Logic meets Probability: Towards Explainable AI Systems for Uncertain Worlds

By Vaishak Belle. 2017

45 citations recorded by the source corpus.

Logical AI is concerned with formal languages to represent and reason with qualitative speciﬁcations; statistical AI is concerned with learning quantitative speciﬁcations from data. To combine the strengths of these two camps, there has been exciting recent progress on unifying logic and probability. We review the many guises for this union, while emphasizing the need for a formal language to represent a system’s knowledge. Formal languages allow their internal properties to be robustly scrutinized, can be augmented by adding new knowledge, and are amenable to abstractions, all of which are vital to the design of intelligent systems that are explainable and interpretable.

Subfields: Artificial Intelligence, Safety, Robustness & Interpretability, Probabilistic & Bayesian Methods

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

In Safety, Robustness & Interpretability

In Probabilistic & Bayesian Methods

Citations in the corpus

Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI (2019, neutral): “Informativeness [5, 44, 21, 25, 26, 45, 30, 32, 34, 35, 37, 38, 41, 46, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 63, 64, 65, 66, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 86, 87, 88, 89, 59, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 1”
Combinatorial Methods for Explainable AI (2020, background): “Consequently, there is a significant need for improvements in explainability of AI/ML system functions and decisions [1][2][3][4][5][6][7].”
A comprehensive taxonomy for explainable artificial intelligence: a systematic survey of surveys on methods and concepts (2021, background): “According to [16], the first mention of the underlying concept already dates back to the year 1958, when McCarthy described his idea of how to realize AI systems.”
Explainable Artificial Intelligence: a Systematic Review (2020, methodology): “” Some methods combine Logical AI and Statistical AI to generate textual explanations [38].”