Linear logic for non-linear storytelling

Abstract. Whilst narrative representations have played a prominent role in AI research, there has been a renewed interest in the topic with the development of interactive narratives. A typical approach aims at generating narratives from baseline action representations, most often using planning techniques. However, this research has developed empirically, often as an application of planning. In this paper, we explore a more rigorous formalisation of narrative concepts, both at the action level and at the plot level. Our aim is to investigate how to bridge the gap between action descriptions and narrative concepts, by considering the latter from the perspective of resource consumption and causality. We propose to use Linear Logic, often introduced as a logic of resources, for it provides, through linear implication, a better description of causality than in Classical and Intuitionistic Logic. Besides advances in the fundamental principles of narrative formalisation, this approach can support the formal validation of scenario description as a preliminary step to their implementation via other computational formalisms.

Let's plan it deductively!

AbstractThe paper describes a transition logic, TL, and a deductive formalism for it. It shows how various important aspects (such as ramification, qualification, specificity, simultaneity, indeterminism etc.) involved in planning (or in reasoning about action and causality for that matter) can be modelled in TL in a rather natural way. (The deductive formalism for) TL extends the linear connection method proposed earlier by the author by embedding the latter into classical logic, so that classical and resource-sensitive reasoning coexist within TL. The attraction of a logical and deductive approach to planning is emphasized and the state of automated deduction briefly described

Conceptual and Mathematical Models, Methods, and Technologies for the Study of the Digital Transformation of Economic and Social Systems: A Literature Review and Research Agenda (Part II)

The results of the review of the subject field on the research of the digital transformation of economic and social systems is carried out (part II). We reviewed main theoretical, mathematical tools which could allow solving unsolved problems. The review of such main theoretical tools that can become the basis for developing the “activity paradigm” of research is carried out

Learning non-monotonic Logic Programs to Reason about Actions and Change

[Resumen] El objetivo de esta tesis es el diseño de métodos de aprendizaje automático capaces de encontrar un modelo de un sistema dinámico que determina cómo las propiedades del sistema con afectadas por la ejecución de acciones, Esto permite obtener de manera automática el conocimiento específico del dominio necesario para las tareas de planficación o diagnóstico así como predecir el comportamiento futuro del sistema. La aproximación seguida difiere de las aproximaciones previas en dos aspectos. Primero, el uso de formalismos no monótonos para el razonamiento sobre acciones y el cambio con respecto a los clásicos operadores tipo STRIPS o aquellos basados en formalismos especializados en tareas muy concretas, y por otro lado el uso de métodos de aprendizaje de programas lógicos (Inductive Logic Programming). La combinación de estos dos campos permite obtener un marco declarativo para el aprendizaje, donde la especificación de las acciones y sus efectos es muy intuitiva y natural y que permite aprender teorías más expresivas que en anteriores aproximaciones

Legal Rules, Legal Reasoning, and Nonmonotonic Logic

This dissertation develops, justifies, and examines the jurisprudential implications of a non-monotonic theory of common law legal reasoning. Legal rules seem to have exceptions but identifying all of them is difficult. This hinders attempts to formalize legal rules using classical logics. Non-monotonic logics allow defeasible inference, permitting rules that hold generally but can be defeated in the presence of exceptions. This ameliorates the problem of characterizing all exceptions to a rule, because exceptions can be added piecemeal while the rule remains. The first portion of the dissertation rebuts a prominent criticism leveled at a large class of theories of legal reasoning that includes my theory. The charge is that no coherent theory can recognize both (i) the difference between distinguishing and overruling, and (ii) the constraint of precedent. The critics argue that (ii) is more important than (i) and that (ii) can only be explained by monotonic legal rules. Drawing on cognitive psychology as well as legal theory, I show that coherent theories, such as my own, can accommodate both (i) and (ii). The second chapter provides motivation for understanding precedential constraint in terms of non-monotonic default rules and introduces my positive theory, which elaborates on John Horty's work in treating legal rules as prioritized defaults involving reasons. I motivate and implement a relaxation of Horty's restrictions on the form of rules to allow a more fine-grained characterization of precedent. Finally, I explore the relationship between these relaxations and the concept of precedent. The final section explains how my theory fits into the traditional jurisprudential ecosystem. I demonstrate that, contrary to assertions in the legal theory literature, this non-monotonic approach is entirely compatible with positivism's commitment to extracting rules from authoritative legal sources, namely court opinions. I also suggest how it might be attractive to law and economics theorists, pragmatists, and followers of Dworkin.PhDPhilosophyUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/110363/1/arigoni_1.pd