Our brains allow us to reason about alternatives and to make choices that are likely to pay off. Often there is no one correct answer, but instead one that is favoured simply because it is more likely ...

ある結果が得られる見込みを素早く評価して、それに基づき判断を下すこと。 どの観点から見るかによって、演繹・帰納的推論なのかが左右される。 日常経験のなかで経験したことからその確率を推論する場合は帰納的推論となり、ある出来事の確率が ...

Human reasoning is traditionally modeled through rational-order frameworks that assume stability, separability, and coherence. Yet across judgment, valuation, perception, and social decision-making, ...

This is a collection of algorithms and models written in Python for probabilistic programming. The main focus of the package is on Bayesian reasoning by using Bayesian networks, Markov networks, and ...

Abstract: The traditional Multi-Agent Reinforcement Learning (MARL) solutions often provide assumptions with respect to full observability or reliable communication that are seldom met in the presence ...

Abstract: The limitations of existing approaches for modeling first-order logic (FOL) queries over knowledge graphs (KGs) have motivated the development of probabilistic reasoning methods. While box ...