Zobrazeno 1 - 10
of 1 426
pro vyhledávání: '"I.2.3"'
Autor:
Bogaerts, Bart, Charalambidis, Angelos, Chatziagapis, Giannos, Kostopoulos, Babis, Pollaci, Samuele, Rondogiannis, Panos
We propose a stable model semantics for higher-order logic programs. Our semantics is developed using Approximation Fixpoint Theory (AFT), a powerful formalism that has successfully been used to give meaning to diverse non-monotonic formalisms. The p
Externí odkaz:
http://arxiv.org/abs/2408.10563
Graph neural networks (GNNs) are frequently used to predict missing facts in knowledge graphs (KGs). Motivated by the lack of explainability for the outputs of these models, recent work has aimed to explain their predictions using Datalog, a widely u
Externí odkaz:
http://arxiv.org/abs/2408.10261
Autor:
Dean, Walter, Naibo, Alberto
This paper explores the relationship of artificial intelligence to the task of resolving open questions in mathematics. We first present an updated version of a traditional argument that limitative results from computability and complexity theory sho
Externí odkaz:
http://arxiv.org/abs/2408.03345
In the context of satellite monitoring of the earth, we can assume that the surface of the earth is divided into a set of regions. We assume that the impact of a big social/environmental event spills into neighboring regions. Using Identifying Code S
Externí odkaz:
http://arxiv.org/abs/2407.14120
Autor:
van Brügge, Jan
Formal reasoning about the time complexity of algorithms and data structures is usually done in interactive theorem provers like Isabelle/HOL. This includes reasoning about amortized time complexity which looks at the worst case performance over a se
Externí odkaz:
http://arxiv.org/abs/2407.13671
A framework with sets of attacking arguments (SETAF) is an extension of the well-known Dung's Abstract Argumentation Frameworks (AAFs) that allows joint attacks on arguments. In this paper, we provide a translation from Normal Logic Programs (NLPs) t
Externí odkaz:
http://arxiv.org/abs/2407.05538
Prior research has enhanced the ability of Large Language Models (LLMs) to solve logic puzzles using techniques such as chain-of-thought prompting or introducing a symbolic representation. These frameworks are still usually insufficient to solve comp
Externí odkaz:
http://arxiv.org/abs/2407.03956
Reinforcement Learning (RL) has gained significant attention across various domains. However, the increasing complexity of RL programs presents testing challenges, particularly the oracle problem: defining the correctness of the RL program. Conventio
Externí odkaz:
http://arxiv.org/abs/2406.19812
Solving Olympiad-level mathematical problems represents a significant advancement in machine intelligence and automated reasoning. Current machine learning methods, however, struggle to solve Olympiad-level problems beyond Euclidean plane geometry du
Externí odkaz:
http://arxiv.org/abs/2406.14219
Autor:
Saki, Amir, Faghihi, Usef
In this paper, we generalize the Pearl and Neyman-Rubin methodologies in causal inference by introducing a generalized approach that incorporates fuzzy logic. Indeed, we introduce a fuzzy causal inference approach that consider both the vagueness and
Externí odkaz:
http://arxiv.org/abs/2406.13731