A Natural Language Argumentation Interface for Explanation Generation in Markov Decision Processes

43 citations recorded by the source corpus.

How researchers see this paper

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

Visualizing and Understanding Atari Agents (2017, background): “Their key contribution is a method of approximating the observed behavior of deep RL policies via "Semi-Aggregated" Markov Decision Processes (SAMDPs), which are used to gain insights about the deep RL policies.”
Explanation and Justification in Machine Learning : A Survey Or (2017, background): “…executing necessary operations; [Khan et al. , 2009] explore the minimal explanation sufﬁcient for tasks such as picking the next course in a college curriculum; [Dodson et al. , 2011] propose a dialog system, instead of a single ﬁxed explanation, which allows the user to argue and ask questions.”
Learn to Interpret Atari Agents (2018, background): “Traditionally, interpreting RL systems may involve language generation via ﬁrst-order logic [28]–[31].”