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We consider the problem of deciding the satisfiability of quantifier-free formulas in the theory of finite sets with cardinality constraints. Sets are a common high-level data structure used in programming; thus, such a theory is useful for…

Logic in Computer Science · Computer Science 2023-06-22 Kshitij Bansal , Clark Barrett , Andrew Reynolds , Cesare Tinelli

We are born with the ability to learn concepts by comparing diverse observations. This helps us to understand the new world in a compositional manner and facilitates extrapolation, as objects naturally consist of multiple concepts. In this…

Machine Learning · Computer Science 2025-10-02 Yujia Zheng , Shaoan Xie , Kun Zhang

We consider modeling the conception of knowledge in terms of temporal logic. The study of knowledge logical operations is originated around 1962 by representation of knowledge and belief using modalities. Nowadays, it is very good…

Logic in Computer Science · Computer Science 2014-05-06 Vladimir Rybakov

Knowledge Representation is important issue in reinforcement learning. In this paper, we bridge the gap between reinforcement learning and knowledge representation, by providing a rich knowledge representation framework, based on normal…

Artificial Intelligence · Computer Science 2010-12-08 Emad Saad

Pre-trained Language Models (PLMs) are trained on vast unlabeled data, rich in world knowledge. This fact has sparked the interest of the community in quantifying the amount of factual knowledge present in PLMs, as this explains their…

Computation and Language · Computer Science 2023-12-06 Paul Youssef , Osman Alperen Koraş , Meijie Li , Jörg Schlötterer , Christin Seifert

We present a propositional logic %which can be used to reason about the uncertainty of events, where the uncertainty is modeled by a set of probability measures assigning an interval of probability to each event. We give a sound and…

Artificial Intelligence · Computer Science 2007-05-23 Joseph Y. Halpern , Riccardo Pucella

We present a propositional logic to reason about the uncertainty of events, where the uncertainty is modeled by a set of probability measures assigning an interval of probability to each event. We give a sound and complete axiomatization…

Artificial Intelligence · Computer Science 2014-08-08 Joseph Y. Halpern , Riccardo Pucella

Reasoning is a fundamental capability for harnessing valuable insight, knowledge and patterns from knowledge graphs. Existing work has primarily been focusing on point-wise reasoning, including search, link predication, entity prediction,…

Artificial Intelligence · Computer Science 2020-11-09 Lihui Liu , Boxin Du , Heng Ji , Hanghang Tong

In the problem of learning with label proportions, which we call LLP learning, the training data is unlabeled, and only the proportions of examples receiving each label are given. The goal is to learn a hypothesis that predicts the…

Machine Learning · Computer Science 2020-04-08 Benjamin Fish , Lev Reyzin

We define several canonical problems related to contrastive explanations, each answering a question of the form ''Why P but not Q?''. The problems compute causes for both P and Q, explicitly comparing their differences. We investigate the…

Artificial Intelligence · Computer Science 2025-07-14 Tobias Geibinger , Reijo Jaakkola , Antti Kuusisto , Xinghan Liu , Miikka Vilander

Rather than discussing the isolated merits of a nominative theory of uncertainty, this paper focuses on a class of problems, referred to as Dynamic Classification Problem (DCP), which requires the integration of many theories, including a…

Artificial Intelligence · Computer Science 2013-04-08 Piero P. Bonissone

Recent work has shown success in incorporating pre-trained models like BERT to improve NLP systems. However, existing pre-trained models lack of causal knowledge which prevents today's NLP systems from thinking like humans. In this paper,…

Computation and Language · Computer Science 2021-08-10 Zhongyang Li , Xiao Ding , Kuo Liao , Bing Qin , Ting Liu

We revisit Popper's falsifiability criterion. A tester hires a potential expert to produce a theory, offering payments contingent on the observed performance of the theory. In our model, instead of knowing the true data-generating process,…

Theoretical Economics · Economics 2026-01-06 Mark Whitmeyer , Kun Zhang

A proof is one of the most important concepts of mathematics. However, there is a striking difference between how a proof is defined in theory and how it is used in practice. This puts the unique status of mathematics as exact science into…

"Clarithmetic" is a generic name for formal number theories similar to Peano arithmetic, but based on computability logic (see http://www.cis.upenn.edu/~giorgi/cl.html) instead of the more traditional classical or intuitionistic logics.…

Logic in Computer Science · Computer Science 2011-08-24 Giorgi Japaridze

This paper establishes the separation of complexity classes $\mathbf{P}$ and $\mathbf{NP}$ through a novel homological algebraic approach grounded in category theory. We construct the computational category $\mathbf{Comp}$, embedding…

Computational Complexity · Computer Science 2025-12-22 Jian-Gang Tang

We survey results on the formalization and independence of mathematical statements related to major open problems in computational complexity theory. Our primary focus is on recent findings concerning the (un)provability of complexity…

Computational Complexity · Computer Science 2025-04-08 Igor C. Oliveira

For several classical nonnegative integer functions, we investigate if they are members of the counting complexity class #P or not. We prove #P membership in surprising cases, and in other cases we prove non-membership, relying on standard…

Computational Complexity · Computer Science 2022-04-29 Christian Ikenmeyer , Igor Pak

In this paper, we make a preliminary interpretation of Cook's theorem presented in [1]. This interpretation reveals cognitive biases in the proof of Cook's theorem that arise from the attempt of constructing a formula in CNF to represent a…

Computational Complexity · Computer Science 2015-01-09 JianMing Zhou , Yu Li

A problem is a multivalued function from a set of \emph{instances} to a set of \emph{solutions}. We consider only instances and solutions coded by sets of integers. A problem admits preservation of some computability-theoretic weakness…

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