Extracting Formal Models from Normative Texts

We are concerned with the analysis of normative texts - documents based on the deontic notions of obligation, permission, and prohibition. Our goal is to make queries about these notions and verify that a text satisfies certain properties concerning causality of actions and timing constraints. This requires taking the original text and building a representation (model) of it in a formal language, in our case the C-O Diagram formalism. We present an experimental, semi-automatic aid that helps to bridge the gap between a normative text in natural language and its C-O Diagram representation. Our approach consists of using dependency structures obtained from the state-of-the-art Stanford Parser, and applying our own rules and heuristics in order to extract the relevant components. The result is a tabular data structure where each sentence is split into suitable fields, which can then be converted into a C-O Diagram. The process is not fully automatic however, and some post-editing is generally required of the user. We apply our tool and perform experiments on documents from different domains, and report an initial evaluation of the accuracy and feasibility of our approach.Comment: Extended version of conference paper at the 21st International Conference on Applications of Natural Language to Information Systems (NLDB 2016). arXiv admin note: substantial text overlap with arXiv:1607.0148

Concept mapping and other formalisms as mindtools for representing knowledge

We seek to provide an alternative theoretical perspective on concept mapping (a formalism for representing structural knowledge) to that provided by Ray McAleese in this issue of ALT-J (auto-monitoring). We begin with an overview of concept maps as a means of describing a learner's knowledge constructs, and then discuss a broader class of tools, Mindtools, of which concept maps are a member. We proceed by defining Mindtools as formalisms for representing knowledge, and further elaborate on concept maps as a formalism for representing a particular kind of knowledge: structural knowledge. We then address McAleese's use of the term auto-monitoring and some of the steps in his model of concept maps. Finally, we describe some limitations of concept mapping as a formalism and as a cognitive learning strategy

Overview on agent-based social modelling and the use of formal languages

Transdisciplinary Models and Applications investigates a variety of programming languages used in validating and verifying models in order to assist in their eventual implementation. This book will explore different methods of evaluating and formalizing simulation models, enabling computer and industrial engineers, mathematicians, and students working with computer simulations to thoroughly understand the progression from simulation to product, improving the overall effectiveness of modeling systems.Postprint (author's final draft

Mapping AADL models to a repository of multiple schedulability analysis techniques

To fill the gap between the modeling of real-time systems and the scheduling analysis, we propose a framework that supports seamlessly the two aspects: 1) modeling a system using a methodology, in our case study, the Architecture Analysis and Design Language (AADL), and 2) helping to easily check temporal requirements (schedulability analysis, worst-case response time, sensitivity analysis, etc.). We introduce an intermediate framework called MoSaRT, which supports a rich semantic concerning temporal analysis. We show with a case study how the input model is transformed into a MoSaRT model, and how our framework is able to generate the proper models as inputs to several classic temporal analysis tools