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Logic Programming with Post-Quantum Cryptographic Primitives for Smart Contract on Quantum-Secured Blockchain [PDF]
Entropy, 2021 This paper investigates the usage of logic and logic programming in the design of smart contracts. Our starting point is the logic-based programming language for smart contracts used in a recently proposed framework of quantum-secured blockchain, called ...
Xin Sun, Piotr Kulicki, Mirek Sopek
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Design and Validation of an Augmented Reality Teaching System for Primary Logic Programming Education [PDF]
Sensors, 2022 Programming is a skill that requires high levels of logical thinking and problem-solving abilities. According to the Curriculum Guidelines for the 12-Year Basic Education currently implemented in Taiwan, programming has been included in the mandatory ...
Chi-Yi Tsai, Yu-Cheng Lai
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Logic and programming languages [PDF]
Communications of the ACM, 1977 Logic has been long interested in whether answers to certain questions are computable in principle, since the outcome puts bounds on the possibilities of formalization. More recently, precise comparisons in the efficiency of decision methods have become available through the developments in complexity theory.
Dana Scott
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DeepStochLog: Neural Stochastic Logic Programming [PDF]
AAAI Conference on Artificial Intelligence, 2021 Recent advances in neural-symbolic learning, such as DeepProbLog, extend probabilistic logic programs with neural predicates. Like graphical models, these probabilistic logic programs define a probability distribution over possible worlds, for which ...
Thomas Winters +3 more
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Inductive logic programming at 30 [PDF]
Machine-mediated learning, 2021 Inductive logic programming (ILP) is a form of logic-based machine learning. The goal is to induce a hypothesis (a logic program) that generalises given training examples and background knowledge.
Andrew Cropper +3 more
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Coalgebraic Semantics for Probabilistic Logic Programming [PDF]
Logical Methods in Computer Science, 2021 Probabilistic logic programming is increasingly important in artificial intelligence and related fields as a formalism to reason about uncertainty. It generalises logic programming with the possibility of annotating clauses with probabilities. This paper
Tao Gu, Fabio Zanasi
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Turning 30: New Ideas in Inductive Logic Programming [PDF]
International Joint Conference on Artificial Intelligence, 2020 Common criticisms of state-of-the-art machine learning include poor generalisation, a lack of interpretability, and a need for large amounts of training data.
Andrew Cropper +2 more
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Inductive logic programming at 30: a new introduction [PDF]
Journal of Artificial Intelligence Research, 2020 Inductive logic programming (ILP) is a form of machine learning. The goal of ILP is to induce a hypothesis (a set of logical rules) that generalises training examples. As ILP turns 30, we provide a new introduction to the field.
Andrew Cropper, Sebastijan Dumancic
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Logic and logic programming [PDF]
Communications of the ACM, 1992 John A. Robinson
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Programming in logic without logic programming [PDF]
Theory and Practice of Logic Programming, 2016 AbstractIn previous work, we proposed a logic-based framework in which computation is the execution of actions in an attempt to make reactive rules of the formif antecedent then consequenttrue in a canonical model of a logic program determined by an initial state, sequence of events, and the resulting sequence of subsequent states.
KOWALSKI, R, SADRI, F
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