Inconsistency Management from the Standpoint of Possibilistic Logic

International audienceUncertainty and inconsistency pervade human knowledge. Possibilistic logic, where propositional logic formulas are associated with lower bounds of a necessity measure, handles uncertainty in the setting of possibility theory. Moreover, central in standard possibilistic logic is the notion of inconsistency level of a possibilistic logic base, closely related to the notion of consistency degree of two fuzzy sets introduced by L. A. Zadeh. Formulas whose weight is strictly above this inconsistency level constitute a sub-base free of any inconsistency. However, several extensions, allowing for a paraconsistent form of reasoning, or associating possibilistic logic formulas with information sources or subsets of agents, or extensions involving other possibility theory measures, provide other forms of inconsistency, while enlarging the representation capabilities of possibilistic logic. The paper offers a structured overview of the various forms of inconsistency that can be accommodated in possibilistic logic. This overview echoes the rich representation power of the possibility theory framework

Epistemic Teleology: Synchronic and Diachronic

According to a widely held view of the matter, whenever we assess beliefs as ‘rational’ or ‘justified’, we are making normative judgements about those beliefs. In this discussion, I shall simply assume, for the sake of argument, that this view is correct. My goal here is to explore a particular approach to understanding the basic principles that explain which of these normative judgements are true. Specifically, this approach is based on the assumption that all such normative principles are grounded in facts about values, and the normative principles that apply to beliefs in particular are grounded in facts about alethic value––a kind of value that is exemplified by believing what is true and not believing what is false. In this chapter, I shall explain what I regard as the best way of interpreting this approach. In doing so, I shall also show how this interpretation can solve some problems that have recently been raised for approaches of this kind by Selim Berker, Jennifer Carr, Michael Caie, and Hilary Greaves

Relative-fuzzy: a novel approach for handling complex ambiguity for software engineering of data mining models

There are two main defined classes of uncertainty namely: fuzziness and ambiguity, where ambiguity is ‘one-to-many’ relationship between syntax and semantic of a proposition. This definition seems that it ignores ‘many-to-many’ relationship ambiguity type of uncertainty. In this thesis, we shall use complex-uncertainty to term many-to-many relationship ambiguity type of uncertainty. This research proposes a new approach for handling the complex ambiguity type of uncertainty that may exist in data, for software engineering of predictive Data Mining (DM) classification models. The proposed approach is based on Relative-Fuzzy Logic (RFL), a novel type of fuzzy logic. RFL defines a new formulation of the problem of ambiguity type of uncertainty in terms of States Of Proposition (SOP). RFL describes its membership (semantic) value by using the new definition of Domain of Proposition (DOP), which is based on the relativity principle as defined by possible-worlds logic. To achieve the goal of proposing RFL, a question is needed to be answered, which is: how these two approaches; i.e. fuzzy logic and possible-world, can be mixed to produce a new membership value set (and later logic) that able to handle fuzziness and multiple viewpoints at the same time? Achieving such goal comes via providing possible world logic the ability to quantifying multiple viewpoints and also model fuzziness in each of these multiple viewpoints and expressing that in a new set of membership value. Furthermore, a new architecture of Hierarchical Neural Network (HNN) called ML/RFL-Based Net has been developed in this research, along with a new learning algorithm and new recalling algorithm. The architecture, learning algorithm and recalling algorithm of ML/RFL-Based Net follow the principles of RFL. This new type of HNN is considered to be a RFL computation machine. The ability of the Relative Fuzzy-based DM prediction model to tackle the problem of complex ambiguity type of uncertainty has been tested. Special-purpose Integrated Development Environment (IDE) software, which generates a DM prediction model for speech recognition, has been developed in this research too, which is called RFL4ASR. This special purpose IDE is an extension of the definition of the traditional IDE. Using multiple sets of TIMIT speech data, the prediction model of type ML/RFL-Based Net has classification accuracy of 69.2308%. This accuracy is higher than the best achievements of WEKA data mining machines given the same speech data

Probability and nonclassical logic

Classical tautologies have probability. Classical contradictions have probability. These familiar features reflect a connection between standard probability theory and classical logic. In contexts in which classical logic is questioned—to deal with the paradoxes of self-reference, or vague propositions, for the purposes of scientific theory or metaphysical anti-realism—we must equally question standard probability theory. Section 1 covers the intended interpretation of ‘nonclassical logic’ and ‘probability’. Section 2 reviews the connection between classical logic and classical probability. Section 3 briefly reviews salient aspects of nonclassical logic, laying out a couple of simple examples to fix ideas. Section 4 explores modifications of probability theory. The variations laid down will be motivated initially by formal analogies to the classical setting. In section 5, however, we look at two foundational justifications for the presentations of ‘nonclassical probabilities’ that are arrived at. Sections 6-7 describe extensions of the nonclassical framework: to conditionalization and decision theory in particular. Section 8 will consider some alternative approaches, and section 9 evaluates progress

The aggregation of propositional attitudes: towards a general theory

How can the propositional attitudes of several individuals be aggregated into overall collective propositional attitudes? Although there are large bodies of work on the aggregation of various special kinds of propositional attitudes, such as preferences, judgments, probabilities and utilities, the aggregation of propositional attitudes is seldom studied in full generality. In this paper, we seek to contribute to filling this gap in the literature. We sketch the ingredients of a general theory of propositional attitude aggregation and prove two new theorems. Our first theorem simultaneously characterizes some prominent aggregation rules in the cases of probability, judgment and preference aggregation, including linear opinion pooling and Arrovian dictatorships. Our second theorem abstracts even further from the specific kinds of attitudes in question and describes the properties of a large class of aggregation rules applicable to a variety of belief-like attitudes. Our approach integrates some previously disconnected areas of investigation.mathematical economics;

Adams Thesis and the Local Interpretation of Conditionals

Adams' Thesis states that the probability of a conditional is the probability of the consequent conditional on the antecedent. S. Kaufmann introduced a rival method, the so-called ``local interpretation'', for calculating the probability of a conditional that, according to a purported majority, squares better with intuition in some circumstances. He also gives an example purporting to show that this new method sometimes corresponds to rational action. We challenge the intuitions and expose a mathematical error in the example. We also offer a model for the local interpretivist semantics. This model puts theoretical local interpretivists on ground as solid as that of Thesis abiders for whom conditionals have truth conditions