A Boxology of Design Patterns for Hybrid Learning and Reasoning Systems

We propose a set of compositional design patterns to describe a large variety of systems that combine statistical techniques from machine learning with symbolic techniques from knowledge representation. As in other areas of computer science (knowledge engineering, software engineering, ontology engineering, process mining and others), such design patterns help to systematize the literature, clarify which combinations of techniques serve which purposes, and encourage re-use of software components. We have validated our set of compositional design patterns against a large body of recent literature.Comment: 12 pages,55 reference

Fuzzy Logic and Its Uses in Finance: A Systematic Review Exploring Its Potential to Deal with Banking Crises

The major success of fuzzy logic in the field of remote control opened the door to its application in many other fields, including finance. However, there has not been an updated and comprehensive literature review on the uses of fuzzy logic in the financial field. For that reason, this study attempts to critically examine fuzzy logic as an effective, useful method to be applied to financial research and, particularly, to the management of banking crises. The data sources were Web of Science and Scopus, followed by an assessment of the records according to pre-established criteria and an arrangement of the information in two main axes: financial markets and corporate finance. A major finding of this analysis is that fuzzy logic has not yet been used to address banking crises or as an alternative to ensure the resolvability of banks while minimizing the impact on the real economy. Therefore, we consider this article relevant for supervisory and regulatory bodies, as well as for banks and academic researchers, since it opens the door to several new research axes on banking crisis analyses using artificial intelligence techniques

LOGICSEG: Parsing Visual Semantics with Neural Logic Learning and Reasoning

Current high-performance semantic segmentation models are purely data-driven sub-symbolic approaches and blind to the structured nature of the visual world. This is in stark contrast to human cognition which abstracts visual perceptions at multiple levels and conducts symbolic reasoning with such structured abstraction. To fill these fundamental gaps, we devise LOGICSEG, a holistic visual semantic parser that integrates neural inductive learning and logic reasoning with both rich data and symbolic knowledge. In particular, the semantic concepts of interest are structured as a hierarchy, from which a set of constraints are derived for describing the symbolic relations and formalized as first-order logic rules. After fuzzy logic-based continuous relaxation, logical formulae are grounded onto data and neural computational graphs, hence enabling logic-induced network training. During inference, logical constraints are packaged into an iterative process and injected into the network in a form of several matrix multiplications, so as to achieve hierarchy-coherent prediction with logic reasoning. These designs together make LOGICSEG a general and compact neural-logic machine that is readily integrated into existing segmentation models. Extensive experiments over four datasets with various segmentation models and backbones verify the effectiveness and generality of LOGICSEG. We believe this study opens a new avenue for visual semantic parsing.Comment: ICCV 2023 (Oral). Code: https://github.com/lingorX/LogicSeg

Информационная поддержка анализа навыков и умений контингента студентов высшего учебного заведения

In the below article, the application of the fuzzy logical conclusion method is considered as decision-maker in the process of analyzing the students skills and abilities based on the requirements of potential employers, in order to reduce the time of the first interview for potential candidates on a vacant position. When analyzing the results of the assessment of the competence of university students, a certain degree of fuzziness arises. In modern practice, fuzzy logic is used in many different assessment methods, including questioning, interviewing, testing, descriptive method, classification method, pairwise comparison, rating method, business games competence models, and the like. Each of the methods has its advantages and disadvantages, but they are effective only as part of a unified personnel management system. As a method for implementing a systematic approach to the assessment of the contingent of students, it is proposed to use fuzzy logic, a mathematical apparatus that allows you to build a model of an object based on fuzzy judgments. The use of fuzzy logic, the mathematical apparatus of which allows you to build a model of the object, based on fuzzy reasoning and rules. The most important condition for creating such a model is to translate the fuzzy, qualitative assessments used by man into the language of mathematics, which will be understood by the computer. The most used are fuzzy inferences using the Mamdani and Sugeno methods. In a fuzzy inference of the Mamdani type, the value of the output variable is given by fuzzy terms, in the conclusion of the Sugeno type, as a linear combination of the input variables. Research in the field of application of fuzzy logic in socio-economic systems suggests that it can be used to assess the competencies of university students.В данной работе рассмотрено использование метода нечеткого логического вывода для поддержки принятия решения в задачах анализа навыков и умений контингента студентов исходя из требований потенциальных работодателей, с целью уменьшения времени на первичную обработку информации касательно потенциальных кандидатов на вакантную должность. При анализе результатов оценки компетентности студентов вузов возникает определенная степень нечеткости. В современной практике нечеткая логика применяется во многих различных методах оценки, в том числе анкетирование, интервью, тестирование, описательный метод, метод классификации, парное сравнение, рейтинговый метод, деловые игры модели компетентности и тому подобное. Каждый из методов имеет свои преимущества и недостатки, но эффективны они только в составе единой системы управления персоналом. Как метод для реализации системного подхода к оценке контингента студентов предложено использовать нечеткую логику, математический аппарат, который позволяет построить модель объекта, основанную на нечетких суждениях. Использование нечеткой логики, математический аппарат которой позволяет построить модель объекта, основываясь на нечетких рассуждениях и правилах. Важнейшее условие создания такой модели заключается в том, чтобы перевести нечеткие, качественные оценки, применяемые человеком, на язык математики, которая будет понятна вычислительной машине. Наиболее используемыми являются нечеткие выводы с помощью способов Мамдани и Сугено. В нечетком выводе типа Мамдани значение выходной переменной задаются нечеткими термами, в заключении типа Сугено – как линейная комбинация входных переменных. Исследования в области применения нечеткой логики в социоэкономических системах позволяют говорить о возможности ее использования для оценки компетенций студентов вузов

Modular Design Patterns for Hybrid Learning and Reasoning Systems: a taxonomy, patterns and use cases

The unification of statistical (data-driven) and symbolic (knowledge-driven) methods is widely recognised as one of the key challenges of modern AI. Recent years have seen large number of publications on such hybrid neuro-symbolic AI systems. That rapidly growing literature is highly diverse and mostly empirical, and is lacking a unifying view of the large variety of these hybrid systems. In this paper we analyse a large body of recent literature and we propose a set of modular design patterns for such hybrid, neuro-symbolic systems. We are able to describe the architecture of a very large number of hybrid systems by composing only a small set of elementary patterns as building blocks. The main contributions of this paper are: 1) a taxonomically organised vocabulary to describe both processes and data structures used in hybrid systems; 2) a set of 15+ design patterns for hybrid AI systems, organised in a set of elementary patterns and a set of compositional patterns; 3) an application of these design patterns in two realistic use-cases for hybrid AI systems. Our patterns reveal similarities between systems that were not recognised until now. Finally, our design patterns extend and refine Kautz' earlier attempt at categorising neuro-symbolic architectures.Comment: 20 pages, 22 figures, accepted for publication in the International Journal of Applied Intelligenc

Bounded Rationality and Heuristics in Humans and in Artificial Cognitive Systems

In this paper I will present an analysis of the impact that the notion of “bounded rationality”, introduced by Herbert Simon in his book “Administrative Behavior”, produced in the field of Artificial Intelligence (AI). In particular, by focusing on the field of Automated Decision Making (ADM), I will show how the introduction of the cognitive dimension into the study of choice of a rational (natural) agent, indirectly determined - in the AI field - the development of a line of research aiming at the realisation of artificial systems whose decisions are based on the adoption of powerful shortcut strategies (known as heuristics) based on “satisficing” - i.e. non optimal - solutions to problem solving. I will show how the “heuristic approach” to problem solving allowed, in AI, to face problems of combinatorial complexity in real-life situations and still represents an important strategy for the design and implementation of intelligent systems