Special issue on logics and artificial intelligence

There is a significant range of ongoing challenges in artificial intelligence (AI) dealing with reasoning, planning, learning, perception and cognition, among others. In this scenario, many-valued logics emerge as one of the topics in many of the solutions to some of those AI problems. This special issue presents a brief introduction to the relation between logics and AI and collects recent research works on logic-based approaches in AI

On the Rationality of Explanations in Classification Algorithms

This paper is a first step towards studying the rationality of explanations produced by up-to-date AI systems. Based on the thesis that designing rational explanations for accomplishing trustworthy AI is fundamental for ethics in AI, we study the rationality criteria that explanations in classification algorithms have to meet. In this way, we identify, define, and exemplify characteristic criteria of rational explanations in classification algorithms

Perspectives innovadores en el disseny d'algoritmes intel·ligents per a la classificació d'obres d'art : eines per a una millor integració humà-màquina

La present investigació explora una perspectiva innovadora en el disseny d'algoritmes capaços de classificar pintures segons el seu estil artístic. Evitant alguns inconvenients d'integració humà-màquina que presenta el disseny amb aprenentatge automàtic, ℓ-SHE és un algoritme basat en sistemes de representació lògica i conceptes qualitatius que, a més de reconèixer i classificar amb èxit l'estil artístic d'una pintura, treballa amb informació semàntica, genera explicacions del perquè dels seus resultats, i pot ser entrenat fàcilment i amb bancs de dades relativament petits.La presente investigación explora una perspectiva innovadora en el diseño de algoritmos capaces de clasificar pinturas según su estilo artístico. Evitando algunos inconvenientes de integración humano-máquina que presenta el diseño con aprendizaje automático, ℓ-SHE es un algoritmo basado en sistemas de representación lógica y conceptos cualitativos que, además de reconocer y clasificar con éxito el estilo artístico de una pintura, trabaja con información semántica, genera explicaciones del por qué de sus resultados, y puede ser entrenado fácilmente y con bancos de datos relativamente pequeños.The present research explores an innovative perspective in the design of algorithms for classifying pictorial artworks according to their art style. Avoiding some of the human-machine integration issues associated with machine learning algorithm design, ℓ-SHE is an algorithm based on logical representation and qualitative concepts which in addition to successfully recognizing and classifying the art style of paintings, it works with semantic information, generates explanations of which reasonings have been followed for the classification results, and can easily be trained using relatively small databases

Deep learning approaches to pattern extraction and recognition in paintings and drawings: an overview

This paper provides an overview of some of the most relevant deep learning approaches to pattern extraction and recognition in visual arts, particularly painting and drawing. Recent advances in deep learning and computer vision, coupled with the growing availability of large digitized visual art collections, have opened new opportunities for computer science researchers to assist the art community with automatic tools to analyse and further understand visual arts. Among other benefits, a deeper understanding of visual arts has the potential to make them more accessible to a wider population, ultimately supporting the spread of culture

Formal approaches to number in Slavic and beyond (Volume 5)

The goal of this collective monograph is to explore the relationship between the cognitive notion of number and various grammatical devices expressing this concept in natural language with a special focus on Slavic. The book aims at investigating different morphosyntactic and semantic categories including plurality and number-marking, individuation and countability, cumulativity, distributivity and collectivity, numerals, numeral modifiers and classifiers, as well as other quantifiers. It gathers 19 contributions tackling the main themes from different theoretical and methodological perspectives in order to contribute to our understanding of cross-linguistic patterns both in Slavic and non-Slavic languages

Significance in Language

This book offers a unique perspective on meaning in language, broadening the scope of existing understanding of meaning by introducing a comprehensive and cohesive account of meaning that draws on a wide range of linguistic approaches. The volume seeks to build up a complete picture of what meaning is, different types of meaning, and different ways of structuring the same meaning across myriad forms and varieties of language across such domains, such as everyday speech, advertising, humour, and academic writing. Supported by data from psycholinguistic and neurolinguistic research, the book combines different approaches from scholarship in semantics, including formalist, structuralist, cognitive, functionalist, and semiotics to demonstrate the ways in which meaning is expressed in words but also in word order and intonation. The book argues for a revised conceptualisation of meaning toward presenting a new perspective on semantics and its wider study in language and linguistic research. This book will appeal to scholars interested in meaning in language in such fields as linguistics, semantics, and semiotics