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The article considers two different interpretations of the reason model of precedent pioneered by John Horty. On a plausible interpretation of the reason model, past cases provide reasons to prioritize reasons favouring the same outcome as a past case over reasons favouring the opposing outcome. Here I consider the merits of this approach to the role of precedent in legal reasoning in comparison with a closely related view favoured by some legal theorists, according to which past cases provide reasons for undercutting (or 'excluding') reasons favouring the opposing outcome. After embedding both accounts within a general default logic, I note some important differences between the two approaches that emerge as a result of plausible distinctions between rebutting and undercutting defeat in formal models of legal reasoning. These differences stem from the 'preference independence' of undercutting defeat. Undercutting reasons succeed in defeating opposing reasons irrespective of their relative strength. As a result, the two accounts differ in their account of the way in which precedents constrain judicial reasoning. I conclude by suggesting that the two approaches can be integrated within a single model, in which the distinction between undercutting and rebutting defeat is used to account for the distinction between strict and persuasive forms of precedential constraint. [ABSTRACT FROM AUTHOR]

Reasoning is not just following logical rules, but a large part of human reasoning depends on our expectations about the world. To some extent, non-monotonic logic has been developed to account for the role of expectations. In this article, the focus is on expectations based on actions and their consequences. The analysis is based on a two-vector model of events where an event is represented in terms of two main components – the force of an action that drives the event, and the result of its application. Actions are modelled in terms of the force domain and the results are modelled with the aid of different domains for locations or properties of objects. As a consequence, the assumption that reasoning about causal relations should be made in terms of propositional structures becomes very unnatural. Instead, the reasoning will be based on the geometric and topological properties of causes and effects modelled in conceptual spaces.

Human and artificial reasoning has to deal with uncertain environments. Ideally, probabilistic information is available. However, sometimes probabilistic information may not be precise or it is missing entirely. In such cases we reason with higher-order uncertainty. Formal argumentation is one of the leading formal methods to model defeasible reasoning in artificial intelligence, in particular in the tradition of Dung's abstract argumentation. Also from the perspective of cognition, reasoning has been considered as argumentative and social in nature, for instance by Mercier and Sperber. In this paper we use formal argumentation to provide a framework for reasoning with higher-order uncertainty. Our approach builds strongly on Haenni's system of probabilistic argumentation, but enhances it in several ways. First, we integrate it with deductive argumentation, both in terms of the representation of arguments and attacks, and in terms of utilizing abstract argumentation semantics for selecting some out of a set of possibly conflicting arguments. We show how our system can be adjusted to perform well under the so-called rationality postulates of formal argumentation. Second, we provide several notions of argument strength which are studied both meta-theoretically and empirically. In this way the paper contributes a formal model of reasoning with higher-order uncertainty with possible applications in artificial intelligence and human cognition.

Boolean Dynamical Systems (BDSs) are networks described by Boolean variables. A new representation of BDSs is presented in this article by using modal non-monotonic logic (H ). This approach allows Boolean Networks to be represented by a set of modal formulas and therefore can be used to describe and learn their properties. The study of a BDS focuses in particular on the search of stable configurations, limit cycles and unstable cycles, which help to characterize a large type of Gene Networks. In this article is presented the identification of such asymptotic properties by introduction of a new concept, ghost extensions. Using ghost extensions, it is possible to translate BDSs in propositional calculus and consequently to use SAT algorithms. [ABSTRACT FROM AUTHOR]

According to existing accounts of indicative conditionals, any argument of the following form is valid: Here, I present a set of counterexamples to show that there exist invalid arguments of this form. I argue that this data poses serious problems to variably strict accounts of conditionals (Lewis 1973; Stalnaker 1968), as such accounts are structurally unable to accommodate it. Dynamic strict accounts (von Fintel 2001; Gillies, 2007; Willer 2017), however, are a different story. While existing dynamic strict accounts do not accommodate the data, they are in principle able to, and I propose a modified dynamic strict account, drawing from von Fintel (2001), that does. The key modification is this: whereas existing dynamic strict accounts take into account only the effects of conditional antecedents in changing the semantic context, the data shows that we must also take into account the effects of conditional consequents in changing the semantic context. [ABSTRACT FROM AUTHOR]

I propose a novel hyperintensional semantics for belief revision and a corresponding system of dynamic doxastic logic. The main goal of the framework is to reduce some of the idealisations that are common in the belief revision literature and in dynamic epistemic logic. The models of the new framework are primarily based on potentially incomplete or inconsistent collections of information, represented by situations in a situation space. I propose that by shifting the representational focus of doxastic models from belief sets to collections of information, and by defining changes of beliefs as artifacts of changes of information, we can achieve a more realistic account of belief representation and belief change. The proposed dynamic operation suggests a non-classical way of changing beliefs: belief revision occurs in non-explosive environments which allow for a non-monotonic and hyperintensional belief dynamics. A logic that is sound with respect to the semantics is also provided. [ABSTRACT FROM AUTHOR]

A 6f-scheme of Fourier holography with resonant architecture is considered, which implements memory replenishment with new information that contradicts the previously recorded. It is shown that the low-frequency attenuation due to the nonlinearity of the exposure characteristics of holographic recording media in the initial reference holographic image recorded in a narrow filtering range corresponding to the degradation in the correlation plane of the global maximum of the autocorrelation function below the lateral maxima leads to the response instability – an intermittent mode. It is shown that the intermittent mode corresponds to the restructuring of the autocorrelation function of a composite standard recorded in holograms from one range of values of the approximation model parameters to another. It is shown that the correlation length of the composite image recorded in holograms is an order parameter of the system; its rapid change precedes the loss of response stability and the transition to an unstable regime with intermittency. The results of numerical simulation are presented.

We extend universal moral grammar theory (UMGT) with nonmonotoniclogic. Our experiment shows that such revision isnecessary as it allows to account for the effects of alleviationsand aggravations in moral reasoning. Our new theory updatesUMGT from classical to non-monotonic logic, which reflectsthe incompleteness of information and uncertainty in actualhuman reasoning. In addition, it provides an explanation ofthe paradoxical findings in the moral dilemma of the Trolleyproblem and the Knobe effect

The phenomenon of defeasibility has long been a central theme in legal literature. This essay aims to shed new light on that phenomenon by clarifying some fundamental conceptual issues. First, the most widespread definition of legal defeasibility is examined and criticized. The essay shows that such a definition is poorly constructed, inaccurate and generates many problems. Indeed, the definition hides the close relationship between legal defeasibility and legal interpretation. Second, this essay argues that no new definition is needed. I will show that from an interpretative standpoint, there is nothing special about legal defeasibility. Contrary to what some authors maintain, no unique or privileged source of legal defeasibility exists, nor are there privileged arguments to justify it. Specifically, legal defeasibility refers to interpretative outcomes deriving from interpretative arguments that, on the one hand, are very different from one another, and, on the other, are often employed to justify different interpretative outcomes. In the legal field, the problems related to defeasibility have little in common with the problems that this label covers in other areas—such as logic or epistemology—and they are nothing but the well-known problems related to legal interpretation. In conclusion, this paper argues that as far as legal argumentation is concerned, the notion of legal defeasibility lacks explanatory power, and it should be abandoned. [ABSTRACT FROM AUTHOR]

Copyright of Quaestio Iuris (QI) is the property of Editora da Universidade do Estado do Rio de Janeiro (EdUERJ) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Justification theory is a unifying semantic framework. While it has its roots in non-monotonic logics, it can be applied to various areas in computer science, especially in explainable reasoning; its most central concept is a justification: an explanation why a property holds (or does not hold) in a model. In this paper, we continue the study of justification theory by means of three major contributions. The first is studying the relation between justification theory and game theory. We show that justification frameworks can be seen as a special type of games. The established connection provides the theoretical foundations for our next two contributions. The second contribution is studying under which condition two different dialects of justification theory (graphs as explanations vs trees as explanations) coincide. The third contribution is establishing a precise criterion of when a semantics induced by justification theory yields consistent results. In the past proving that such semantics were consistent took cumbersome and elaborate proofs. We show that these criteria are indeed satisfied for all common semantics of logic programming. [ABSTRACT FROM AUTHOR]

We present a formal argumentation system for dealing with the detachment of prioritized conditional obligations and permissions. In the presence of facts and constraints, we answer the question whether an unconditional obligation or permission is detachable by considering arguments for and against its detachment. For the evaluation of arguments in favour of detachment, we use a Dung-style argumentation-theoretical semantics. We illustrate how violations and contrary-to-duty scenarios are dealt with in our framework and pay special attention to conflict-resolution via priorities. [ABSTRACT FROM AUTHOR]

In automotive industry, validation and maintenance of product configuration data is a complex task. Both orders from the customers and new product line designs from the R&D department are subject to a set of configuration rules to be satisfied. In this work, non-monotonic computational logic, answer set programming in particular, is applied to industrial-scale automotive product configuration problems. This methodology provides basic validation of the product configuration documentation and validation of single product orders, where Reiter style diagnosis provides minimal changes needed to correct an invalid order or a product configuration rule set. In addition, a method for discovering groups of product configuration variables that are strongly related can be obtained by small modification of the basic logic program, and by the usage of cautious and brave reasoning methods. As a result, options that are used in every, or respectively in no configuration, can easily be identified, as well as groups of options that are always used together or not at all. Finally it is possible to single out mandatory and obsolete options, relative to a preselected set of included or excluded options. Experimental results on an industrial dataset show applicability, example results, and computational feasibility with computation times on the order of seconds using a state-of-the-art answer set solver on standard PC hardware. [ABSTRACT FROM AUTHOR]

Copyright of Art Inquiry is the property of Lodz Scientific Society / Lodzkie Towarzystwo Naukowe and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The paper suggests a modal predicate logic that deals with classical quantification and modalities as well as intermediate operators, like “most” and “mostly”. Following up the theory of generalized quantifiers, we will understand them as two-placed operators and call them determiners. Quantifiers as well as modal operators will be constructed from them. Besides the classical deduction, we discuss a weaker probabilistic inference “therefore, probably” defined by symmetrical probability measures in Carnap’s style. The given probabilistic inference relates intermediate quantification to singular statements: “Most S are P” does not logically entail that a particular individual S is also P, but it follows that this is probably the case, where the probability is not ascribed to the propositions but to the inference. We show how this system deals with single case expectations while predictions of statistical statements remain generally problematic. [ABSTRACT FROM AUTHOR]

This paper analyzes the logic deduction and reasoning techniques used in several intelligent decision support algorithms, and proposes a flexible planning method GARIv using fuzzy descriptive logic in media enterprise management. Combined with experiments, the above methods are illustrated in terms of effectiveness and feasibility. In the end, the challenges and possible solutions of intelligent decision support algorithms with self-adaption reasoning are discussed. [ABSTRACT FROM AUTHOR]

Human and artificial reasoning has to deal with uncertain environments. Ideally, probabilistic information is available. However, sometimes probabilistic information may not be precise or it is missing entirely. In such cases we reason with higher-order uncertainty. Formal argumentation is one of the leading formal methods to model defeasible reasoning in artificial intelligence, in particular in the tradition of Dung's abstract argumentation. Also from the perspective of cognition, reasoning has been considered as argumentative and social in nature, for instance by Mercier and Sperber. In this paper we use formal argumentation to provide a framework for reasoning with higher-order uncertainty. Our approach builds strongly on Haenni's system of probabilistic argumentation, but enhances it in several ways. First, we integrate it with deductive argumentation, both in terms of the representation of arguments and attacks, and in terms of utilizing abstract argumentation semantics for selecting some out of a set of possibly conflicting arguments. We show how our system can be adjusted to perform well under the so-called rationality postulates of formal argumentation. Second, we provide several notions of argument strength which are studied both meta-theoretically and empirically. In this way the paper contributes a formal model of reasoning with higher-order uncertainty with possible applications in artificial intelligence and human cognition., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Straßer and Michajlova.)

The present paper analyses the topic of scientific discovery and the problem of the existence of a logical framework involved in such endeavour. We inquire how several non-monotonic logic frameworks and other formalisms can account for such a task. In the same vein, we analyse some key aspects of the historical and theoretical debate surrounding scientific discovery, in particular, the context of discovery and context of justification context distinction. We present an argument concerning the weakening of the discovery/justification context dichotomy based on the descriptive accent contained in the non-monotonic logic perspective together with its epistemological concerns. [ABSTRACT FROM PUBLISHER]

The paper presents a focused survey about the presence and the use of the concept of 'preferences' in Artificial Intelligence. Preferences are a central concept for decision making and have extensively been studied in disciplines such as economy, operational research, decision analysis, psychology and philosophy. However, in the recent years it has also become an important topic both for research and applications in Computer Science and more specifically in Artificial Intelligence, in fields spanning from recommender systems to automatic planning, from non monotonic reasoning to computational social choice and algorithmic decision theory. The survey essentially covers the basics of preference modelling, the use of preference in reasoning and argumentation, the problem of compact representations of preferences, preference learning and the use of non conventional preference models based on extended logical languages. It aims at providing a general reference for all researchers both in Artificial Intelligence and Decision Analysis interested in this exciting interdisciplinary topic. [ABSTRACT FROM AUTHOR]