GOAL-DIRECTED ANSWER SET PROGRAMMING by Kyle Brandon Marple APPROVED BY ...

128 Pages·2014·0.59 MB·English

GOAL-DIRECTED ANSWER SET PROGRAMMING
by
Kyle Brandon Marple
APPROVED BY SUPERVISORY COMMITTEE:
Gopal Gupta, Chair
Vibhav Gogate
Kevin W. Hamlen
Haim Schweitzer
Copyright (cid:13)c 2014
Kyle Brandon Marple
All rights reserved
GOAL-DIRECTED ANSWER SET PROGRAMMING
by
KYLE BRANDON MARPLE, BS, MS
DISSERTATION
Presented to the Faculty of
The University of Texas at Dallas
in Partial Fulfillment
of the Requirements
for the Degree of
DOCTOR OF PHILOSOPHY IN
COMPUTER SCIENCE
THE UNIVERSITY OF TEXAS AT DALLAS
August 2014
ACKNOWLEDGMENTS
This dissertation would not exist without the help and support of several wonderful individ-
uals, and I am grateful to them all. Dr. Gopal Gupta, my adviser, introduced me to both
logic programming and answer set programming. Without his guidance and support, I would
never have made it this far. Dr. Feliks Klu´zniak provided me with invaluable feedback and
constructive criticism, and told me the truth even when I did not want to hear it. Without
him, Galliwasp might still be an incomprehensible mass of C code, and much of my work
would have been impossible. I thank Dr. Ajay Bansal and Dr. Richard Min, whose work
provided me with the foundation for my own. I would also like to thank those who helped
kindle my original interest in computer science: Steve Jackson of Lawton High School, who
taught the first programming course I ever took; and Professor Feridoon Moinian and Dr.
Chao Zhao of Cameron University, who helped turn my interest into passion. Finally, I
would not be where I am today without my father and grandmother, who have supported
me through it all.
June 2014
iv
PREFACE
This dissertation was produced in accordance with guidelines which permit the inclusion as
part of the dissertation the text of an original paper or papers submitted for publication.
The dissertation must still conform to all other requirements explained in the “Guide for the
Preparation of Master’s Theses and Doctoral Dissertations at The University of Texas at
Dallas.” It must include a comprehensive abstract, a full introduction and literature review,
and a final overall conclusion. Additional material (procedural and design data as well as
descriptions of equipment) must be provided in sufficient detail to allow a clear and precise
judgment to be made of the importance and originality of the research reported.
It is acceptable for this dissertation to include as chapters authentic copies of papers already
published, provided these meet type size, margin, and legibility requirements. In such cases,
connecting texts which provide logical bridges between different manuscripts are mandatory.
Where the student is not the sole author of a manuscript, the student is required to make an
explicit statement in the introductory material to that manuscript describing the student’s
contribution to the work and acknowledging the contribution of the other author(s). The
signatures of the Supervising Committee which precede all other material in the dissertation
attest to the accuracy of this statement.
v
GOAL-DIRECTED ANSWER SET PROGRAMMING
Publication No.
Kyle Brandon Marple, PhD
The University of Texas at Dallas, 2014
Supervising Professor: Gopal Gupta
Answer Set Programming (ASP) provides an attractive means for integrating non-monotonic
reasoning into logic programming. However, current ASP solvers are implemented using
bottom-up execution algorithms. That is, none of the current approaches to implementation
aregoal-directed. ThisresultsinanumberofissueswhichpreventASPfrombeingadoptedon
alargerscale,includingtheneedtocomputeanentireanswersetforanyqueryandtheability
of minor inconsistencies to invalidate solutions in unrelated areas of a knowledgebase. This
dissertationpresentsa goal-directed methodforexecuting answer setprogramsin thestyleof
Selective Linear Definite clause resolution (SLD resolution), and discusses its advantages and
how it addresses the first of the problems listed above. The implementation of the algorithm
in the Galliwasp system is also presented and its performance is compared to that of other
popular ASP solvers. Next, optimization techniques developed to improve performance are
discussed and their advantages and disadvantages compared, along with the performance
of various combinations of the techniques. Finally, Dynamic Consistency Checking (DCC),
a technique for querying inconsistent databases which addresses the second problem listed
above, is covered.
vi
TABLE OF CONTENTS
ACKNOWLEDGMENTS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . iv
PREFACE . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . v
ABSTRACT . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vi
LIST OF FIGURES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xi
LIST OF TABLES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xii
CHAPTER 1 INTRODUCTION . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.1 Overview. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.2 Structure of the Dissertation . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
CHAPTER 2 BACKGROUND . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
2.1 Overview. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
2.2 Answer Set Programming . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
2.3 Coinductive Logic Programming . . . . . . . . . . . . . . . . . . . . . . . . . 9
CHAPTER 3 GOAL-DIRECTED EXECUTION OF ANSWER SET PROGRAMS 11
3.1 Acknowledgements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
3.2 Overview. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
3.3 Goal-directed ASP Issues . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12
3.3.1 Ordinary Rules . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14
3.3.2 OLON Rules . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16
3.3.3 Coinductive Success Under ASP . . . . . . . . . . . . . . . . . . . . . 17
3.3.4 NMR Consistency Check . . . . . . . . . . . . . . . . . . . . . . . . . 19
3.4 Goal-directed Execution of Answer Set Programs . . . . . . . . . . . . . . . 20
3.4.1 Dual Rules . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
3.4.2 Goal-directed Method for Computing Answer Sets . . . . . . . . . . . 21
3.5 Soundness and Correctness of the Goal-directed Method . . . . . . . . . . . 24
3.5.1 Relevance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24
vii
3.5.2 Soundness . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26
3.5.3 Completeness . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27
3.6 Discussion and Related Work . . . . . . . . . . . . . . . . . . . . . . . . . . 29
CHAPTER 4 GALLIWASP: A GOAL-DIRECTED ANSWER SET SOLVER . . . 31
4.1 Acknowledgements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31
4.2 Overview. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31
4.3 System Architecture of Galliwasp . . . . . . . . . . . . . . . . . . . . . . . . 32
4.3.1 Compiler . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32
4.3.2 Interpreter . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37
4.4 Performance Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38
4.4.1 Rule and Goal Ordering . . . . . . . . . . . . . . . . . . . . . . . . . 40
4.5 Related Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42
CHAPTER 5 IMPROVING THE PERFORMANCE OF GALLIWASP . . . . . . . 43
5.1 Overview. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43
5.2 Compiler-based Optimization . . . . . . . . . . . . . . . . . . . . . . . . . . 44
5.2.1 Program Simplification . . . . . . . . . . . . . . . . . . . . . . . . . . 44
5.2.2 Factorization of Common Goals . . . . . . . . . . . . . . . . . . . . . 46
5.3 NMR Check Optimization . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51
5.4 CHS-based Optimization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53
5.5 Comparative Performance Results . . . . . . . . . . . . . . . . . . . . . . . . 54
5.5.1 Compiler Performance . . . . . . . . . . . . . . . . . . . . . . . . . . 54
5.5.2 Interpreter Performance . . . . . . . . . . . . . . . . . . . . . . . . . 56
5.6 Related Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60
CHAPTER 6 DYNAMIC CONSISTENCY CHECKING . . . . . . . . . . . . . . . 61
6.1 Acknowledgements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61
6.2 Overview. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61
6.3 Dynamic Consistency Checking . . . . . . . . . . . . . . . . . . . . . . . . . 63
6.3.1 Relevance Under DCC . . . . . . . . . . . . . . . . . . . . . . . . . . 64
6.3.2 Execution with DCC . . . . . . . . . . . . . . . . . . . . . . . . . . . 66
viii
6.3.3 Correctness of DCC . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67
6.4 Advantages of DCC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 68
6.4.1 Answer Sets of Inconsistent Programs . . . . . . . . . . . . . . . . . . 68
6.4.2 Query-relevant Output . . . . . . . . . . . . . . . . . . . . . . . . . . 69
6.4.3 Performance Compared to Normal Consistency Checking . . . . . . . 69
6.5 Related Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71
CHAPTER 7 FUTURE WORK AND CONCLUSION . . . . . . . . . . . . . . . . 72
7.1 Future Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 72
7.2 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73
APPENDIX A HANDLING SPECIAL LITERALS AND RULES . . . . . . . . . . . 75
A.1 Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 75
A.2 Special Literals . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 75
A.2.1 Constraint Literals . . . . . . . . . . . . . . . . . . . . . . . . . . . . 75
A.2.2 Weight Literals . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 77
A.2.3 Classically Negated Literals . . . . . . . . . . . . . . . . . . . . . . . 78
A.3 Special Rule Types . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 78
A.3.1 Choice Rules . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 78
A.3.2 Optimization Statements . . . . . . . . . . . . . . . . . . . . . . . . . 80
APPENDIX B HANOI: TAKING ADVANTAGE OF PARTIAL ANSWER SETS . 83
B.1 Our Towers of Hanoi Encoding . . . . . . . . . . . . . . . . . . . . . . . . . 83
B.2 Output Comparison . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 85
B.2.1 Galliwasp’s Output for hanoi-4x15 . . . . . . . . . . . . . . . . . . . 85
B.2.2 Clasp’s Output for hanoi-4x15 . . . . . . . . . . . . . . . . . . . . . . 86
APPENDIX C GALLIWASP 1.3.5 USER MANUAL . . . . . . . . . . . . . . . . . . 90
C.1 README . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 90
C.1.1 Description . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 90
C.1.2 License . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 90
C.1.3 Package Contents . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 90
C.1.4 Installation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 91
ix
C.1.5 Grounding . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 91
C.1.6 Basic Usage . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 92
C.1.7 Optimization Statements . . . . . . . . . . . . . . . . . . . . . . . . . 94
C.1.8 Dynamic Consistency Checking (DCC) . . . . . . . . . . . . . . . . . 94
C.2 Compiler Usage and Options . . . . . . . . . . . . . . . . . . . . . . . . . . . 95
C.3 Interpreter Usage and Options . . . . . . . . . . . . . . . . . . . . . . . . . . 97
C.4 Compiled Program Format . . . . . . . . . . . . . . . . . . . . . . . . . . . . 103
C.4.1 CPF Grammar . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 104
REFERENCES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 112
VITA
x