Learning Lukasiewicz logic

The integration between connectionist learning and logic-based reasoning is a longstanding foundational question in artificial intelligence, cognitive systems, and computer science in general. Research into neural-symbolic integration aims to tackle this challenge, developing approaches bridging the gap between sub-symbolic and symbolic representation and computation. In this line of work the core method has been suggested as a way of translating logic programs into a multilayer perceptron computing least models of the programs. In particular, a variant of the core method for three valued Łukasiewicz logic has proven to be applicable to cognitive modelling among others in the context of Byrne’s suppression task. Building on the underlying formal results and the corresponding computational framework, the present article provides a modified core method suitable for the supervised learning of Łukasiewicz logic (and of a closely-related variant thereof), implements and executes the corresponding supervised learning with the backpropagation algorithm and, finally, constructs a rule extraction method in order to close the neural-symbolic cycle. The resulting system is then evaluated in several empirical test cases, and recommendations for future developments are derived

Introduction. The School: Its Genesis, Development and Significance

The Introduction outlines, in a concise way, the history of the Lvov-Warsaw School – a most unique Polish school of worldwide renown, which pioneered trends combining philosophy, logic, mathematics and language. The author accepts that the beginnings of the School fall on the year 1895, when its founder Kazimierz Twardowski, a disciple of Franz Brentano, came to Lvov on his mission to organize a scientific circle. Soon, among the characteristic features of the School was its serious approach towards philosophical studies and teaching of philosophy, dealing with philosophy and propagation of it as an intellectual and moral mission, passion for clarity and precision, as well as exchange of thoughts, and cooperation with representatives of other disciplines.The genesis is followed by a chronological presentation of the development of the School in the successive years. The author mentions all the key representatives of the School (among others, Ajdukiewicz, Lesniewski, Łukasiewicz,Tarski), accompanying the names with short descriptions of their achievements. The development of the School after Poland’s regaining independence in 1918 meant part of the members moving from Lvov to Warsaw, thus providing the other segment to the name – Warsaw School of Logic. The author dwells longer on the activity of the School during the Interwar period – the time of its greatest prosperity, which ended along with the outbreak of World War 2. Attempts made after the War to recreate the spirit of the School are also outlined and the names of followers are listed accordingly. The presentation ends with some concluding remarks on the contribution of the School to contemporary developments in the fields of philosophy, mathematical logic or computer science in Poland

Statistical relational learning with soft quantifiers

Quantification in statistical relational learning (SRL) is either existential or universal, however humans might be more inclined to express knowledge using soft quantifiers, such as ``most'' and ``a few''. In this paper, we define the syntax and semantics of PSL^Q, a new SRL framework that supports reasoning with soft quantifiers, and present its most probable explanation (MPE) inference algorithm. To the best of our knowledge, PSL^Q is the first SRL framework that combines soft quantifiers with first-order logic rules for modelling uncertain relational data. Our experimental results for link prediction in social trust networks demonstrate that the use of soft quantifiers not only allows for a natural and intuitive formulation of domain knowledge, but also improves the accuracy of inferred results

Foundations of Reasoning with Uncertainty via Real-valued Logics

Real-valued logics underlie an increasing number of neuro-symbolic approaches, though typically their logical inference capabilities are characterized only qualitatively. We provide foundations for establishing the correctness and power of such systems. For the first time, we give a sound and complete axiomatization for a broad class containing all the common real-valued logics. This axiomatization allows us to derive exactly what information can be inferred about the combinations of real values of a collection of formulas given information about the combinations of real values of several other collections of formulas. We then extend the axiomatization to deal with weighted subformulas. Finally, we give a decision procedure based on linear programming for deciding, under certain natural assumptions, whether a set of our sentences logically implies another of our sentences.Comment: 9 pages (incl. references), 9 pages supplementary. In submission to NeurIPS 202

Greek and Roman Logic

In ancient philosophy, there is no discipline called “logic” in the contemporary sense of “the study of formally valid arguments.” Rather, once a subfield of philosophy comes to be called “logic,” namely in Hellenistic philosophy, the field includes (among other things) epistemology, normative epistemology, philosophy of language, the theory of truth, and what we call logic today. This entry aims to examine ancient theorizing that makes contact with the contemporary conception. Thus, we will here emphasize the theories of the “syllogism” in the Aristotelian and Stoic traditions. However, because the context in which these theories were developed and discussed were deeply epistemological in nature, we will also include references to the areas of epistemological theorizing that bear directly on theories of the syllogism, particularly concerning “demonstration.” Similarly, we will include literature that discusses the principles governing logic and the components that make up arguments, which are topics that might now fall under the headings of philosophy of logic or non-classical logic. This includes discussions of problems and paradoxes that connect to contemporary logic and which historically spurred developments of logical method. For example, there is great interest among ancient philosophers in the question of whether all statements have truth-values. Relevant themes here include future contingents, paradoxes of vagueness, and semantic paradoxes like the liar. We also include discussion of the paradoxes of the infinite for similar reasons, since solutions have introduced sophisticated tools of logical analysis and there are a range of related, modern philosophical concerns about the application of some logical principles in infinite domains. Our criterion excludes, however, many of the themes that Hellenistic philosophers consider part of logic, in particular, it excludes epistemology and metaphysical questions about truth. Ancient philosophers do not write treatises “On Logic,” where the topic would be what today counts as logic. Instead, arguments and theories that count as “logic” by our criterion are found in a wide range of texts. For the most part, our entry follows chronology, tracing ancient logic from its beginnings to Late Antiquity. However, some themes are discussed in several eras of ancient logic; ancient logicians engage closely with each other’s views. Accordingly, relevant publications address several authors and periods in conjunction. These contributions are listed in three thematic sections at the end of our entry

An approach to supervised learning of three valued Lukasiewicz logic in Hölldobler's core method

The core method [6] provides a way of translating logic programs into a multilayer perceptron computing least models of the programs. In [7] , a variant of the core method for three valued Lukasiewicz logic and its applicability to cognitive modelling were introduced. Building on these results, the present paper provides a modified core suitable for supervised learning, implements and executes supervised learning with the backpropagation algorithm and, finally, constructs a rule extraction method in order to close the neural-symbolic cycle

Unifying Functional Interpretations: Past and Future

This article surveys work done in the last six years on the unification of various functional interpretations including G\"odel's dialectica interpretation, its Diller-Nahm variant, Kreisel modified realizability, Stein's family of functional interpretations, functional interpretations "with truth", and bounded functional interpretations. Our goal in the present paper is twofold: (1) to look back and single out the main lessons learnt so far, and (2) to look forward and list several open questions and possible directions for further research.Comment: 18 page

Soft quantification in statistical relational learning

We present a new statistical relational learning (SRL) framework that supports reasoning with soft quantifiers, such as "most" and "a few." We define the syntax and the semantics of this language, which we call , and present a most probable explanation inference algorithm for it. To the best of our knowledge, is the first SRL framework that combines soft quantifiers with first-order logic rules for modelling uncertain relational data. Our experimental results for two real-world applications, link prediction in social trust networks and user profiling in social networks, demonstrate that the use of soft quantifiers not only allows for a natural and intuitive formulation of domain knowledge, but also improves inference accuracy

Logika narodu: Nacjonalizm, logika formalna i międzywojenna Polska

Between the World Wars, a robust research community emerged in the nascent discipline of mathematical logic in Warsaw. Logic in Warsaw grew out of overlapping imperial legacies, launched mainly by Polish-speaking scholars who had trained in Habsburg universities and had come during the First World War to the University of Warsaw, an institution controlled until recently by Russia and reconstructed as Polish under the auspices of German occupation. The intellectuals who formed the Warsaw School of Logic embraced a patriotic Polish identity. Competitive nationalist attitudes were common among interwar scientists – a stance historians have called “Olympic internationalism,” in which nationalism and internationalism interacted as complementary rather than conflicting impulses.One of the School’s leaders, Jan Łukasiewicz, developed a system of notation that he promoted as a universal tool for logical research and communication. A number of his compatriots embraced it, but few logicians outside Poland did; Łukasiewicz’s notation thus inadvertently served as a distinctively national vehicle for his and his colleagues’ output. What he had intended as his most universally applicable invention became instead a respected but provincialized way of writing. Łukasiewicz’s system later spread in an unanticipated form, when postwar computer scientists found aspects of its design practical for working under the specific constraints of machinery; they developed a modified version for programming called “Reverse Polish Notation” (RPN). RPN attained a measure of international currency that Polish notation in logic never had, enjoying a global career in a different discipline outside its namesake country. The ways in which versions of the notation spread, and remained or did not remain “Polish” as they traveled, depended on how readers (whether in mathematical logic or computer science) chose to read it; the production of a nationalized science was inseparable from its international reception.W okresie międzywojennym w rodzącej się dyscyplinie logiki matematycznej w Warszawie wyłoniła się silna społeczność badawcza. Logika w Warszawie wyrosła w wyniku nakładających się na siebie imperialnych spuścizn, dzięki działaniom głównie polskojęzycznych uczonych, którzy kształcili się na uniwersytetach habsburskich i przybyli w czasie I wojny światowej na Uniwersytet Warszawski, instytucję kontrolowaną do niedawna przez Rosję i zrekonstruowaną jako polską pod auspicjami niemieckiego okupanta. Intelektualiści, którzy tworzyli Warszawską Szkołę Logiki, przyjęli patriotyczną polską tożsamość. Konkurencyjne postawy nacjonalistyczne były powszechne wśród naukowców międzywojennych – stanowisko, które historycy nazwali „internacjonalizmem olimpijskim”, w którym nacjonalizm i internacjonalizm oddziaływały jako impulsy raczej wzajemnie się uzupełniające niż sprzeczne.Jeden z liderów Szkoły, Jan Łukasiewicz, opracował system notacji, który promował jako uniwersalne narzędzie do badań i komunikacji w logice. Wielu jego rodaków przyjęło ten system notacji, ale niewielu logików poza Polską. W ten sposób notacja Łukasiewicza nieumyślnie posłużyła jemu i jego współpracownikom jako narzędzie specyficznie polskie. Wynalazek, który w zamyśle miał być najbardziej uniwersalną formą zapisu, stał się szanowanym, lecz zrozumiałym tylko w kraju narzędziem. System notacji Łukasiewicza później rozprzestrzenił się w nieprzewidzianej formie, gdy powojenni informatycy zdali sobie sprawę z praktycznej użyteczności jego aspektów do pracy w specyficznych uwarunkowaniach maszynowych i opracowali zmodyfikowaną wersję tej notacji do programowania o nazwie „Reverse Polish Notation” (RPN). RPN osiągnął miarę waluty międzynarodowej, której nigdy nie miała polska notacja w logice, ciesząc się globalną karierą w innej dyscyplinie poza krajem jej imiennika. Drogi, w jakich wersje tej notacji rozprzestrzeniły się i pozostały lub nie pozostały „polskie” podczas tej podróży, zależały od tego, jak czytelnicy (zajmujący się logiką matematyczną albo informatyką) postanowili czytać tę notację; tworzenie znacjonalizowanej nauki było nierozerwalnie związane z jej międzynarodową recepcją