Introduction to the special issue on probability, logic and learning

Recently, the combination of probability, logic and learning has received considerable attention in the artificial intelligence and machine learning communities; see e.g. Getoor and Taskar (2007); De Raedt et al. (2008). Computational logic often plays a major role in these developments since it forms the theoretical backbone for much of the work in probabilistic programming and logical and relational learning. Contemporary work in this area is often application- and experiment-driven, but is also concerned with the theoretical foundations of formalisms and inference procedures and with advanced implementation technology that scales well

Towards Ideal Semantics for Analyzing Stream Reasoning

The rise of smart applications has drawn interest to logical reasoning over data streams. Recently, different query languages and stream processing/reasoning engines were proposed in different communities. However, due to a lack of theoretical foundations, the expressivity and semantics of these diverse approaches are given only informally. Towards clear specifications and means for analytic study, a formal framework is needed to define their semantics in precise terms. To this end, we present a first step towards an ideal semantics that allows for exact descriptions and comparisons of stream reasoning systems.Comment: International Workshop on Reactive Concepts in Knowledge Representation (ReactKnow 2014), co-located with the 21st European Conference on Artificial Intelligence (ECAI 2014). Proceedings of the International Workshop on Reactive Concepts in Knowledge Representation (ReactKnow 2014), pages 17-22, technical report, ISSN 1430-3701, Leipzig University, 2014. http://nbn-resolving.de/urn:nbn:de:bsz:15-qucosa-150562 2014,

Logics of preference when there is no best

Well-behaved preferences (e.g., total pre-orders) are a cornerstone of several areas in artificial intelligence, from knowledge representation, where preferences typically encode likelihood comparisons, to both game and decision theories, where preferences typically encode utility comparisons. Yet weaker (e.g., cyclical) structures of comparison have proven important in a number of areas, from argumentation theory to tournaments and social choice theory. In this paper we provide logical foundations for reasoning about this type of preference structures where no obvious best elements may exist. Concretely, we compare and axiomatize a number of ways in which the concepts of maximality and optimality can be lifted to this general class of preferences. In doing so we expand the scope of the long-standing tradition of the logical analysis of preference

FORMALITY AND REPRESENTATIONAL RELATIVISM: A CRITICAL PHILOSOPHICAL INVESTIGATION INTO KNOWLEDGE REPRESENTATION AS ONE TRANSFORMATION OF WESTERN PHILOSOPHY

This paper provides a philosophical discussion of Knowledge Representation [KR], which has become an influential interdisciplinary and technology friendly research field through Artificial Intelligence and Computer Science. While KR appears an increasingly fashionable and subsequently blurred term, it originally emerged out of genuine meta-theoretical considerations. Subsequently, the reconstruction of KR's formal, structural and functional foundations should call for further philosophical evaluation of KR's interdisciplinary and practical potential. The focus is put on KR's logical and semiotical roots, both methodologically and historically, whose exposure prove necessary for a proper understanding and possible criticism of KR's [technological] applicability. The stipulation of analytical symbol theory is new in this context, but nevertheless necessary, as only a more principal semiotic focus may allow an appropriate evaluation of symbolic intelligence, which has to be considered KR's essence

Prolegomena filozofijskog utemeljenja dubokog učenja kao teorije (umjetne) inteligencije

This paper examines the philosophical foundations of deep learning. By pointing to the beginnings of deep learning and artificial neuron as a logical model of a human neuron, it is possible to claim that artificial intelligence was developed even before its official creation and that it was strongly connected to propositional logic. Bearing in mind some major setbacks in the development of neural networks, we show that deep learning can be treated as the theory of artificial intelligence and that it falls under artificial intelligence paradigm by claiming that everything can be done with learning alone and that all intelligent behavior is learnable. Thus, deep learning is a philosophical or an epistemological approach in which a form of radical empiricism must be advocated. Therefore, there is nothing in the mind that was not in the senses, and there cannot be anything in the mind that is not learnable.U radu se ispituju filozofski temelji dubokog učenja. Ukazivanjem na početke dubokog učenja i umjetnog neurona kao formalnog modela ljudskog neurona moguće je tvrditi da je umjetna inteligencija razvijena i prije njezinog službenog imenovanja te da je bila snažno povezana s propozicionalnom logikom. Imajući na umu neke velike zastoje u razvoju neuronskih mreža, pokazujemo da se dubinsko učenje može tretirati kao teorija umjetne inteligencije te da potpada pod paradigmu umjetne inteligencije jer je za nju dovoljno samo učenje jer se inteligentno ponašanje uči. Dakle, duboko učenje je filozofski ili epistemološki pristup u kojem se mora zagovarati radikalni empirizam. Prema tome, ne samo da ne postoji ništa u umu što nije bilo u osjetilima, već u umu ne postoji ništa što se ne može naučiti

Mathematical Fuzzy Logic in the Emerging Fields of Engineering, Finance, and Computer Sciences

Mathematical fuzzy logic (MFL) specifically targets many-valued logic and has significantly contributed to the logical foundations of fuzzy set theory (FST). It explores the computational and philosophical rationale behind the uncertainty due to imprecision in the backdrop of traditional mathematical logic. Since uncertainty is present in almost every real-world application, it is essential to develop novel approaches and tools for efficient processing. This book is the collection of the publications in the Special Issue “Mathematical Fuzzy Logic in the Emerging Fields of Engineering, Finance, and Computer Sciences”, which aims to cover theoretical and practical aspects of MFL and FST. Specifically, this book addresses several problems, such as:- Industrial optimization problems- Multi-criteria decision-making- Financial forecasting problems- Image processing- Educational data mining- Explainable artificial intelligence, etc

Dependency Stochastic Boolean Satisfiability: A Logical Formalism for NEXPTIME Decision Problems with Uncertainty

Stochastic Boolean Satisfiability (SSAT) is a logical formalism to model decision problems with uncertainty, such as Partially Observable Markov Decision Process (POMDP) for verification of probabilistic systems. SSAT, however, is limited by its descriptive power within the PSPACE complexity class. More complex problems, such as the NEXPTIME-complete Decentralized POMDP (Dec-POMDP), cannot be succinctly encoded with SSAT. To provide a logical formalism of such problems, we extend the Dependency Quantified Boolean Formula (DQBF), a representative problem in the NEXPTIME-complete class, to its stochastic variant, named Dependency SSAT (DSSAT), and show that DSSAT is also NEXPTIME-complete. We demonstrate the potential applications of DSSAT to circuit synthesis of probabilistic and approximate design. Furthermore, to study the descriptive power of DSSAT, we establish a polynomial-time reduction from Dec-POMDP to DSSAT. With the theoretical foundations paved in this work, we hope to encourage the development of DSSAT solvers for potential broad applications.Comment: 10 pages, 5 figures. A condensed version of this work is published in the AAAI Conference on Artificial Intelligence (AAAI) 202

Dimensions of Neural-symbolic Integration - A Structured Survey

Research on integrated neural-symbolic systems has made significant progress in the recent past. In particular the understanding of ways to deal with symbolic knowledge within connectionist systems (also called artificial neural networks) has reached a critical mass which enables the community to strive for applicable implementations and use cases. Recent work has covered a great variety of logics used in artificial intelligence and provides a multitude of techniques for dealing with them within the context of artificial neural networks. We present a comprehensive survey of the field of neural-symbolic integration, including a new classification of system according to their architectures and abilities.Comment: 28 page

Philosophical foundations of the Death and Anti-Death discussion

Perhaps there has been no greater opportunity than in this “VOLUME FIFTEEN of our Death And Anti-Death set of anthologies” to write about how might think about life and how to avoid death. There are two reasons to discuss “life”, the first being enhancing our understanding of who we are and why we may be here in the Universe. The second is more practical: how humans meet the physical challenges brought about by the way they have interacted with their environment. Many persons discussing “life” beg the question about what “life” is. Surely, when one discusses how to overcome its opposite, death, they are not referring to another “living” thing such as a plant. There seems to be a commonality, though, and it is this commonality is one needing elaboration. It ostensibly seems to be the boundary condition separating what is completely passive (inert) from what attempts to maintain its integrity, as well as fulfilling other conditions we think “life” has. In our present discussion, there will be a reminder that it by no means has been unequivocally established what life really is by placing quotes around the word, namely, “life”. Consider it a tag representing a bundle of philosophical ideas that will be unpacked in this paper