scholarly journals Making Use of Advances in Answer-Set Programming for Abstract Argumentation Systems

2013 ◽  
pp. 114-133 ◽  
Author(s):  
Wolfgang Dvořák ◽  
Sarah Alice Gaggl ◽  
Johannes Peter Wallner ◽  
Stefan Woltran
Keyword(s):  
Answer Set Programming ◽  
Abstract Argumentation ◽  
Argumentation Systems ◽  
Answer Set

scholarly journals Improved answer-set programming encodings for abstract argumentation

2015 ◽  
Vol 15 (4-5) ◽  
pp. 434-448 ◽  
Author(s):  
SARAH A. GAGGL ◽  
NORBERT MANTHEY ◽  
ALESSANDRO RONCA ◽  
JOHANNES P. WALLNER ◽  
STEFAN WOLTRAN
Keyword(s):  
Answer Set Programming ◽  
Efficient Solutions ◽  
Crucial Step ◽  
Abstract Argumentation ◽  
Argumentation Systems ◽  
Answer Set

AbstractThe design of efficient solutions for abstract argumentation problems is a crucial step towards advanced argumentation systems. One of the most prominent approaches in the literature is to use Answer-Set Programming (ASP) for this endeavor. In this paper, we present new encodings for three prominent argumentation semantics using the concept of conditional literals in disjunctions as provided by the ASP-system clingo. Our new encodings are not only more succinct than previous versions, but also outperform them on standard benchmarks.

scholarly journals Solving Advanced Argumentation Problems with Answer Set Programming

2020 ◽  
Vol 20 (3) ◽  
pp. 391-431
Author(s):  
GERHARD BREWKA ◽  
MARTIN DILLER ◽  
GEORG HEISSENBERGER ◽  
THOMAS LINSBICHLER ◽  
STEFAN WOLTRAN
Keyword(s):  
Answer Set Programming ◽  
Polynomial Hierarchy ◽  
Abstract Argumentation ◽  
The Third ◽  
Labeled Graphs ◽  
The Relationship ◽  
Answer Set

AbstractPowerful formalisms for abstract argumentation have been proposed, among them abstract dialectical frameworks (ADFs) that allow for a succinct and flexible specification of the relationship between arguments and the GRAPPA framework which allows argumentation scenarios to be represented as arbitrary edge-labeled graphs. The complexity of ADFs and GRAPPA is located beyond NP and ranges up to the third level of the polynomial hierarchy. The combined complexity of Answer Set Programming (ASP) exactly matches this complexity when programs are restricted to predicates of bounded arity. In this paper, we exploit this coincidence and present novel efficient translations from ADFs and GRAPPA to ASP. More specifically, we provide reductions for the five main ADF semantics of admissible, complete, preferred, grounded, and stable interpretations, and exemplify how these reductions need to be adapted for GRAPPA for the admissible, complete, and preferred semantics.

scholarly journals Reasoning over Assumption-Based Argumentation Frameworks via Direct Answer Set Programming Encodings

2019 ◽  
Vol 33 ◽  
pp. 2938-2945 ◽  
Author(s):  
Tuomo Lehtonen ◽  
Johannes P. Wallner ◽  
Matti Järvisalo
Keyword(s):  
Answer Set Programming ◽  
Direct Approach ◽  
Problem Complexity ◽  
New Approach ◽  
Abstract Argumentation ◽  
Reasoning Systems ◽  
Complexity Results ◽  
Empirical Performance ◽  
Direct Answer ◽  
Answer Set

Focusing on assumption-based argumentation (ABA) as a central structured formalism to AI argumentation, we propose a new approach to reasoning in ABA with and without preferences. While previous approaches apply either specialized algorithms or translate ABA reasoning to reasoning over abstract argumentation frameworks, we develop a direct approach by encoding ABA reasoning tasks in answer set programming. This significantly improves on the empirical performance of current ABA reasoning systems. We also give new complexity results for reasoning in ABA+, suggesting that the integration of preferential information into ABA results in increased problem complexity for several central argumentation semantics.

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

Answer set programming for collaborative housekeeping robotics: representation, reasoning, and execution

2012 ◽  
Vol 5 (4) ◽  
pp. 275-291 ◽  
Author(s):  
Esra Erdem ◽  
Erdi Aker ◽  
Volkan Patoglu
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.

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