Work

Refining complexity analyses in planning by exploiting the exponential time hypothesis

Year: 2016

Type: article

Abstract: The use of computational complexity in planning, and in AI in general, has always been a disputed topic. A major problem with ordinary worst-case analyses is that they do not provide any quantitative information: they do not tell us much about the running time of concrete algorithms, nor do they tell us much about the running time of optimal algorithms. We address problems like this by presenting results based on the exponential time hypothesis (ETH) , which is a widely accepted hypothesis concerning the time complexity of 3- SAT . By using this approach, we provide, for instance, almost matching upper and lower bounds onthe time complexity of propositional planning.

Source: Annals of Mathematics and Artificial Intelligence

DOI: 10.1007/s10472-016-9521-y

Authors: Meysam Aghighi, Christer Bäckström, Peter Jonsson, Simon Ståhlberg

Institution: Linköping University

Language: English

Topic: AI-based Problem Solving and Planning

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: hybrid

Estimated APC paid: $3,190

Funders: Vetenskapsrådet, National Graduate School in Computer Science