it could rain weather forecasting as a reasoning process

Abstract Meteorological forecasting is the process of providing reliable prediction about the future weathear within a given interval of time. Forecasters adopt a model of reasoning that can be mapped onto an integrated conceptual framework. A forecaster essentially precesses data in advance by using some models of machine learning to extract macroscopic tendencies such as air movements, pressure, temperature, and humidity differentials measured in ways that depend upon the model, but fundamentally, as gradients. Limit values are employed to transform these tendencies in fuzzy values, and then compared to each other in order to extract indicators, and then evaluate these indicators by means of priorities based upon distance in fuzzy values. We formalise the method proposed above in a workflow of evaluation steps, and propose an architecture that implements the reasoning techniques

A Computational Framework for Formalizing Rules and Managing Changes in Normative Systems

Legal texts are typically written in a natural language. However, a legal text that is written in a formal language has the advantage of being subject to automation, at least partially. Such a translation is not easy, and the matter is even more complex because the law changes with time, so if we formalized a legal text that was originally written in natural language, there is a need to keep track of the change. This thesis proposes original developments on these subjects. In order to formalize a legal document, we provide a pipeline for the translation of a legal text from natural to formal language and we apply it to the case of natural resources contracts. In general, adjectives play an important role in a text and they allow to characterize it: for this reason we developed a logical system aimed at reasoning with gradable adjectives. Regarding norm change, we provide an ontology to represent change in a normative system, some basic mechanisms by which an agent may acquire new norms, and a study on the problem of revising a defeasible theory by only changing its facts. Another contribution of this thesis is a general framework for revision that includes the previous points as specific cases

Computational Complexity of Strong Admissibility for Abstract Dialectical Frameworks

Abstract dialectical frameworks (ADFs) have been introduced as a formalism for modeling and evaluating argumentation allowing general logical satisfaction conditions. Different criteria used to settle the acceptance of arguments arecalled semantics. Semantics of ADFs have so far mainly been defined based on the concept of admissibility. Recently, the notion of strong admissibility has been introduced for ADFs. In the current work we study the computational complexityof the following reasoning tasks under strong admissibility semantics. We address 1. the credulous/skeptical decision problem; 2. the verification problem; 3. the strong justification problem; and 4. the problem of finding a smallest witness of strong justification of a queried argument

Proceedings of the 11th Workshop on Nonmonotonic Reasoning

These are the proceedings of the 11th Nonmonotonic Reasoning Workshop. The aim of this series is to bring together active researchers in the broad area of nonmonotonic reasoning, including belief revision, reasoning about actions, planning, logic programming, argumentation, causality, probabilistic and possibilistic approaches to KR, and other related topics. As part of the program of the 11th workshop, we have assessed the status of the field and discussed issues such as: Significant recent achievements in the theory and automation of NMR; Critical short and long term goals for NMR; Emerging new research directions in NMR; Practical applications of NMR; Significance of NMR to knowledge representation and AI in general

Multi-Context Reasoning in Continuous Data-Flow Environments

The field of artificial intelligence, research on knowledge representation and reasoning has originated a large variety of formats, languages, and formalisms. Over the decades many different tools emerged to use these underlying concepts. Each one has been designed with some specific application in mind and are even used nowadays, where the internet is seen as a service to be sufficient for the age of Industry 4.0 and the Internet of Things. In that vision of a connected world, with these many different formalisms and systems, a formal way to uniformly exchange information, such as knowledge and belief is imperative. That alone is not enough, because even more systems get integrated into the online world and nowadays we are confronted with a huge amount of continuously flowing data. Therefore a solution is needed to both, allowing the integration of information and dynamic reaction to the data which is provided in such continuous data-flow environments. This work aims to present a unique and novel pair of formalisms to tackle these two important needs by proposing an abstract and general solution. We introduce and discuss reactive Multi-Context Systems (rMCS), which allow one to utilise different knowledge representation formalisms, so-called contexts which are represented as an abstract logic framework, and exchange their beliefs through bridge rules with other contexts. These multiple contexts need to mutually agree on a common set of beliefs, an equilibrium of belief sets. While different Multi-Context Systems already exist, they are only solving this agreement problem once and are neither considering external data streams, nor are they reasoning continuously over time. rMCS will do this by adding means of reacting to input streams and allowing the bridge rules to reason with this new information. In addition we propose two different kind of bridge rules, declarative ones to find a mutual agreement and operational ones for adapting the current knowledge for future computations. The second framework is more abstract and allows computations to happen in an asynchronous way. These asynchronous Multi-Context Systems are aimed at modelling and describing communication between contexts, with different levels of self-management and centralised management of communication and computation. In this thesis rMCS will be analysed with respect to usability, consistency management, and computational complexity, while we will show how asynchronous Multi-Context Systems can be used to capture the asynchronous ideas and how to model an rMCS with it. Finally we will show how rMCSs are positioned in the current world of stream reasoning and that it can capture currently used technologies and therefore allows one to seamlessly connect different systems of these kinds with each other. Further on this also shows that rMCSs are expressive enough to simulate the mechanics used by these systems to compute the corresponding results on its own as an alternative to already existing ones. For asynchronous Multi-Context Systems, we will discuss how to use them and that they are a very versatile tool to describe communication and asynchronous computation