Strengthening Consistency Results in Modal Logic
- URL: http://arxiv.org/abs/2307.05053v1
- Date: Tue, 11 Jul 2023 07:05:37 GMT
- Title: Strengthening Consistency Results in Modal Logic
- Authors: Samuel Allen Alexander (US Securities and Exchange Commission), Arthur
Paul Pedersen (City University of New York)
- Abstract summary: A fundamental question in modal logic is whether a given theory is consistent, but consistent with what?
A typical way to address this question identifies a choice of background knowledge axioms (say, S4, D, etc.) and then shows the assumptions codified by the theory in question to be consistent with those background axioms.
This paper introduces generic theories for propositional modal logic to address consistency results in a more robust way.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: A fundamental question asked in modal logic is whether a given theory is
consistent. But consistent with what? A typical way to address this question
identifies a choice of background knowledge axioms (say, S4, D, etc.) and then
shows the assumptions codified by the theory in question to be consistent with
those background axioms. But determining the specific choice and division of
background axioms is, at least sometimes, little more than tradition. This
paper introduces **generic theories** for propositional modal logic to address
consistency results in a more robust way. As building blocks for background
knowledge, generic theories provide a standard for categorical determinations
of consistency. We argue that the results and methods of this paper help to
elucidate problems in epistemology and enjoy sufficient scope and power to have
purchase on problems bearing on modalities in judgement, inference, and
decision making.
Related papers
- A Note on an Inferentialist Approach to Resource Semantics [48.65926948745294]
'Inferentialism' is the view that meaning is given in terms of inferential behaviour.
This paper shows how 'inferentialism' enables a versatile and expressive framework for resource semantics.
arXiv Detail & Related papers (2024-05-10T14:13:21Z) - Towards Generalizable and Faithful Logic Reasoning over Natural Language via Resolution Refutation [24.584926992534346]
We propose a novel framework, named Generalizable and Faithful Reasoner (GFaiR), which introduces the paradigm of resolution refutation.
Resolution refutation has the capability to solve all first-order logic reasoning problems by extending reasoning rules and employing the principle of proof by contradiction.
Our system outperforms previous works by achieving state-of-the-art performances in complex scenarios while maintaining performances in simple scenarios.
arXiv Detail & Related papers (2024-04-02T06:28:44Z) - Soft Reasoning on Uncertain Knowledge Graphs [85.1968214421899]
We study the setting of soft queries on uncertain knowledge, which is motivated by the establishment of soft constraint programming.
We propose an ML-based approach with both forward inference and backward calibration to answer soft queries on large-scale, incomplete, and uncertain knowledge graphs.
arXiv Detail & Related papers (2024-03-03T13:13:53Z) - An epistemic logic for modeling decisions in the context of incomplete
knowledge [9.104555003332344]
This paper presents a new language for modeling decisions with incomplete knowledge.
It combines three principles: stratification, autoepistemic logic, and definitions.
arXiv Detail & Related papers (2023-12-18T13:27:04Z) - Phenomenal Yet Puzzling: Testing Inductive Reasoning Capabilities of Language Models with Hypothesis Refinement [92.61557711360652]
Language models (LMs) often fall short on inductive reasoning, despite achieving impressive success on research benchmarks.
We conduct a systematic study of the inductive reasoning capabilities of LMs through iterative hypothesis refinement.
We reveal several discrepancies between the inductive reasoning processes of LMs and humans, shedding light on both the potentials and limitations of using LMs in inductive reasoning tasks.
arXiv Detail & Related papers (2023-10-12T17:51:10Z) - A Semantic Approach to Decidability in Epistemic Planning (Extended
Version) [72.77805489645604]
We use a novel semantic approach to achieve decidability.
Specifically, we augment the logic of knowledge S5$_n$ and with an interaction axiom called (knowledge) commutativity.
We prove that our framework admits a finitary non-fixpoint characterization of common knowledge, which is of independent interest.
arXiv Detail & Related papers (2023-07-28T11:26:26Z) - Learnability with PAC Semantics for Multi-agent Beliefs [38.88111785113001]
The tension between deduction and induction is perhaps the most fundamental issue in areas such as philosophy, cognition and artificial intelligence.
Valiant recognised that the challenge of learning should be integrated with deduction.
Although weaker than classical entailment, it allows for a powerful model-theoretic framework for answering queries.
arXiv Detail & Related papers (2023-06-08T18:22:46Z) - An Embedding-based Approach to Inconsistency-tolerant Reasoning with
Inconsistent Ontologies [12.760301272393898]
We propose a novel approach to reasoning with inconsistent semantics based on the embeddings of axioms.
We show that our embedding-based method can outperform existing inconsistency-tolerant reasoning methods based on maximal consistent subsets.
arXiv Detail & Related papers (2023-04-04T09:38:02Z) - Logical Credal Networks [87.25387518070411]
This paper introduces Logical Credal Networks, an expressive probabilistic logic that generalizes many prior models that combine logic and probability.
We investigate its performance on maximum a posteriori inference tasks, including solving Mastermind games with uncertainty and detecting credit card fraud.
arXiv Detail & Related papers (2021-09-25T00:00:47Z) - Exploiting Game Theory for Analysing Justifications [13.72913891724593]
We continue the study of justification theory by means of three major contributions.
The first is studying the relation between justification theory and game theory.
The second contribution is studying under which condition two different dialects of justification theory coincide.
The third contribution is establishing a precise criterion of when a semantics induced by justification theory yields consistent results.
arXiv Detail & Related papers (2020-08-04T14:45:08Z) - Logical Neural Networks [51.46602187496816]
We propose a novel framework seamlessly providing key properties of both neural nets (learning) and symbolic logic (knowledge and reasoning)
Every neuron has a meaning as a component of a formula in a weighted real-valued logic, yielding a highly intepretable disentangled representation.
Inference is omni rather than focused on predefined target variables, and corresponds to logical reasoning.
arXiv Detail & Related papers (2020-06-23T16:55:45Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.