reasoning, logic and argumentation theory long followed separate tracks. `Legal logicians' tended to focus on a deductive reconstruction of justifying a decision, disregarding the dialectical process leading to the chosen justification. Others instead emphasized the adversarial and discretionary nature of legal reasoning, involving reasonable evaluation of alternative choices, and the use of analogical reasoning. Recently, however, developments in Artificial Intelligence and Law have paved the way for overcoming this separation. Logic has widened its scope to defensible argumentation, and informal accounts of analogy and dialectics have inspired the construction of computer programs. Thus the prospect is emerging of an integrated logical and dialectical account of legal argument, adding to the understanding of legal reasoning, and providing a formal basis for computer tools that assist and mediate legal debates while leaving room for human initiative. This book presents contributions to this development. From a logical point of view it covers topics such as evaluating conflicting arguments, weighing reasons, modelling legal disputes as a dialogue game, the role of the burden of proof, the relation between principles, rules, reasons and facts, and the relation between deductive and nondeductive arguments. Written by leading scholars in the field and building on recent developments in logic and Artificial Intelligence, the chapters provide a state-of-the-art account of research on the logical aspects of legal argument.

The Software Engineering and Knowledgebase Systems (SOFfEKS) Research Group of the Department of Computer Science, Concordia University, Canada, organized a workshop on Incompleteness and Uncertainty in Information Systems from October 8-9, 1993 in Montreal. A major aim of the workshop was to bring together researchers who share a concern for issues of incompleteness and uncertainty. The workshop attracted people doing fundamental research and industry oriented research in databases, software engineering and AI from North America, Europe and Asia. The workshop program featured six invited talks and twenty other presentations. The invited speakers were: Martin Feather (University of Southern CalifornialInformation Systems Institute) Laks V. S. Lakshmanan (Concordia University) Ewa Orlowska (Polish Academy of Sciences) z. Pawlak (Warsaw Technical University and Academy of Sciences) F. Sadri (Concordia University) A. Skowron (Warsaw University) The papers can be classified into four groups: rough sets and logic, concept analysis, databases and information retrieval, and software engineering. The workshop opened with a warm welcome speech from Dr. Dan Taddeo, Dean, Faculty of Engineering and Computer Science. The first day's presentations were in rough sets, databases and information retrieval. Papers given on the second day centered around software engineering and concept analysis. Sufficient time was given in between presentations to promote active interactions and numerous lively discussions. At the end of two days, the participants expressed their hope that this workshop would be continued.

It is necessary to practice methodological doubt, like Descartes, in - der to loosen the hold of mental habits; and it is necessary to cultivate logical imagination, in order to have a number of hypotheses at c- mand, and not to be the slave of the one which common sense has r- dered easy to imagine. These two processes, of doubting the familiar and imagining the unfamiliar, are corrective, and form the chief part of the mental training required for a philosopher. Bertrand Russell At every stage and in all circumstances knowledge is incomplete and provisional, conditioned and limited by the historical circumstances under which it was acquired, including the means and methods used for gaining it and the historically conditioned assumptions and categories used in the formulation of ideas and conclusions. Maurice Cornforth This monograph is the second in the series of meta-theoretic analysis of fuzzy paradigm and its contribution and possible contribution to formal reasoning in order to free the knowledge production process from the ridge frame of the classical paradigm that makes its application to soft and inexact sciences d- ficult or irrelevant. The work in the previous monograph was strictly devoted to problems of theory of knowledge and critique of classical, bounded and other rationalities in decision-choice processes regarding the principles of verification, falsification or corroboration in knowledge production. This monograph deals mostly with epistemic decision-choice models and theories and how they are related to both the classical and fuzzy paradigms.

We do not perceive the present as it is and in totality, nor do we infer the future from the present with any high degree of dependability, nor yet do we accurately know the consequences of our own actions. In addition, there is a fourth source of error to be taken into account, for we do not execute actions in the precise form in which they are imaged and willed. Frank H. Knight [R4.34, p. 202] The “degree” of certainty of confidence felt in the conclusion after it is reached cannot be ignored, for it is of the greatest practical signi- cance. The action which follows upon an opinion depends as much upon the amount of confidence in that opinion as it does upon fav- ableness of the opinion itself. The ultimate logic, or psychology, of these deliberations is obscure, a part of the scientifically unfathomable mystery of life and mind. Frank H. Knight [R4.34, p. 226-227] With some inaccuracy, description of uncertain consequences can be classified into two categories, those which use exclusively the language of probability distributions and those which call for some other principle, either to replace or supplement.

This article is a first step in the direction of extending possibilistic planning to account for incomplete and imprecise knowledge of the world state.

Using qualitative methods to deal with imperfect information.