Database Updates in the Event Calculus
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Probabilistic Event Calculus for Event Recognition
A Probabilistic Event Calculus for Event Recognition ANASTASIOS SKARLATIDIS1;2, GEORGIOS PALIOURAS1, ALEXANDER ARTIKIS1 and GEORGE A. VOUROS2, 1Institute of Informatics and Telecommunications, NCSR “Demokritos”, 2Department of Digital Systems, University of Piraeus Symbolic event recognition systems have been successfully applied to a variety of application domains, extracting useful information in the form of events, allowing experts or other systems to monitor and respond when significant events are recognised. In a typical event recognition application, however, these systems often have to deal with a significant amount of uncertainty. In this paper, we address the issue of uncertainty in logic-based event recognition by extending the Event Calculus with probabilistic reasoning. Markov Logic Networks are a natural candidate for our logic-based formalism. However, the temporal semantics of the Event Calculus introduce a number of challenges for the proposed model. We show how and under what assumptions we can overcome these problems. Additionally, we study how probabilistic modelling changes the behaviour of the formalism, affecting its key property, the inertia of fluents. Furthermore, we demonstrate the advantages of the probabilistic Event Calculus through examples and experiments in the domain of activity recognition, using a publicly available dataset for video surveillance. Categories and Subject Descriptors: I.2.3 [Deduction and Theorem Proving]: Uncertainty, “fuzzy,” and probabilistic reasoning; I.2.4 [Knowledge Representation Formalisms and Methods]: Temporal logic; I.2.6 [Learning]: Parameter learning; I.2.10 [Vision and Scene Understanding]: Video analysis General Terms: Complex Event Processing, Event Calculus, Markov Logic Networks Additional Key Words and Phrases: Events, Probabilistic Inference, Machine Learning, Uncertainty ACM Reference Format: Anastasios Skarlatidis, Georgios Paliouras, Alexander Artikis and George A. -
Learning Effect Axioms Via Probabilistic Logic Programming
Learning Effect Axioms via Probabilistic Logic Programming Rolf Schwitter Department of Computing, Macquarie University, Sydney NSW 2109, Australia [email protected] Abstract Events have effects on properties of the world; they initiate or terminate these properties at a given point in time. Reasoning about events and their effects comes naturally to us and appears to be simple, but it is actually quite difficult for a machine to work out the relationships between events and their effects. Traditionally, effect axioms are assumed to be given for a particular domain and are then used for event recognition. We show how we can automatically learn the structure of effect axioms from example interpretations in the form of short dialogue sequences and use the resulting axioms in a probabilistic version of the Event Calculus for query answering. Our approach is novel, since it can deal with uncertainty in the recognition of events as well as with uncertainty in the relationship between events and their effects. The suggested probabilistic Event Calculus dialect directly subsumes the logic-based dialect and can be used for exact as well as a for inexact inference. 1998 ACM Subject Classification D.1.6 Logic Programming Keywords and phrases Effect Axioms, Event Calculus, Event Recognition, Probabilistic Logic Programming, Reasoning under Uncertainty Digital Object Identifier 10.4230/OASIcs.ICLP.2017.8 1 Introduction The Event Calculus [9] is a logic language for representing events and their effects and provides a logical foundation for a number of reasoning tasks [13, 24]. Over the years, different versions of the Event Calculus have been successfully used in various application domains; amongst them for temporal database updates, for robot perception and for natural language understanding [12, 13]. -
By Robert Kowalski
Edinburgh Research Explorer Review of "Computational logic and human thinking: How to be artificially intelligent" by Robert Kowalski Citation for published version: Bundy, A 2012, 'Review of "Computational logic and human thinking: How to be artificially intelligent" by Robert Kowalski', Artificial Intelligence, vol. 191-192, pp. 96-97. https://doi.org/10.1016/j.artint.2012.05.006 Digital Object Identifier (DOI): 10.1016/j.artint.2012.05.006 Link: Link to publication record in Edinburgh Research Explorer Document Version: Early version, also known as pre-print Published In: Artificial Intelligence General rights Copyright for the publications made accessible via the Edinburgh Research Explorer is retained by the author(s) and / or other copyright owners and it is a condition of accessing these publications that users recognise and abide by the legal requirements associated with these rights. Take down policy The University of Edinburgh has made every reasonable effort to ensure that Edinburgh Research Explorer content complies with UK legislation. If you believe that the public display of this file breaches copyright please contact [email protected] providing details, and we will remove access to the work immediately and investigate your claim. Download date: 27. Sep. 2021 Review of \Computational Logic and Human Thinking: How to be Artificially Intelligent" by Robert Kowalski Alan Bundy School of Informatics, University of Edinburgh, Informatics Forum, 10 Crichton St, Edinburgh, EH8 9AB, Scotland 1 August 2012 Abstract This is a review of the book \Computational Logic and Human Thinking: How to be Artificially Intelligent" by Robert Kowalski. Keywords: computational logic, human thinking, book review. -
A Logic-Based Calculus of Events
10 A Logic-based Calculus of Events ROBERT KOWALSKI and MAREK SERGOT 1 introduction Formal Logic can be used to represent knowledge of many kinds for many purposes. It can be used to formalize programs, program specifications, databases, legislation, and natural language in general. For many such applications of logic a representation of time is necessary. Although there have been several attempts to formalize the notion of time in classical first-order logic, it is still widely believed that classical logic is not adequate for the representation of time and that some form of non-classical Temporal Logic is needed. In this paper, we shall outline a treatment of time, based on the notion of event, formalized in the Horn clause subset of classical logic augmented with negation as failure. The resulting formalization is executable as a logic program. We use the term ‘‘event calculus’’ to relate it to the well-known ‘‘situation calculus’’ (McCarthy and Hayes 1969). The main difference between the two is conceptual: the situation calculus deals with global states whereas the event calculus deals with local events and time periods. Like the event calculus, the situation calculus can be formalized by means of Horn clauses augmented with negation by failure (Kowalski 1979). The main intended applications investigated in this paper are the updating of data- bases and narrative understanding. In order to treat both cases uniformly we have taken the view that an update consists of the addition of new knowledge to a knowledge base. The effect of explicit deletion of information in conventional databases is obtained without deletion by adding new knowledge about the end of the period of time for which the information holds. -
7. Logic Programming in Prolog
Artificial Intelligence 7. Logic Programming in Prolog Prof. Bojana Dalbelo Baˇsi´c Assoc. Prof. Jan Snajderˇ University of Zagreb Faculty of Electrical Engineering and Computing Academic Year 2019/2020 Creative Commons Attribution{NonCommercial{NoDerivs 3.0 v1.10 Dalbelo Baˇsi´c, Snajderˇ (UNIZG FER) AI { Logic programming AY 2019/2020 1 / 38 Outline 1 Logic programming and Prolog 2 Inference on Horn clauses 3 Programming in Prolog 4 Non-declarative aspects of Prolog Dalbelo Baˇsi´c, Snajderˇ (UNIZG FER) AI { Logic programming AY 2019/2020 2 / 38 Outline 1 Logic programming and Prolog 2 Inference on Horn clauses 3 Programming in Prolog 4 Non-declarative aspects of Prolog Dalbelo Baˇsi´c, Snajderˇ (UNIZG FER) AI { Logic programming AY 2019/2020 3 / 38 Logic programming Logic programming: use of logic inference as a way of programming Main idea: define the problem in terms of logic formulae, then let the computer do the problem solving (program execution = inference) This is a typical declarative programming approach: express the logic of computation, don't bother with the control flow We focus on the description of the problem (declarative), rather than on how the program is executed (procedural) However, we still need some flow control mechanism, thus: Algorithm = Logic + Control Different from automated theorem proving because: 1 explicit control flow is hard-wired into the program 2 not the full expressivity of FOL is supported Dalbelo Baˇsi´c, Snajderˇ (UNIZG FER) AI { Logic programming AY 2019/2020 4 / 38 Refresher: Declarative programming -
Modeling and Reasoning in Event Calculus Using Goal-Directed Constraint Answer Set Programming?
Modeling and Reasoning in Event Calculus Using Goal-Directed Constraint Answer Set Programming? Joaqu´ınArias1;2, Zhuo Chen3, Manuel Carro1;2, and Gopal Gupta3 1 IMDEA Software Institute 2 Universidad Polit´ecnicade Madrid [email protected], [email protected],upm.esg 3 University of Texas at Dallas fzhuo.chen,[email protected] Abstract. Automated commonsense reasoning is essential for building human-like AI systems featuring, for example, explainable AI. Event Cal- culus (EC) is a family of formalisms that model commonsense reasoning with a sound, logical basis. Previous attempts to mechanize reasoning using EC faced difficulties in the treatment of the continuous change in dense domains (e.g., time and other physical quantities), constraints among variables, default negation, and the uniform application of differ- ent inference methods, among others. We propose the use of s(CASP), a query-driven, top-down execution model for Predicate Answer Set Pro- gramming with Constraints, to model and reason using EC. We show how EC scenarios can be naturally and directly encoded in s(CASP) and how its expressiveness makes it possible to perform deductive and abductive reasoning tasks in domains featuring, for example, constraints involving dense time and fluents. Keywords: ASP · goal-directed · Event Calculus · Constraints 1 Introduction The ability to model continuous characteristics of the world is essential for Com- monsense Reasoning (CR) in many domains that require dealing with continu- ous change: time, the height of a falling object, the gas level of a car, the water level in a sink, etc. Event Calculus (EC) is a formalism based on many-sorted predicate logic [13, 23] that can represent continuous change and capture the commonsense law of inertia, whose modeling is a pervasive problem in CR. -
Variants of the Event Calculus
See discussions, stats, and author profiles for this publication at: https://www.researchgate.net/publication/2519366 Variants of the Event Calculus Article · October 2000 Source: CiteSeer CITATIONS READS 42 88 2 authors: Fariba Sadri Robert Kowalski Imperial College London Imperial College London 114 PUBLICATIONS 2,761 CITATIONS 154 PUBLICATIONS 12,446 CITATIONS SEE PROFILE SEE PROFILE Some of the authors of this publication are also working on these related projects: Non-modal deontic logic View project SOCS - Societies of Computees View project All content following this page was uploaded by Fariba Sadri on 12 September 2014. The user has requested enhancement of the downloaded file. Variants of the Event Calculus ICLP 95 Fariba Sadri and Robert Kowalski Department of Computing, Imperial College of Science, Technology and Medicine, 180, Queens Gate, London SW7 2BZ [email protected] [email protected] Abstract The event calculus was proposed as a formalism for reasoning about time and events. Through the years, however, a much simpler variant (SEC) of the original calculus (EC) has proved more useful in practice. We argue that EC has the advantage of being more general than SEC, but the disadvantage of being too complex and in some cases erroneous. SEC has the advantage of simplicity, but the disadvantage of being too specialised. This paper has two main objectives. The first is to show the formal relationship between the two calculi. The second is to propose a new variant (NEC) of the event calculus, which is essentially SEC in iff-form augmented with integrity constraints, and to argue that NEC combines the generality of EC with the simplicity of SEC. -
Inconsistency Robustness for Logic Programs Carl Hewitt
Inconsistency Robustness for Logic Programs Carl Hewitt To cite this version: Carl Hewitt. Inconsistency Robustness for Logic Programs. Inconsistency Robustness, 2015, 978-1- 84890-159. hal-01148496v6 HAL Id: hal-01148496 https://hal.archives-ouvertes.fr/hal-01148496v6 Submitted on 27 Apr 2016 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. Copyright Inconsistency Robustness for Logic Programs Carl Hewitt This article is dedicated to Alonzo Church and Stanisław Jaśkowski Abstract This article explores the role of Inconsistency Robustness in the history and theory of Logic Programs. Inconsistency Robustness has been a continually recurring issue in Logic Programs from the beginning including Church's system developed in the early 1930s based on partial functions (defined in the lambda calculus) that he thought would allow development of a general logic without the kind of paradoxes that had plagued earlier efforts by Frege, etc.1 Planner [Hewitt 1969, 1971] was a kind of hybrid between the procedural and logical paradigms in that it featured a procedural embedding of logical sentences in that an implication of the form (p implies q) can be procedurally embedded in the following ways: Forward chaining When asserted p, Assert q When asserted q, Assert p Backward chaining When goal q, SetGoal p When goal p, SetGoal q Developments by different research groups in the fall of 1972 gave rise to a controversy over Logic Programs that persists to this day in the form of following alternatives: 1. -
Development of Logic Programming: What Went Wrong, What Was Done About It, and What It Might Mean for the Future
Development of Logic Programming: What went wrong, What was done about it, and What it might mean for the future Carl Hewitt at alum.mit.edu try carlhewitt Abstract follows: A class of entities called terms is defined and a term is an expression. A sequence of expressions is an Logic Programming can be broadly defined as “using logic expression. These expressions are represented in the to deduce computational steps from existing propositions” machine by list structures [Newell and Simon 1957]. (although this is somewhat controversial). The focus of 2. Certain of these expressions may be regarded as this paper is on the development of this idea. declarative sentences in a certain logical system which Consequently, it does not treat any other associated topics will be analogous to a universal Post canonical system. related to Logic Programming such as constraints, The particular system chosen will depend on abduction, etc. programming considerations but will probably have a The idea has a long development that went through single rule of inference which will combine substitution many twists in which important questions turned out to for variables with modus ponens. The purpose of the combination is to avoid choking the machine with special have surprising answers including the following: cases of general propositions already deduced. Is computation reducible to logic? 3. There is an immediate deduction routine which when Are the laws of thought consistent? given a set of premises will deduce a set of immediate This paper describes what went wrong at various points, conclusions. Initially, the immediate deduction routine what was done about it, and what it might mean for the will simply write down all one-step consequences of the future of Logic Programming. -
Monitoring Business Constraints with the Event Calculus
Universit`adegli Studi di Bologna DEIS Monitoring Business Constraints with the Event Calculus MARCO MONTALI FABRIZIO M. MAGGI FEDERICO CHESANI PAOLA MELLO WIL M. P. VAN DER AALST June 4, 2012 DEIS Technical Report no. DEIS-LIA-002-11 LIA Series no. 100 Monitoring Business Constraints with the Event Calculus MARCO MONTALI 1 FABRIZIO M. MAGGI 2 FEDERICO CHESANI 3 PAOLA MELLO 3 WIL M. P. VAN DER AALST 2 1Free University of Bozen-Bolzano Research Centre for Knowledge and Data (KRDB) [email protected] 2Eindhoven University of Technology Department of Mathematics & Computer Science ff.m.maggi j [email protected] 3University of Bologna Department of Electronics, Computer Science and Systems (DEIS) ffederico.chesani j [email protected] June 4, 2012 Abstract. Today, large business processes are composed of smaller, au- tonomous, interconnected sub-systems, achieving modularity and robustness. Quite often, these large processes comprise software components as well as hu- man actors, they face highly dynamic environments, and their sub-systems are updated and evolve independently of each other. Due to their dynamic nature and complexity, it might be difficult, if not impossible, to ensure at design-time that such systems will always exhibit the desired/expected behaviours. This in turn trigger the need for runtime verification and monitoring facilities. These are needed to check whether the actual behaviour complies with expected busi- ness constraints. In this work we present mobucon, a novel monitoring framework that tracks streams of events and continuously determines the state of business constraints. In mobucon, business constraints are defined using ConDec, a declarative pro- cess modelling language. -
Logic for Problem Solving
LOGIC FOR PROBLEM SOLVING Robert Kowalski Imperial College London 12 October 2014 PREFACE It has been fascinating to reflect and comment on the way the topics addressed in this book have developed over the past 40 years or so, since the first version of the book appeared as lecture notes in 1974. Many of the developments have had an impact, not only in computing, but also more widely in such fields as math- ematical, philosophical and informal logic. But just as interesting (and perhaps more important) some of these developments have taken different directions from the ones that I anticipated at the time. I have organised my comments, chapter by chapter, at the end of the book, leaving the 1979 text intact. This should make it easier to compare the state of the subject in 1979 with the subsequent developments that I refer to in my commentary. Although the main purpose of the commentary is to look back over the past 40 years or so, I hope that these reflections will also help to point the way to future directions of work. In particular, I remain confident that the logic-based approach to knowledge representation and problem solving that is the topic of this book will continue to contribute to the improvement of both computer and human intelligence for a considerable time into the future. SUMMARY When I was writing this book, I was clear about the problem-solving interpretation of Horn clauses, and I knew that Horn clauses needed to be extended. However, I did not know what extensions would be most important. -
Copyright by Amelia J. Harrison 2017
Copyright by Amelia J. Harrison 2017 The Dissertation Committee for Amelia J. Harrison certifies that this is the approved version of the following dissertation: Formal Methods for Answer Set Programming Committee: Vladimir Lifschitz, Supervisor Robert S. Boyer Isil Dillig Warren A. Hunt, Jr. Torsten Schaub Formal Methods for Answer Set Programming by Amelia J. Harrison Dissertation Presented to the Faculty of the Graduate School of The University of Texas at Austin in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy The University of Texas at Austin December 2017 To Grandmother Betsy Acknowledgments It’s not an exaggeration to say that were it not for many people other than myself this document would not exist. This is by no means an exhaustive list. I’ve had the opportunity to collaborate with many wonderful and talented colleagues. Among them are Daniel Bailey, Martin Gebser, Yuliya Lierler, Roland Kaminski, Julian Michael, David Pearce, Dhananjay Raju, Mirek Truszczynski, Agustin Valverde, and Fangkai Yang. Without friends to help maintain balance, it’s entirely unclear if I would have been able to see this through. Backyard barbecues and Game of Thrones viewing parties have been essential. I feel especially lucky to have met early on, and shared much of this journey with Hannah Alpert-Abrams and Kate Mesh. My family has been a constant source of reassurance and has helped me nav- igate many of the inevitable bouts of self-doubt that arise in the process of a Ph.D. This includes my parents, Jack and Betsy Harrison, who have always unequivocally encouraged me to find my own path.