22 papers · ranked by Valyu relevance
Paul Brunet
The formal analysis of automated systems is an important and growing industry. This activity routinely requires new verification frameworks to be developed to tackle new programming features, or new considerations (bugs of interest). Often, one particular property can prove frustrating to establish: completeness of the…
Arnaud Plagnol
Underlying the theory of inferences, a primary task of logic is language analysis. Such a task can be understood as depending on a general theory of representation, taking as a starting point the idea that some entities (« representations ») can present some entites (« contents »). We outline a theory of representation…
Vaishak Belle
In this paper, our aim is to briefly survey and articulate the logical and philosophical foundations of using (first-order ) logic to represent (probabilistic) knowledge in a non-technical fashion. Our motivation is three fold. First, for machine learning researchers unaware of why the research community cares about…
Hope Kean, Alexander Fung, Paris Jaggers, Jason Chen + 6 more
Humans are endowed with a powerful capacity for both inductive and deductive logical thought: we easily form generalizations based on a few examples and draw conclusions from known premises. Humans also arguably have the most sophisticated communication system in the animal kingdom: natural language allows us to…
Ida Szubert, Omri Abend, Nathan Schneider, Samuel Gibbon + 3 more
'Louis Mahon' 'Sharon Goldwater' 'Mark Steedman'] Corpora of child speech and child-directed speech (CDS) have enabled major contributions to the study of child language acquisition, yet semantic annotation for such corpora is still scarce and lacks a uniform standard. Semantic annotation of CDS is particularly…
Lars Vogt
This manuscript introduces the Semantic Units Framework, a technology-agnostic representational approach to semantic modularization in which statements and compound meaning structures are treated as first-class semantic units with explicit boundaries, identity, and epistemic status. Motivated by recurring limitations…
Parisa Kordjamshidi, Dan Roth, Kristian Kersting
Data-driven approaches are becoming increasingly common as problem-solving tools in many areas of science and technology. In most cases, machine learning models are the key component of these solutions. Often, a solution involves multiple learning models, along with significant levels of reasoning with the models'…
Simon Odense, Artur S. d’Avila Garcez
The field of neuro-symbolic AI aims to benefit from the combination of neural networks and symbolic systems. A cornerstone of the field is the translation or encoding of symbolic knowledge into neural networks. Although many neuro-symbolic methods and approaches have been proposed, and with a large increase in recent…
Hope Kean, Alexander Fung, Paris Jaggers, Jason Chen + 6 more
Humans are endowed with a powerful capacity for both inductive and deductive logical thought: we easily form generalizations based on a few examples and draw conclusions from known premises. Humans also arguably have the most sophisticated communication system in the animal kingdom: natural language allows us to…
Adam Pease, Richard Thompson
Human language is often vague and ambiguous. There have been many efforts to create formal languages and many attempts to translate human language into formal languages. Logic has a great deal of flexibility, not least in how the symbols used are defined. We anchor lexical elements in a formal ontology, which helps…
Prakash Mondal
Fundamental tensions exist between formal-logical approaches and cognitive approaches to linguistic meaning. The divergence arises from the fundamental differences in nature and form between formal/mathematical structures of natural language meaning and their cognitive representations. While the former are abstract and…
Pedro Domingos
Progress in AI is hindered by the lack of a programming language with all the requisite features. Libraries like PyTorch and TensorFlow provide automatic differentiation and efficient GPU implementation, but are additions to Python, which was never intended for AI. Their lack of support for automated reasoning and…
Violetta Molokopoy, Amedeo D’Angiulli, Tomaso Vecchi
This article will explore the expressivity and tractability of vividness, as viewed from the interdisciplinary perspective of the cognitive sciences, including the sub-disciplines of artificial intelligence, cognitive psychology, neuroscience, and phenomenology. Following the precursor work by Benussi in experimental…
Authors not listed
Step-by-step thinking is essential in all domains of chemical sciences and engineering. While machine learning tools are broadly used, algorithms that automate reasoning are far less common. We elaborate on seven categories of human reasoning activities and connect each to applications in chemical science and…
Xin Zhang, Victor S. Sheng
Explainability is an essential reason limiting the application of neural networks in many vital fields. Although neuro-symbolic AI hopes to enhance the overall explainability by leveraging the transparency of symbolic learning, the results are less evident than imagined. This article proposes a classification for…
Tirtharaj Dash, Sharad Chitlangia, Aditya Ahuja, Ashwin Srinivasan
We present a survey of ways in which existing scientific knowledge are included when constructing models with neural networks. The inclusion of domain-knowledge is of special interest not just to constructing scientific assistants, but also, many other areas that involve understanding data using human-machine…
Yuxiang Yao, Dong Liu, Zheting Zhang, Chengchen Zhao + 1 more
Complex biosystems exhibit ordered, functional, self-organized features. How-ever, a universal framework for exploring their logical paradigms and dynamic characteristics remains lacking. Here we describe BioLogical, a user-friendly R package, designed for analyzing properties of biosystems. We demonstrate its…
Difei Tang, Natasa Miskov-Zivanov
In computational modeling, Bounded Linear Temporal Logic (BLTL) is a valuable formalism for describing and verifying the temporal behavior of biological systems. However, translating natural language (NL) descriptions of system behaviors into accurate BLTL properties remains a labor-intensive task, requiring deep…
Giuseppe Marra, Sebastijan Dumančić, Robin Manhaeve, Luc De Raedt
This survey explores the integration of learning and reasoning in two different fields of artificial intelligence: neural-symbolic computation (NeSy) and statistical relational artificial intelligence (StarAI). NeSy aims to integrate symbolic reasoning and neural networks while StarAI focuses on integrating logic with…
Luna Xingyu Li, Carissa Bleker, Sylvain Soliman, Laurence Calzone + 13 more
Logical models are widely used to study regulatory and signaling systems, yet their reuse, annotation, and exchange across tools remain challenging. Although SBML Level 3 Qualitative Models (SBML-qual) provides a standard representation, its XML-based syntax is difficult to inspect and edit directly. Here we introduce…
Authors not listed
In the real world, many reversal phenomena occur—for example, cases in which a statement once regarded as false is later recognized as true. Upside-Down Logic is a framework designed to formalize such reversal phenomena as a logical system. It inverts the truth and falsity of propositions through contextual…
Authors not listed
Machine learning is increasingly used to predict reaction properties such as barrier heights, reaction energies, rates, or yields, as well as the underlying molecular geometries, including transition state structures. While such predictions have the potential to provide mechanistic insight for high-impact applications…