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Advanced Tools and Methods for Treewidth-Based Problem Solving [Minkštas viršelis]

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This book, Advanced Tools and Methods for Treewidth-Based Problem Solving, contains selected results from the author’s PhD studies, which were carried out from 2015 to 2021. For his PhD thesis, Markus Hecher received the EurAI Dissertation Award 2021 and the GI Dissertation Award 2021, amongst others.The aim of the book is to present a new toolkit for using the structural parameter of treewidth to solve problems in knowledge representation and reasoning (KR) and artificial intelligence (AI), thereby establishing both theoretical upper and lower bounds, as well as methods to deal with treewidth efficiently in practice. The key foundations outlined in the book provide runtime lower bounds – under reasonable assumptions in computational complexity – for evaluating quantified Boolean formulas and logic programs which match the known upper bounds already published in 2004 and 2009. The general nature of the developed tools and techniques means that a wide applicability beyond the selected problems and formalisms tackled in the book is anticipated, and it is hoped that the book will serve as a starting point for future theoretical and practical investigations, which will no doubt establish further results and gain deeper insights.
Chapter 1 Introduction
1(16)
1.1 Contributions and Overview"
7(10)
Chapter 2 Preliminaries
17(24)
2.1 Graph Theory
17(2)
2.2 Computational Complexity
19(3)
2.3 (Quantified) Boolean Formulas
22(3)
2.4 Answer Set Programming
25(5)
2.5 Tree Decompositions and Treewidth
30(5)
2.6 Labeled Tree Decompositions
35(6)
Chapter 3 Upper Bounds for Using Treewidth
41(30)
3.1 Basics on Dynamic Programming
43(7)
3.2 Dynamic Programming for ASP
50(20)
3.3 Outlook on Dynamic Programming
70(1)
Chapter 4 Decomposition-Guided Reductions
71(36)
4.1 Concept and Definitions
74(3)
4.2 DG Reduction from Tight Asp to Sat
77(4)
4.3 DG Reduction from HCF Asp to Sat
81(12)
4.4 DG Reduction from Asp to Almost Tight Asp
93(11)
4.5 Discussion: Different Ways of Treating Hard Cycles
104(3)
Chapter 5 Lower Bounds by DG Reductions
107(40)
5.1 Lower Bounds for QBFs and Treewidth
111(21)
5.2 Lower Bounds for Asp and Treewidth
132(15)
Chapter 6 A Complexity Landscape for Treewidth
147(12)
6.1 A Methodology for Lower Bounds
149(4)
6.2 Complexity Characterization for Treewidth
153(6)
Chapter 7 Efficient Treewidth-Aware Algorithms
159(40)
7.1 Abstractions as a Key for Nested DP
161(8)
7.2 Hybrid Dynamic Programming - Refining Nested DP
169(7)
7.3 Dynamic Programming with Database Systems
176(11)
7.4 Abstractions and Hybrid Dynamic Programming
187(12)
Chapter 8 Discussion
199(8)
8.1 Related Work
201(2)
8.2 Future Work
203(4)
Bibliography 207