An epistemic logic for modeling decisions in the context of incomplete
knowledge
- URL: http://arxiv.org/abs/2312.11186v1
- Date: Mon, 18 Dec 2023 13:27:04 GMT
- Title: An epistemic logic for modeling decisions in the context of incomplete
knowledge
- Authors: {\DJ}or{\dj}e Markovi\'c and Simon Vandevelde and Linde Vanbesien and
Joost Vennekens and Marc Denecker
- Abstract summary: This paper presents a new language for modeling decisions with incomplete knowledge.
It combines three principles: stratification, autoepistemic logic, and definitions.
- Score: 9.104555003332344
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Substantial efforts have been made in developing various Decision Modeling
formalisms, both from industry and academia. A challenging problem is that of
expressing decision knowledge in the context of incomplete knowledge. In such
contexts, decisions depend on what is known or not known. We argue that none of
the existing formalisms for modeling decisions are capable of correctly
capturing the epistemic nature of such decisions, inevitably causing issues in
situations of uncertainty. This paper presents a new language for modeling
decisions with incomplete knowledge. It combines three principles:
stratification, autoepistemic logic, and definitions. A knowledge base in this
language is a hierarchy of epistemic theories, where each component theory may
epistemically reason on the knowledge in lower theories, and decisions are made
using definitions with epistemic conditions.
Related papers
- Epistemology of Language Models: Do Language Models Have Holistic Knowledge? [30.02796959216552]
This paper investigates the inherent knowledge in language models from the perspective of holism.
The purpose of this paper is to explore whether characteristics of language models exhibit consistent with holism.
arXiv Detail & Related papers (2024-03-19T16:06:10Z) - Defeasible Reasoning with Knowledge Graphs [0.0]
This paper introduces work on an intuitive notation and model for defeasible reasoning with imperfect knowledge.
The paper closes with observations on symbolic approaches in the era of large language models.
arXiv Detail & Related papers (2023-09-22T09:27:26Z) - 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) - Strengthening Consistency Results in Modal Logic [0.0]
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.
arXiv Detail & Related papers (2023-07-11T07:05:37Z) - Causal Discovery with Language Models as Imperfect Experts [119.22928856942292]
We consider how expert knowledge can be used to improve the data-driven identification of causal graphs.
We propose strategies for amending such expert knowledge based on consistency properties.
We report a case study, on real data, where a large language model is used as an imperfect expert.
arXiv Detail & Related papers (2023-07-05T16:01:38Z) - Causal Deep Learning [77.49632479298745]
Causality has the potential to transform the way we solve real-world problems.
But causality often requires crucial assumptions which cannot be tested in practice.
We propose a new way of thinking about causality -- we call this causal deep learning.
arXiv Detail & Related papers (2023-03-03T19:19:18Z) - Learning by Applying: A General Framework for Mathematical Reasoning via
Enhancing Explicit Knowledge Learning [47.96987739801807]
We propose a framework to enhance existing models (backbones) in a principled way by explicit knowledge learning.
In LeAp, we perform knowledge learning in a novel problem-knowledge-expression paradigm.
We show that LeAp improves all backbones' performances, learns accurate knowledge, and achieves a more interpretable reasoning process.
arXiv Detail & Related papers (2023-02-11T15:15:41Z) - Acquiring and Modelling Abstract Commonsense Knowledge via Conceptualization [49.00409552570441]
We study the role of conceptualization in commonsense reasoning, and formulate a framework to replicate human conceptual induction.
We apply the framework to ATOMIC, a large-scale human-annotated CKG, aided by the taxonomy Probase.
arXiv Detail & Related papers (2022-06-03T12:24:49Z) - Thinking About Causation: A Causal Language with Epistemic Operators [58.720142291102135]
We extend the notion of a causal model with a representation of the state of an agent.
On the side of the object language, we add operators to express knowledge and the act of observing new information.
We provide a sound and complete axiomatization of the logic, and discuss the relation of this framework to causal team semantics.
arXiv Detail & Related papers (2020-10-30T12:16:45Z) - Knowledge Patterns [19.57676317580847]
This paper describes a new technique, called "knowledge patterns", for helping construct axiom-rich, formal Ontology.
Knowledge patterns provide an important insight into the structure of a formal Ontology.
We describe the technique and an application built using them, and then critique their strengths and weaknesses.
arXiv Detail & Related papers (2020-05-08T22:33:30Z)
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.