Uncertain Linear Logic via Fibring of Probabilistic and Fuzzy Logic
- URL: http://arxiv.org/abs/2009.12990v1
- Date: Mon, 28 Sep 2020 00:19:42 GMT
- Title: Uncertain Linear Logic via Fibring of Probabilistic and Fuzzy Logic
- Authors: Ben Goertzel
- Abstract summary: probabilistic and fuzzy logic correspond to two different assumptions regarding the combination of propositions whose evidence bases are not currently available.
It is shown that these two sets of formulas provide a natural grounding for the multiplicative and additive operator-sets in linear logic.
The concept of linear logic as a logic of resources" is manifested here via the principle of conservation of evidence"
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Beginning with a simple semantics for propositions, based on counting
observations, it is shown that probabilistic and fuzzy logic correspond to two
different heuristic assumptions regarding the combination of propositions whose
evidence bases are not currently available. These two different heuristic
assumptions lead to two different sets of formulas for propagating quantitative
truth values through lattice operations. It is shown that these two sets of
formulas provide a natural grounding for the multiplicative and additive
operator-sets in linear logic. The standard rules of linear logic then emerge
as consequences of the underlying semantics. The concept of linear logic as a
``logic of resources" is manifested here via the principle of ``conservation of
evidence" -- the restrictions to weakening and contraction in linear logic
serve to avoid double-counting of evidence (beyond any double-counting incurred
via use of heuristic truth value functions).
Related papers
- An elementary belief function logic [6.091096843566857]
duality between possibility and necessity measures, belief and plausibility functions and imprecise probabilities share a common feature with modal logic.
This paper shows that a simpler belief function logic can be devised by adding Lukasiewicz logic on top of MEL.
arXiv Detail & Related papers (2023-03-23T10:39:18Z) - Discourse-Aware Graph Networks for Textual Logical Reasoning [142.0097357999134]
Passage-level logical relations represent entailment or contradiction between propositional units (e.g., a concluding sentence)
We propose logic structural-constraint modeling to solve the logical reasoning QA and introduce discourse-aware graph networks (DAGNs)
The networks first construct logic graphs leveraging in-line discourse connectives and generic logic theories, then learn logic representations by end-to-end evolving the logic relations with an edge-reasoning mechanism and updating the graph features.
arXiv Detail & Related papers (2022-07-04T14:38:49Z) - Neuro-Symbolic Inductive Logic Programming with Logical Neural Networks [65.23508422635862]
We propose learning rules with the recently proposed logical neural networks (LNN)
Compared to others, LNNs offer strong connection to classical Boolean logic.
Our experiments on standard benchmarking tasks confirm that LNN rules are highly interpretable.
arXiv Detail & Related papers (2021-12-06T19:38:30Z) - 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) - A natural deduction system for orthomodular logic [0.0]
Orthomodular logic is a weakening of quantum logic in the sense of Birkhoff and von Neumann.
It is shown to be a nonlinear noncommutative logic.
It is extended to two systems of predicate logic: the first is sound for Takeuti's quantum set theory, and the second is sound for a variant of Weaver's quantum logic.
arXiv Detail & Related papers (2021-09-11T22:28:17Z) - RNNLogic: Learning Logic Rules for Reasoning on Knowledge Graphs [91.71504177786792]
This paper studies learning logic rules for reasoning on knowledge graphs.
Logic rules provide interpretable explanations when used for prediction as well as being able to generalize to other tasks.
Existing methods either suffer from the problem of searching in a large search space or ineffective optimization due to sparse rewards.
arXiv Detail & Related papers (2020-10-08T14:47:02Z) - Foundations of Reasoning with Uncertainty via Real-valued Logics [70.43924776071616]
We give a sound and strongly complete axiomatization that can be parametrized to cover essentially every real-valued logic.
Our class of sentences are very rich, and each describes a set of possible real values for a collection of formulas of the real-valued logic.
arXiv Detail & Related papers (2020-08-06T02:13:11Z) - 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.