5 citations · 7 across the 7 of their papers we have counts for
7 papers · 1 filter
A Simple Generative Model of Logical Reasoning and Statistical Learning
Hiroyuki Kido
Statistical learning and logical reasoning are two major fields of AI expected to be unified for human-like machine intelligence. Most existing work considers how to combine existi…
A Data-Transparent Probabilistic Model of Temporal Propositional Abstraction
Hiroyuki Kido
Standard probabilistic models face fundamental challenges such as data scarcity, a large hypothesis space, and poor data transparency. To address these challenges, we propose a nov…
Towards Unifying Perceptual Reasoning and Logical Reasoning
Hiroyuki Kido
An increasing number of scientific experiments support the view of perception as Bayesian inference, which is rooted in Helmholtz's view of perception as unconscious inference. Rec…
Towards Unifying Logical Entailment and Statistical Estimation
Hiroyuki Kido
This paper gives a generative model of the interpretation of formal logic for data-driven logical reasoning. The key idea is to represent the interpretation as likelihood of a form…
Bayes Meets Entailment and Prediction: Commonsense Reasoning with Non-monotonicity, Paraconsistency and Predictive Accuracy
Hiroyuki Kido, Keishi Okamoto
The recent success of Bayesian methods in neuroscience and artificial intelligence gives rise to the hypothesis that the brain is a Bayesian machine. Since logic and learning are b…
Bayesian Entailment Hypothesis: How Brains Implement Monotonic and Non-monotonic Reasoning
Hiroyuki Kido
Recent success of Bayesian methods in neuroscience and artificial intelligence gives rise to the hypothesis that the brain is a Bayesian machine. Since logic, as the laws of though…
A Bayesian Approach to Direct and Inverse Abstract Argumentation Problems
Hiroyuki Kido, Beishui Liao
This paper studies a fundamental mechanism of how to detect a conflict between arguments given sentiments regarding acceptability of the arguments. We introduce a concept of the in…