Learning action models from plan examples using weighted MAX-SAT
type: Item
instance of: scholarly article
title: Learning action models from plan examples using weighted MAX-SAT
zbMATH Open document ID: 1168.68555
author: Qiang Yang
author: Kangheng Wu
author: Yunfei Jiang
published in: Artificial Intelligence
publication date: 2009-07-09T00:00:00Z
Mathematics Subject Classification ID: 68T20
Mathematics Subject Classification ID: 68T05
zbMATH DE Number: 5576660
zbMATH Keywords: learning action models
zbMATH Keywords: automated planning
zbMATH Keywords: statistical relational learning
Wikidata QID: Q59678813
describes a project that uses: Walksat
describes a project that uses: DIMACS
describes a project that uses: SHOP2
describes a project that uses: SATO
describes a project that uses: PDDL
describes a project that uses: ARMS
describes a project that uses: Chaff
MaRDI profile type: Publication
full work available at URL: j.artint.2006.11.005
OpenAlex ID: W2024113144
cites work: A two-phase exact algorithm for MAX-SAT and weighted MAX-SAT problems
cites work: A Computing Procedure for Quantification Theory
cites work: Unifying Logical and Statistical AI
cites work: STRIPS: A new approach to the application of theorem proving to problem solving
cites work: Q4449295
cites work: Q4198056
cites work: Improved approximation algorithms for maximum cut and satisfiability problems using semidefinite programming
cites work: Solving propositional satisfiability problems
cites work: Q4375773
cites work: Q4139711
cites work: Q4449309
cites work: Markov logic networks
cites work: Q5687271
cites work: SATO: An efficient propositional prover
DBLP publication ID: journals/ai/YangWJ07
DOI: 10.1016/J.ARTINT.2006.11.005
description: scientific article; zbMATH DE number 5576660