scholarly journals Knowledge-Assisted Reasoning of Model-Augmented System Requirements with Event Calculus and Goal-Directed Answer Set Programming

2021 ◽  
Vol 344 ◽  
pp. 79-90
Author(s):  
Brendan Hall ◽  
Sarat Chandra Varanasi ◽  
Jan Fiedor ◽  
Joaquín Arias ◽  
Kinjal Basu ◽  
...  
Keyword(s):  
Answer Set Programming ◽  
Event Calculus ◽  
Augmented System ◽  
System Requirements ◽  
Answer Set

scholarly journals Online Learning Probabilistic Event Calculus Theories in Answer Set Programming

2021 ◽  
pp. 1-25
Author(s):  
NIKOS KATZOURIS ◽  
GEORGIOS PALIOURAS ◽  
ALEXANDER ARTIKIS
Keyword(s):  
Probabilistic Reasoning ◽  
Answer Set Programming ◽  
Predictive Performance ◽  
Theory And Practice ◽  
Markov Logic ◽  
Event Calculus ◽  
Maritime Surveillance ◽  
Novel Approach ◽  
Combine Uncertainty ◽  
Answer Set

Abstract Complex Event Recognition (CER) systems detect event occurrences in streaming time-stamped input using predefined event patterns. Logic-based approaches are of special interest in CER, since, via Statistical Relational AI, they combine uncertainty-resilient reasoning with time and change, with machine learning, thus alleviating the cost of manual event pattern authoring. We present a system based on Answer Set Programming (ASP), capable of probabilistic reasoning with complex event patterns in the form of weighted rules in the Event Calculus, whose structure and weights are learnt online. We compare our ASP-based implementation with a Markov Logic-based one and with a number of state-of-the-art batch learning algorithms on CER data sets for activity recognition, maritime surveillance and fleet management. Our results demonstrate the superiority of our novel approach, both in terms of efficiency and predictive performance. This paper is under consideration for publication in Theory and Practice of Logic Programming (TPLP).

scholarly journals Modeling and Reasoning in Event Calculus using Goal-Directed Constraint Answer Set Programming

2021 ◽  
pp. 1-30
Author(s):  
JOAQUÍN ARIAS ◽  
MANUEL CARRO ◽  
ZHUO CHEN ◽  
GOPAL GUPTA
Keyword(s):  
Answer Set Programming ◽  
Abductive Reasoning ◽  
Commonsense Reasoning ◽  
Continuous Change ◽  
Top Down ◽  
Event Calculus ◽  
Execution Model ◽  
Inference Methods ◽  
Physical Quantities ◽  
Answer Set

Abstract Automated commonsense reasoning (CR) is essential for building human-like AI systems featuring, for example, explainable AI. Event calculus (EC) is a family of formalisms that model CR 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 different inference methods, among others. We propose the use of s(CASP), a query-driven, top-down execution model for Predicate Answer Set Programming with Constraints, to model and reason using EC. We show how EC scenarios can be naturally and directly encoded in s(CASP) and how it enables deductive and abductive reasoning tasks in domains featuring constraints involving both dense time and dense fluents.

scholarly journals Open answer set programming with guarded programs

2008 ◽  
Vol 9 (4) ◽  
pp. 1-53 ◽  
Author(s):  
Stijn Heymans ◽  
Davy Van Nieuwenborgh ◽  
Dirk Vermeir
Keyword(s):  
Answer Set Programming ◽  
Answer Set

scholarly journals Exhaustively characterizing feasible logic models of a signaling network using Answer Set Programming

2013 ◽  
Vol 29 (18) ◽  
pp. 2320-2326 ◽  
Author(s):  
Carito Guziolowski ◽  
Santiago Videla ◽  
Federica Eduati ◽  
Sven Thiele ◽  
Thomas Cokelaer ◽  
...  
Keyword(s):  
Answer Set Programming ◽  
Signaling Network ◽  
Logic Models ◽  
Answer Set

CoreALMlib: An library translated from the Component Library

2016 ◽  
Vol 16 (5-6) ◽  
pp. 800-816 ◽  
Author(s):  
DANIELA INCLEZAN
Keyword(s):  
Natural Language ◽  
Answer Set Programming ◽  
Natural Language Understanding ◽  
Inference Engine ◽  
Lexical Resources ◽  
Language Understanding ◽  
Commonsense Knowledge ◽  
Component Library ◽  
Action Language ◽  
Answer Set

AbstractThis paper presents CoreALMlib, an $\mathscr{ALM}$ library of commonsense knowledge about dynamic domains. The library was obtained by translating part of the Component Library (CLib) into the modular action language $\mathscr{ALM}$. CLib consists of general reusable and composable commonsense concepts, selected based on a thorough study of ontological and lexical resources. Our translation targets CLibstates (i.e., fluents) and actions. The resulting $\mathscr{ALM}$ library contains the descriptions of 123 action classes grouped into 43 reusable modules that are organized into a hierarchy. It is made available online and of interest to researchers in the action language, answer-set programming, and natural language understanding communities. We believe that our translation has two main advantages over its CLib counterpart: (i) it specifies axioms about actions in a more elaboration tolerant and readable way, and (ii) it can be seamlessly integrated with ASP reasoning algorithms (e.g., for planning and postdiction). In contrast, axioms are described in CLib using STRIPS-like operators, and CLib's inference engine cannot handle planning nor postdiction.

scholarly journals Grounding and Solving in Answer Set Programming

AI Magazine ◽  
2016 ◽  
Vol 37 (3) ◽  
pp. 25-32 ◽  
Author(s):  
Benjamin Kaufmann ◽  
Nicola Leone ◽  
Simona Perri ◽  
Torsten Schaub
Keyword(s):  
Problem Solving ◽  
Propositional Logic ◽  
Logic Program ◽  
Answer Set Programming ◽  
Key Issues ◽  
Answer Sets ◽  
Answer Set

Answer set programming is a declarative problem solving paradigm that rests upon a workflow involving modeling, grounding, and solving. While the former is described by Gebser and Schaub (2016), we focus here on key issues in grounding, or how to systematically replace object variables by ground terms in a effective way, and solving, or how to compute the answer sets of a propositional logic program obtained by grounding.

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