On probability-raising causality in Markov decision processes

01/21/2022
by   Christel Baier, et al.
0

The purpose of this paper is to introduce a notion of causality in Markov decision processes based on the probability-raising principle and to analyze its algorithmic properties. The latter includes algorithms for checking cause-effect relationships and the existence of probability-raising causes for given effect scenarios. Inspired by concepts of statistical analysis, we study quality measures (recall, coverage ratio and f-score) for causes and develop algorithms for their computation. Finally, the computational complexity for finding optimal causes with respect to these measures is analyzed.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
09/07/2022

Foundations of probability-raising causality in Markov decision processes

This work introduces a novel cause-effect relation in Markov decision pr...
research
04/28/2021

Probabilistic causes in Markov chains

The paper studies a probabilistic notion of causes in Markov chains that...
research
05/09/2012

The Temporal Logic of Causal Structures

Computational analysis of time-course data with an underlying causal str...
research
04/01/2022

Actual Causality and Responsibility Attribution in Decentralized Partially Observable Markov Decision Processes

Actual causality and a closely related concept of responsibility attribu...
research
02/28/2018

Verification of Markov Decision Processes with Risk-Sensitive Measures

We develop a method for computing policies in Markov decision processes ...
research
10/15/2018

Machine Self-Confidence in Autonomous Systems via Meta-Analysis of Decision Processes

Algorithmic assurances from advanced autonomous systems assist human use...
research
01/25/2016

Conditional distribution variability measures for causality detection

In this paper we derive variability measures for the conditional probabi...

Please sign up or login with your details

Forgot password? Click here to reset