Results 101 to 110 of about 38,933 (219)
First-order logic automated theorem proving is an important research branch in the field of artificial intelligence, and the clause selection strategy plays an important role in improving the capability of theorem proving.
郭海林(GUO Hailin) +4 more
doaj +1 more source
Do robots boost productivity? A quantitative meta‐study
ABSTRACT This meta‐study analyzes the productivity effects of industrial robots. More than 1800 estimates from 85 primary studies are collected. The meta‐analytic evidence suggests that robotization has so far provided, at best, a small boost to productivity. There is strong evidence of publication bias in the positive direction.
Florian Schneider
wiley +1 more source
The Impact of TikTok on Elections: (Mis)information and Regulatory Challenges
ABSTRACT TikTok's algorithm‐driven feed is reshaping electoral communication, yet a clear understanding of its effects is lacking. This study synthesizes and appraises evidence on how the platform's design and governance shape political (dis)information and may affect electoral dynamics.
Michele Giuseppe Giuranno +1 more
wiley +1 more source
Equilibrium Reward for Liquidity Providers in Automated Market Makers
ABSTRACT We find the equilibrium contract that an automated market maker (AMM) offers to their strategic liquidity providers (LPs) in order to maximize the order flow that gets processed by the venue. Our model is formulated as a leader–follower stochastic game, where the venue is the leader and a representative LP is the follower.
Alif Aqsha +2 more
wiley +1 more source
Staying Offline or Going Online? Managing the Establishment of Service Platforms
ABSTRACT We study a global game in which consumers and sellers decide whether to join a service platform and interact more efficiently online. Uncertainties about the platform's technology value and users' participation behavior on both market sides cause a coordination problem.
Marit Holler +2 more
wiley +1 more source
Personalized Differential Privacy for Ridge Regression Under Output Perturbation
ABSTRACT The increased application of machine learning (ML) in sensitive domains requires protecting the training data through privacy frameworks, such as differential privacy (DP). Traditional DP enforces a uniform privacy level ε$$ \varepsilon $$, which bounds the maximum privacy loss that each data point in the dataset is allowed to incur.
Krishna Acharya +3 more
wiley +1 more source
Creating learning models that can exhibit sophisticated reasoning abilities is one of the greatest challenges in deep learning research, and mathematics is rapidly becoming one of the target domains for assessing scientific progress in this direction. In
Alberto Testolin
doaj +1 more source
Finding Minimum‐Cost Explanations for Predictions Made by Tree Ensembles
ABSTRACT The ability to reliably explain why a machine learning model arrives at a particular prediction is crucial when used as decision support by human operators of critical systems. The provided explanations must be provably correct, and preferably without redundant information, called minimal explanations.
John Törnblom +2 more
wiley +1 more source
A Resource Efficient Ising Model‐Based Quantum Sudoku Solver
ABSTRACT Background Quantum algorithms exploit superposition and parallelism to address complex combinatorial problems, many of which fall into the non‐polynomial (NP) class. Sudoku, a widely known logic‐based puzzle, is proven to be NP‐complete and thus presents a suitable testbed for exploring quantum optimization approaches.
Wen‐Li Wang +5 more
wiley +1 more source
Δ1: An Automated Theorem Generator
This paper introduces Δ1, a novel automated theorem generator for propositional and first-order logic that operates without a traditional built-in theorem prover.
Yang Xu +3 more
doaj +1 more source

