Of Starships and Klingons: Bayesian Logic for the 23rd Century
This addresses the need for intelligent systems to handle uncertain, interacting entities in open-world scenarios, representing a novel integration rather than an incremental improvement.
The paper tackles the problem of reasoning about uncertain relational domains by integrating first-order logic with Bayesian probability theory, resulting in Multi-entity Bayesian networks (MEBN) that can express probability distributions over models of any consistent, finitely axiomatizable first-order theory.
Intelligent systems in an open world must reason about many interacting entities related to each other in diverse ways and having uncertain features and relationships. Traditional probabilistic languages lack the expressive power to handle relational domains. Classical first-order logic is sufficiently expressive, but lacks a coherent plausible reasoning capability. Recent years have seen the emergence of a variety of approaches to integrating first-order logic, probability, and machine learning. This paper presents Multi-entity Bayesian networks (MEBN), a formal system that integrates First Order Logic (FOL) with Bayesian probability theory. MEBN extends ordinary Bayesian networks to allow representation of graphical models with repeated sub-structures, and can express a probability distribution over models of any consistent, finitely axiomatizable first-order theory. We present the logic using an example inspired by the Paramount Series StarTrek.