REFINING the SEMANTICS for EPISTEMIC LOGIC PROGRAMS by Patrick Thor Kahl, M.S. a Dissertation in COMPUTER SCIENCE Submitted to T

Total Page:16

File Type:pdf, Size:1020Kb

REFINING the SEMANTICS for EPISTEMIC LOGIC PROGRAMS by Patrick Thor Kahl, M.S. a Dissertation in COMPUTER SCIENCE Submitted to T REFINING THE SEMANTICS FOR EPISTEMIC LOGIC PROGRAMS by Patrick Thor Kahl, M.S. A Dissertation in COMPUTER SCIENCE Submitted to the Graduate Faculty of Texas Tech University in Partial Fulfillment of the Requirements for the Degree of DOCTOR OF PHILOSOPHY Approved Richard Watson Chair of Committee Michael Gelfond Yuanlin Zhang Mark Sheridan Dean of the Graduate School May 2014 Copyright © 2014 by Patrick Thor Kahl To my family \Exploit symmetry whenever possible. Do not destroy it lightly." |Edsger Dijkstra (1930-2002) Texas Tech University, Patrick Thor Kahl, May 2014 ACKNOWLEDGMENTS My sincere thanks to the members of my committee who were all instrumental in the success of my research goal. My advisor, Dr. Richard Watson, who kindly agreed to take me in as his doctoral student, was a pleasure to work with on this topic. I appreciate all the hours he spent guiding me, sharing the ups and downs of one after another failure, yet maintaining the fervent belief that we were ever closer to our goal. In short, he was able to make this task an exciting endeavor, sharing with me in the adventure of scientific research. It was Dr. Michael Gelfond who introduced Epistemic Specifications to the world in the early 1990s, and he has continued to lead in its development, particularly in recent years with the increase in interest. His keen insight concerning the various different modal reducts proposed and whether they reflected how a rational agent should pick from among different models kept me from abandoning what eventually became the definition presented herein. The new definition led to a new intuition concerning the world views of various programs, one that I now believe is correct and also beautiful. Dr. Gelfond has continued to be my mentor since my undergraduate years at the University of Texas at El Paso. Anyone who has worked with him has indeed been blessed. Dr. Yuanlin Zhang continued to support me in my ever-changing directions, pa- tiently listening to my ideas and helping guide me toward realistic goals. He was the only one who was able to come up with a methodology that successfully addressed the problem of circular support through modal operator M as originally perceived. Although what I eventually chose to propose in this dissertation does not use his methodology, he addressed the objective I had at the time, and his idea remains a promising approach for future research. He has been both a teacher and a friend, giving me that most precious commodity, his time. I also wish to express my appreciation to the past and present members of the Knowledge Representation Lab at Texas Tech for their support, suggestions, encour- ii Texas Tech University, Patrick Thor Kahl, May 2014 agement, and friendship. Everything from KR seminars to ping-pong was made more enjoyable and edifying due to their participation. I humbly follow in the footsteps of greats|Marcello, Veena, Sandeep, Ricardo, Yana, Weijun, Daniela, Justin|whose accomplishments have been an inspiration. I am especially grateful to my most recent lab mates|Evgenii, Edward, and Qianji|for their assistance and unending patience with me. My best wishes to them in their research endeavors. (And, yes, I'll miss the basement dwellers. You know who you are.) My thanks also goes out to Dr. Nelson Rushton who further enlightened me con- cerning the language of mathematics and mathematical rigor in the field of computer science. His teachings have helped me improve the clarity of my communications, though I realize that I still have much to learn. Further thanks is extended to the faculty, staff, and fellow students of the Depart- ment of Computer Science who taught, assisted, laughed, struggled, worked with, and shared in my many experiences here at Texas Tech. Finally, to my dear family, I thank you with all my heart. The support I received from my mother, my siblings, and my extended family|Lara, Michael, and Greg| enabled me to feel good about pursuing this goal so late in my life. My wife Yulia and my children Alexandra and Jonathan all persevered through the years of my return to school. Their never-ending love, encouragement, support, and patience during this time have made it all possible. Patrick Thor Kahl March 12, 2014 Department of Computer Science Texas Tech University Lubbock, TX, USA iii Texas Tech University, Patrick Thor Kahl, May 2014 TABLE OF CONTENTS ACKNOWLEDGMENTS . ii ABSTRACT . vi I INTRODUCTION . .1 1.1 Motivation for a New Definition . .3 1.2 Finding the New Definition . .5 II BACKGROUND . .7 2.1 ASP . .7 2.2 Epistemic Specifications . .9 2.3 Language Development Timeline . 10 III SYNTAX AND SEMANTICS OF EPISTEMIC SPECIFICATIONS 13 3.1 Syntax . 13 3.2 Semantics . 15 IV AN ALGORITHM FOR FINDING WORLD VIEWS . 21 V SIMPLIFYING AN EPISTEMIC LOGIC PROGRAM . 24 VI COMPARISON WITH OLD VERSIONS OF THE LANGUAGE . 25 6.1 Semantic Differences between Old and New Language . 25 6.2 Expressibility . 26 VII APPLICATIONS . 28 VIII RELATED WORK . 32 IX CONCLUSIONS AND FUTURE WORK . 35 9.1 Conclusion . 35 9.2 Future Work . 35 BIBLIOGRAPHY . 41 APPENDIX A: PROOFS . A-1 APPENDIX B: FAILED ATTEMPTS TO FIND CORRECT SEMANTICS B-1 B.1 Failed Attempt 1 . B-2 B.2 Failed Attempt 2 . B-2 iv Texas Tech University, Patrick Thor Kahl, May 2014 B.3 Failed Attempt 3 . B-3 B.4 Failed Attempt 4 . B-4 B.5 Failed Attempt 5 . B-5 B.6 Failed Attempt 6 . B-6 B.7 Failed Attempt 7 . B-6 APPENDIX C: EPISTEMIC LOGIC PROGRAMS WITH SORTS . C-1 C.1 Introduction . C-1 C.2 Syntax and Semantics . C-2 C.2.1 A New Meaning for Rules with Variables . C-2 C.2.2 Syntax of an ELPS . C-3 C.2.3 Semantics . C-4 C.3 Algorithm . C-4 APPENDIX D: EXAMPLE PROGRAMS . D-1 v Texas Tech University, Patrick Thor Kahl, May 2014 ABSTRACT The primary goal of this dissertation is to present a new semantics for the lan- guage of Epistemic Specifications that accurately defines those models of associated programs that are to be considered as world views from the standpoint of a rational agent. Epistemic Specifications is a declarative programming language that is an ex- tension of answer set programming (ASP) through the addition of modal operators K and M. Programs written in this language are called epistemic logic programs. Previously proposed semantics did not satisfy our intuition for certain programs. In order to achieve the stated goal, the semantics should reflect the principles of a rational agent, as well as our intuition regarding the notion of support for a literal. Toward that goal, the focus here is on the following items. First, a new definition is proposed for the modal reduct of a program with respect to a collection of sets of literals. This is used to define models of a program that are accepted as its world views. Among other things, the new definition adds insight into a preference held by a rational agent when deciding between models that are based on different forms of extended literals. Second, an algorithm is proposed for finding the world views of an epistemic logic program under the proposed semantics. The algorithm was designed to take advantage of current state-of-the-art ASP solvers. A proof-of- concept implementation demonstrates its effectiveness. Finally, a framework for a conformant planner written in the language of Epistemic Specifications is introduced. Combined, these show real promise for the practical use of epistemic logic programs. Included in this dissertation is a history of Epistemic Specifications and an account of many failed attempts to “fix” the semantics. Also included is a list of example programs and their associated world views. These programs represent some of the more challenging problems and are believed useful in verifying the correctness of any proposed semantics as well as algorithms and their implementations. Many of these example programs come as a result of experience gained from failed attempts to find a solution to the so-called \unintended world views" problem. vi Texas Tech University, Patrick Thor Kahl, May 2014 CHAPTER I INTRODUCTION We define a new semantics for the language of Epistemic Specifications, an ex- tension of the language of answer set programming (ASP) that was introduced in the early 1990s by Michael Gelfond [Gelfond, 1991, Gelfond, 1994]. The language of Epistemic Specifications allows for introspective reasoning through the use of modal operators K and M. A literal preceded by a modal operator is called a subjective literal. Subjective literal K p can be understood to mean "p is known." M p can be understood to mean \p may be believed." We believe the semantics proposed herein better addresses the principles that a rational agent should uphold when deciding which models of a program should be accepted and which should be rejected. Among the principles that we wish to address is the belief that a rational agent should only believe what he is forced to believe based on the rules of the program. What is meant by \based on the rules of the program" is that one or more rules combined should provide the justification—a clear argument of support|for literals being present in a belief set among the collection of belief sets in a world view of the program. The primary difficulty has been to formalize this notion of support as it applies to a literal in a program, particularly when it is caught up in a dependency cycle due to recursive rules involving subjective literals. It is hoped that the definitions presented here will provide a satisfactory semantics for the language, a quarry that has evaded us for two decades.
Recommended publications
  • Answer Set Programming: a Tour from the Basics to Advanced Development Tools and Industrial Applications
    Answer Set Programming: A tour from the basics to advanced development tools and industrial applications Nicola Leone and Francesco Ricca Department of Mathematics and Computer Science, University of Calabria, Italy leone,ricca @mat.unical.it { } Abstract. Answer Set Programming (ASP) is a powerful rule-based language for knowledge representation and reasoning that has been developed in the field of logic programming and nonmonotonic reasoning. After more than twenty years from the introduction of ASP, the theoretical properties of the language are well understood and the solving technology has become mature for practical appli- cations. In this paper, we first present the basics of the ASP language, and we then concentrate on its usage for knowledge representation and reasoning in real- world contexts. In particular, we report on the development of some industry-level applications with the ASP system DLV, and we illustrate two advanced develop- ment tools for ASP, namely ASPIDE and JDLV, which speed-up and simplify the implementation of applications. 1 Introduction Answer Set Programming (ASP) [11, 19, 30] is a powerful rule-based language for knowledge representation and reasoning that has been developed in the field of logic programming and nonmonotonic reasoning. ASP features disjunction in rule heads, non monotonic negation in rule bodies [30], aggregate atoms [16] for concise mod- eling of complex combinatorial problems, and weak constraints [12] for the declarative encoding of optimization problems. Computational problems, even of high complexity [19], can be solved in ASP by specifying a logic program, i.e., a set of logic rules, such that its answer sets correspond to solutions, and then, using an answer set solver to find such solutions [38, 34].
    [Show full text]
  • Complexity Results for Probabilistic Answer Set Programming
    International Journal of Approximate Reasoning 118 (2020) 133–154 Contents lists available at ScienceDirect International Journal of Approximate Reasoning www.elsevier.com/locate/ijar Complexity results for probabilistic answer set programming ∗ Denis Deratani Mauá a, , Fabio Gagliardi Cozman b a Institute of Mathematics and Statistics, Universidade de São Paulo, Brazil b Escola Politécnica, Universidade de São Paulo, Brazil a r t i c l e i n f o a b s t r a c t Article history: We analyze the computational complexity of probabilistic logic programming with Received 16 May 2019 constraints, disjunctive heads, and aggregates such as sum and max. We consider Received in revised form 25 October 2019 propositional programs and relational programs with bounded-arity predicates, and look Accepted 9 December 2019 at cautious reasoning (i.e., computing the smallest probability of an atom over all Available online 16 December 2019 probability models), cautious explanation (i.e., finding an interpretation that maximizes the Keywords: lower probability of evidence) and cautious maximum-a-posteriori (i.e., finding a partial Probabilistic logic programming interpretation for a set of atoms that maximizes their lower probability conditional on Answer set programming evidence) under Lukasiewicz’s credal semantics. Computational complexity © 2019 Elsevier Inc. All rights reserved. 1. Introduction Probabilities and logic programming have been combined in a variety of ways [1–8]. A particularly interesting and power- ful combination is offered by probabilistic answer set programming, which exploits the powerful knowledge representation and problem solving toolset of answer set programming [9]. Available surveys describe probabilistic logic programming in detail and go over many promising applications [10–13].
    [Show full text]
  • Answer Set Programming: a Primer?
    Answer Set Programming: A Primer? Thomas Eiter1, Giovambattista Ianni2, and Thomas Krennwallner1 1 Institut fur¨ Informationssysteme, Technische Universitat¨ Wien Favoritenstraße 9-11, A-1040 Vienna, Austria feiter,[email protected] 2 Dipartimento di Matematica, Universita´ della Calabria, I-87036 Rende (CS), Italy [email protected] Abstract. Answer Set Programming (ASP) is a declarative problem solving para- digm, rooted in Logic Programming and Nonmonotonic Reasoning, which has been gaining increasing attention during the last years. This article is a gentle introduction to the subject; it starts with motivation and follows the historical development of the challenge of defining a semantics for logic programs with negation. It looks into positive programs over stratified programs to arbitrary programs, and then proceeds to extensions with two kinds of negation (named weak and strong negation), and disjunction in rule heads. The second part then considers the ASP paradigm itself, and describes the basic idea. It shows some programming techniques and briefly overviews Answer Set solvers. The third part is devoted to ASP in the context of the Semantic Web, presenting some formalisms and mentioning some applications in this area. The article concludes with issues of current and future ASP research. 1 Introduction Over the the last years, Answer Set Programming (ASP) [1–5] has emerged as a declar- ative problem solving paradigm that has its roots in Logic Programming and Non- monotonic Reasoning. This particular way of programming, in a language which is sometimes called AnsProlog (or simply A-Prolog) [6, 7], is well-suited for modeling and (automatically) solving problems which involve common sense reasoning: it has been fruitfully applied to a range of applications (for more details, see Section 6).
    [Show full text]
  • Executable Formal Specifications in Game Development
    TIMO NUMMENMAA Executable Formal Specifications in Game Development Design, Validation and Evolution ACADEMIC DISSERTATION To be presented, with the permission of the Board of the School of Information Sciences of the University of Tampere, for public discussion in the Auditorium A1 of the Main Building, Kalevantie 4, Tampere, on November 30th, 2013, at 12 o’clock. UNIVERSITY OF TAMPERE ACADEMIC DISSERTATION University of Tampere, School of Information Sciences Finland Copyright ©2013 Tampere University Press and the author Cover design by Mikko Reinikka Acta Universitatis Tamperensis 1875 Acta Electronica Universitatis Tamperensis 1356 ISBN 978-951-44-9275-4 (print) ISBN 978-951-44-9276-1 (pdf) ISSN-L 1455-1616 ISSN 1456-954X ISSN 1455-1616 http://tampub.uta.fi Suomen Yliopistopaino Oy – Juvenes Print Tampere 2013 Abstract Games provide players with enjoyment, escapism, unique experiences and even a way to socialise. Software based games played on electronic devices such as computers and games consoles are a huge business that is still growing. New games are con- tinually developed as demand for these digital games is high. Digital games are often complex and have high requirements for quality. The complexity is especially ap- parent in complex multiplayer games and games that are constantly evolving. This complexity can be problematic in various stages of development. For example, under- standing if a design of a game works as intended can be difficult. Managing changes that need to be made to a game during its lifetime, even after its initial release, is also challenging from both a design and an implementation standpoint. In this thesis these problems are addressed by presenting a method of utilising formal methods for simulations of game designs as a way of development, commu- nication, documentation and design.
    [Show full text]
  • Answer Set Programming
    ANSWER SET PROGRAMMING Tran Cao Son Department of Computer Science New Mexico State University Las Cruces, NM 88011, USA [email protected] http://www.cs.nmsu.edu/~tson October 2005 Answer Set Programming. Acknowledgement This tutorial contains some materials from tutorials on answer set programming and on knowledge representation and logic programming from those provided by • Chitta Baral, available at www.public.asu.edu/~cbaral. • Michael Gelfond, available at www.cs.ttu.ued/~mgelfond. Tran Cao Son 1 Answer Set Programming. Introduction — Answer Set Programming Answer set programming is a new programming paradigm. It is introduced in the late 90’s and manages to attracts the intention of different groups of researchers thanks to its: • declarativeness: programs do not specify how answers are computed; • modularity: programs can be developed incrementally; • expressiveness: answer set programming can be used to solve problems in high 2 complexity classes (e.g. ΣP , Π2P , etc.) Answer set programming has been applied in several areas: reasoning about actions and changes, planning, configuration, wire routing, phylogenetic inference, semantic web, information integration, etc. Tran Cao Son 2 Answer Set Programming. Purpose • Introduce answer set programming • Provide you with some initial references, just in case • ...you get excited about answer set programming Tran Cao Son 3 Answer Set Programming. Outline • Foundation of answer set programming: logic programming with answer set semantics (syntax, semantics, early application). • Answer set programming: general ideas and examples • Application of answer set programming in – Knowledge representation – Constraint satisfaction problem – Combinatoric problems – Reasoning about action and change – Planning and diagnostic reasoning • Current issues Tran Cao Son 4 LOGIC PROGRAMMING AND ANSWER SET SEMANTICS Answer Set Programming.
    [Show full text]
  • Action Language for Foundational UML (Alf) Concrete Syntax for a UML Action Language Version 1.1 ______
    An OMG® Action Language for Foundational UML™ Publication Action Language for Foundational UML (Alf) Concrete Syntax for a UML Action Language Version 1.1 ____________________________________________________ OMG Document Number: formal/2017-07-04 Date: July 2017 Normative reference: http://www.omg.org/spec/ALF/1.1 Machine readable file(s): Normative: http://www.omg.org/spec/ALF/20170201/Alf-Syntax.xmi http://www.omg.org/spec/ALF/20170201/Alf-Library.xmi http://www.omg.org/spec/ALF/20120827/ActionLanguage-Profile.xmi ____________________________________________________ Copyright © 2010-2013 88solutions Corporation Copyright © 2013 Commissariat à l’Energie Atomique et aux Energies Alternatives (CEA) Copyright © 2010-2017 Data Access Technologies, Inc. (Model Driven Solutions) Copyright © 2010-2013 International Business Machines Copyright © 2010-2013 Mentor Graphics Corporation Copyright © 2010-2013 No Magic, Inc. Copyright © 2010-2013 Visumpoint Copyright © 2010-2017 Object Management Group USE OF SPECIFICATION - TERMS, CONDITIONS & NOTICES The material in this document details an Object Management Group specification in accordance with the terms, conditions and notices set forth below. This document does not represent a commitment to implement any portion of this specification in any company's products. The information contained in this document is subject to change without notice. LICENSES The companies listed above have granted to the Object Management Group, Inc. (OMG) a nonexclusive, royalty- free, paid up, worldwide license to copy and distribute this document and to modify this document and distribute copies of the modified version. Each of the copyright holders listed above has agreed that no person shall be deemed to have infringed the copyright in the included material of any such copyright holder by reason of having used the specification set forth herein or having conformed any computer software to the specification.
    [Show full text]
  • A Meta-Programming Technique for Debugging Answer-Set Programs∗
    A Meta-Programming Technique for Debugging Answer-Set Programs∗ Martin Gebser1, Jorg¨ Puhrer¨ 2, Torsten Schaub1, and Hans Tompits2 1 Institut fur¨ Informatik, Universitat¨ Potsdam, Germany, {gebser,torsten}@cs.uni-potsdam.de 2 Institut fur¨ Informationssysteme 184/3, Technische Universitat¨ Wien, Austria, {puehrer,tompits}@kr.tuwien.ac.at Abstract applying such an approach to ASP results in some deci- sive drawbacks, undermining the declarativity of answer- Answer-set programming (ASP) is widely recognised as a vi- able tool for declarative problem solving. However, there is set semantics. In particular, establishing canonical tracing- currently a lack of tools for developing answer-set programs. based techniques for debugging answer-set programs re- In particular, providing tools for debugging answer-set pro- quires also a canonical procedure for answer-set computa- grams has recently been identified as a crucial prerequisite tion, and programmers would have to be familiar with the al- for a wider acceptance of ASP. In this paper, we introduce a gorithm. This would lead to a shift of perspectives in which meta-programming technique for debugging in ASP. The ba- an answer-set program is degraded from a declarative prob- sic question we address is why interpretations expected to be lem description to a set of parameters for a static solving answer sets are not answer sets of the program to debug. We algorithm. Therefore, we argue for declarative debugging thus deal with finding semantical errors of programs. The ex- strategies that are independent of answer-set computation. planations provided by our method are based on an intuitive Indeed, among the few approaches dealing with debug- scheme of errors that relies on a recent characterisation of the answer-set semantics.
    [Show full text]
  • Planning in Action Language BC While Learning Action Costs for Mobile Robots
    Planning in Action Language BC while Learning Action Costs for Mobile Robots Piyush Khandelwal, Fangkai Yang, Matteo Leonetti, Vladimir Lifschitz and Peter Stone Department of Computer Science The University of Texas at Austin 2317 Speedway, Stop D9500 Austin, TX 78712, USA fpiyushk,fkyang,matteo,vl,[email protected] Abstract 1999). Furthermore, the action language BC (Lee, Lifschitz, and Yang 2013) can easily formalize recursively defined flu- The action language BC provides an elegant way of for- ents, which can be useful in robot task planning. malizing dynamic domains which involve indirect ef- fects of actions and recursively defined fluents. In com- The main contribution of this paper is a demonstration plex robot task planning domains, it may be necessary that the action language BC can be used for robot task for robots to plan with incomplete information, and rea- planning in realistic domains, that require planning in the son about indirect or recursive action effects. In this presence of missing information and indirect action effects. paper, we demonstrate how BC can be used for robot These features are necessary to completely describe many task planning to solve these issues. Additionally, action complex tasks. For instance, in a task where a robot has to costs are incorporated with planning to produce opti- collect mail intended for delivery from all building residents, mal plans, and we estimate these costs from experience the robot may need to visit a person whose location it does making planning adaptive. This paper presents the first not know. To overcome this problem, it can plan to complete application of BC on a real robot in a realistic domain, its task by asking someone else for that person’s location, which involves human-robot interaction for knowledge acquisition, optimal plan generation to minimize navi- thereby acquiring this missing information.
    [Show full text]
  • REBA: a Refinement-Based Architecture for Knowledge Representation and Reasoning in Robotics
    Journal of Artificial Intelligence Research 65 (2019) 1-94 Submitted 10/2018; published 04/2019 REBA: A Refinement-Based Architecture for Knowledge Representation and Reasoning in Robotics Mohan Sridharan [email protected] School of Computer Science University of Birmingham, UK Michael Gelfond [email protected] Department of Computer Science Texas Tech University, USA Shiqi Zhang [email protected] Department of Computer Science SUNY Binghamton, USA Jeremy Wyatt [email protected] School of Computer Science University of Birmingham, UK Abstract This article describes REBA, a knowledge representation and reasoning architecture for robots that is based on tightly-coupled transition diagrams of the domain at two different levels of granu- larity. An action language is extended to support non-boolean fluents and non-deterministic causal laws, and used to describe the transition diagrams, with the domain’s fine-resolution transition diagram being defined as a refinement of the coarse-resolution transition diagram. The coarse- resolution system description, and a history that includes prioritized defaults, are translated into an Answer Set Prolog (ASP) program. For any given goal, inference in the ASP program provides a plan of abstract actions. To implement each such abstract action, the robot automatically zooms to the part of the fine-resolution transition diagram relevant to this action. A probabilistic representa- tion of the uncertainty in sensing and actuation is then included in this zoomed fine-resolution sys- tem description, and used to construct a partially observable Markov decision process (POMDP). The policy obtained by solving the POMDP is invoked repeatedly to implement the abstract ac- tion as a sequence of concrete actions, with the corresponding observations being recorded in the coarse-resolution history and used for subsequent coarse-resolution reasoning.
    [Show full text]
  • Integrating Answer Set Programming and Constraint Logic Programming
    Integrating Answer Set Programming and Constraint Logic Programming Veena S. Mellarkod and Michael Gelfond and Yuanlin Zhang {veena.s.mellarkod,mgelfond,yzhang}@cs.ttu.edu Texas Tech University Dedicated to Victor Marek on his 65th birthday as a specification for the sets of beliefs to be held by a ra- tional reasoner associated with Π. Such sets, called an- Abstract swer sets of Π, are represented by collections of ground literals. A rule (1) is viewed as a constraint which says We introduce a knowledge representation language that if literals lk+1, . , ln belong to an answer set A of AC(C) extending the syntax and semantics of ASP Π and none of the literals ln+1, . , lm belong to A then and CR-Prolog, give some examples of its use, and A must contain at least one of the literals l , . , l . To present an algorithm, ACsolver, for computing answer 1 k sets of AC(C) programs. The algorithm does not re- form answer sets of Π, the reasoner must satisfy Π’s quire full grounding of a program and combines “clas- rules together with the rationality principle which says: sical” ASP solving methods with constraint logic pro- “Believe nothing you are not forced to believe”. gramming techniques and CR-Prolog based abduction. The AC(C) based approach often allows to solve prob- Given a computational problem P , an ASP programmer lems which are impossible to solve by more traditional • Expresses information relevant to the problem in the ASP solving techniques. We belief that further inves- language of ASP; tigation of the language and development of more effi- cient and reliable solvers for its programs can help to • Reduces P to a query Q requesting computation of substantially expand the domain of applicability of the (parts of) answer sets of Π.
    [Show full text]
  • Towards Flexible Goal-Oriented Logic Programming
    FACULTY OF SCIENCES Towards Flexible Goal-Oriented Logic Programming ir. Benoit Desouter Dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Computer Science Supervisors: prof. dr. ir. Tom Schrijvers prof. dr. ir. Marko van Dooren Department of Applied Mathematics, Computer Science and Statistics Faculty of Sciences, Ghent University ii Acknowledgments As it feels more natural to thank people in the language that we have used on a daily basis during the journey towards completing this PhD thesis, I'll use Dutch for most of the next few pages. In de eerste plaats wil ik mijn promotoren Tom en Marko bedanken. Tom, bedankt voor het geduld als ik occasioneel iets maar half begreep, en om er altijd vertrouwen in te blijven hebben. Je hebt me heel wat kansen aangereikt, waardoor ik van heel wat onderwerpen iets heb opgestoken. Marko, hoewel je er pas halverwege bijkwam, toonde je al snel interesse voor het onderwerp en heb je er vanuit je eigen expertise heel wat aan toegevoegd. Je deur stond altijd voor me open als ik even een tussentijdse statusupdate wou geven. Bedankt voor de babbels over vanalles en nog wat, en om me grondig te betrekken bij het geven van Programmeren 1. Ik heb nog heel wat bijgeleerd over objec- tori¨entatie door jouw visie, slides en codevoorbeelden. Daarnaast ook bedankt om mijn lokale LATEX-goeroe te zijn: niettegenstaande ik LATEX al tien jaar lang gebruik, heb ik voor het precies goed krijgen van deze thesis heel wat nieuwe pakketten en trucjes moeten gebruiken, met regelmatig vreemde out- put of cryptische foutmeldingen tot gevolg die ik niet altijd alleen kon oplossen | of het zou me op zijn minst veel meer tijd gekost hebben.
    [Show full text]
  • CS 252R: Advanced Topics in Programming Languages
    CS 252r: Advanced Topics in Programming Languages Eric K. Zhang [email protected] Fall 2020 Abstract These are notes for Harvard's CS 252r, a graduate seminar class on topics at the intersection of programming languages and artificial intelligence, as taught by Nada Amin1 in Fall 2020. Possible topics include: differentiable programming, interpretable AI, neuro-symbolic systems, reinforcement learning, probabilistic programming, and synthesis. Course description: We will explore programming languages for artificial intelligence. Programming languages drive the way we communicate with computers, including how we make them intelligent and reasonable. In this advanced topic course, we will look at artificial intelligence broadly construed from the point of view of programming languages. We gain clarity of semantics, algorithms and purpose. Contents 1 September 3rd, 20204 1.1 Breakout Discussion: Learning DSLs...........................4 1.2 Program Synthesis.....................................4 1.2.1 Type-Driven Specification [OZ15].........................4 1.2.2 Logic-Based Approaches..............................6 1.2.3 Neural-Guided Search [KMP+18].........................6 1.2.4 Differentiable Programming [BRNR17]......................7 1.3 Breakout Prompts: Applying PL to Improve ML....................7 2 September 8th, 20209 2.1 Probabilistic Programming [vdMPYW18]........................9 2.2 Bayesian Inference Algorithms.............................. 11 3 September 10th, 2020 13 3.1 Breakout Discussion: Perceptual Versus Symbolic...................
    [Show full text]