GLORIA

GEOMAR Library Ocean Research Information Access

X

Your email was sent successfully. Check your inbox.

An error occurred while sending the email. Please try again.

Proceed reservation?

Export
  • 1
    Online Resource
    Online Resource
    Methods for solving reasoning problems in abstract argumentation – A survey
    Elsevier BV ; 2015
    In:  Artificial Intelligence Vol. 220 ( 2015-03), p. 28-63
    Details
    In: Artificial Intelligence, Elsevier BV, Vol. 220 ( 2015-03), p. 28-63
    Type of Medium: Online Resource
    ISSN: 0004-3702
    URL: Article
    DOI: 10.1016/j.artint.2014.11.008
    RVK:
    EQ 1000
    RVK:
    SA 2940
    Language: English
    Publisher: Elsevier BV
    Publication Date: 2015
    detail.hit.zdb_id: 1468341-6
    detail.hit.zdb_id: 218797-8
    Location Call Number Limitation Availability
    BibTip Others were also interested in ...
    Online Resource
  • 2
    Online Resource
    Online Resource
    Structural constraints for dynamic operators in abstract argumentation
    IOS Press ; 2020
    In:  Argument & Computation Vol. 11, No. 1-2 ( 2020-05-19), p. 151-190
    Details
    In: Argument & Computation, IOS Press, Vol. 11, No. 1-2 ( 2020-05-19), p. 151-190
    Type of Medium: Online Resource
    ISSN: 1946-2174 , 1946-2166
    URL: Article
    DOI: 10.3233/AAC-190471
    Language: Unknown
    Publisher: IOS Press
    Publication Date: 2020
    detail.hit.zdb_id: 2553676-X
    Location Call Number Limitation Availability
    BibTip Others were also interested in ...
    Online Resource
  • 3
    Online Resource
    Online Resource
    Abstract solvers for Dung’s argumentation frameworks
    IOS Press ; 2018
    In:  Argument & Computation Vol. 9, No. 1 ( 2018-01-31), p. 41-72
    Details
    In: Argument & Computation, IOS Press, Vol. 9, No. 1 ( 2018-01-31), p. 41-72
    Type of Medium: Online Resource
    ISSN: 1946-2174 , 1946-2166
    URL: Article
    DOI: 10.3233/AAC-170031
    Language: Unknown
    Publisher: IOS Press
    Publication Date: 2018
    detail.hit.zdb_id: 2553676-X
    Location Call Number Limitation Availability
    BibTip Others were also interested in ...
    Online Resource
  • 4
    Online Resource
    Online Resource
    Complexity Results and Algorithms for Extension Enforcement in Abstract Argumentation
    AI Access Foundation ; 2017
    In:  Journal of Artificial Intelligence Research Vol. 60 ( 2017-09-13), p. 1-40
    Details
    In: Journal of Artificial Intelligence Research, AI Access Foundation, Vol. 60 ( 2017-09-13), p. 1-40
    Abstract: Argumentation is an active area of modern artificial intelligence (AI) research, with connections to a range of fields, from computational complexity theory and knowledge representation and reasoning to philosophy and social sciences, as well as application-oriented work in domains such as legal reasoning, multi-agent systems, and decision support. Argumentation frameworks (AFs) of abstract argumentation have become the graph-based formal model of choice for many approaches to argumentation in AI, with semantics defining sets of jointly acceptable arguments, i.e., extensions. Understanding the dynamics of AFs has been recently recognized as an important topic in the study of argumentation in AI. In this work, we focus on the so-called extension enforcement problem in abstract argumentation as a recently proposed form of argumentation dynamics. We provide a nearly complete computational complexity map of argument-fixed extension enforcement under various major AF semantics, with results ranging from polynomial-time algorithms to completeness for the second level of the polynomial hierarchy. Complementing the complexity results, we propose algorithms for NP-hard extension enforcement based on constraint optimization under the maximum satisfiability (MaxSAT) paradigm. Going beyond NP, we propose novel MaxSAT-based counterexample-guided abstraction refinement procedures for the second-level complete problems and present empirical results on a prototype system constituting the first approach to extension enforcement in its generality.
    Type of Medium: Online Resource
    ISSN: 1076-9757
    URL: Article
    DOI: 10.1613/jair.5415
    Language: Unknown
    Publisher: AI Access Foundation
    Publication Date: 2017
    detail.hit.zdb_id: 1468362-3
    Location Call Number Limitation Availability
    BibTip Others were also interested in ...
    Online Resource
  • 5
    Online Resource
    Online Resource
    Declarative Algorithms and Complexity Results for Assumption-Based Argumentation
    AI Access Foundation ; 2021
    In:  Journal of Artificial Intelligence Research Vol. 71 ( 2021-06-23), p. 265-318
    Details
    In: Journal of Artificial Intelligence Research, AI Access Foundation, Vol. 71 ( 2021-06-23), p. 265-318
    Abstract: The study of computational models for argumentation is a vibrant area of artificial intelligence and, in particular, knowledge representation and reasoning research. Arguments most often have an intrinsic structure made explicit through derivations from more basic structures. Computational models for structured argumentation enable making the internal structure of arguments explicit. Assumption-based argumentation (ABA) is a central structured formalism for argumentation in AI. In this article, we make both algorithmic and complexity-theoretic advances in the study of ABA. In terms of algorithms, we propose a new approach to reasoning in a commonly studied fragment of ABA (namely the logic programming fragment) with and without preferences. While previous approaches to reasoning over ABA frameworks apply either specialized algorithms or translate ABA reasoning to reasoning over abstract argumentation frameworks, we develop a direct declarative approach to ABA reasoning by encoding ABA reasoning tasks in answer set programming. We show via an extensive empirical evaluation that our approach significantly improves on the empirical performance of current ABA reasoning systems. In terms of computational complexity, while the complexity of reasoning over ABA frameworks is well-understood, the complexity of reasoning in the ABA+ formalism integrating preferences into ABA is currently not fully established. Towards bridging this gap, our results suggest that the integration of preferential information into ABA via so-called reverse attacks results in increased problem complexity for several central argumentation semantics.
    Type of Medium: Online Resource
    ISSN: 1076-9757
    URL: Article
    DOI: 10.1613/jair.1.12479
    Language: Unknown
    Publisher: AI Access Foundation
    Publication Date: 2021
    detail.hit.zdb_id: 1468362-3
    Location Call Number Limitation Availability
    BibTip Others were also interested in ...
    Online Resource
  • 6
    Online Resource
    Online Resource
    Advanced algorithms for abstract dialectical frameworks based on complexity analysis of subclasses and SAT solving
    Elsevier BV ; 2022
    In:  Artificial Intelligence Vol. 307 ( 2022-06), p. 103697-
    Details
    In: Artificial Intelligence, Elsevier BV, Vol. 307 ( 2022-06), p. 103697-
    Type of Medium: Online Resource
    ISSN: 0004-3702
    URL: Article
    DOI: 10.1016/j.artint.2022.103697
    RVK:
    EQ 1000
    RVK:
    SA 2940
    Language: English
    Publisher: Elsevier BV
    Publication Date: 2022
    detail.hit.zdb_id: 1468341-6
    detail.hit.zdb_id: 218797-8
    Location Call Number Limitation Availability
    BibTip Others were also interested in ...
    Online Resource
  • 7
    Online Resource
    Online Resource
    Strong Explanations in Abstract Argumentation
    Association for the Advancement of Artificial Intelligence (AAAI) ; 2021
    In:  Proceedings of the AAAI Conference on Artificial Intelligence Vol. 35, No. 7 ( 2021-05-18), p. 6496-6504
    Details
    In: Proceedings of the AAAI Conference on Artificial Intelligence, Association for the Advancement of Artificial Intelligence (AAAI), Vol. 35, No. 7 ( 2021-05-18), p. 6496-6504
    Abstract: Abstract argumentation constitutes both a major research strand and a key approach that provides the core reasoning engine for a multitude of formalisms in computational argumentation in AI. Reasoning in abstract argumentation is carried out by viewing arguments and their relationships as abstract entities, with argumentation frameworks (AFs) being the most commonly used abstract formalism. Argumentation semantics then drive the reasoning by specifying formal criteria on which sets of arguments, called extensions, can be deemed as jointly acceptable. Such extensions provide a basic way of explaining argumentative acceptance. Inspired by recent research, we present a more general class of explanations: in this paper we propose and study so-called strong explanations for explaining argumentative acceptance in AFs. A strong explanation is a set of arguments such that a target set of arguments is acceptable in each subframework containing the explaining set. We formally show that strong explanations form a larger class than extensions, in particular giving the possibility of having smaller explanations. Moreover, assuming basic properties, we show that any explanation strategy, broadly construed, is a strong explanation. We show that the increase in variety of strong explanations comes with a computational trade-off: we provide an in-depth analysis of the associated complexity, showing a jump in the polynomial hierarchy compared to extensions.
    Type of Medium: Online Resource
    ISSN: 2374-3468 , 2159-5399
    URL: Article
    DOI: 10.1609/aaai.v35i7.16805
    Language: Unknown
    Publisher: Association for the Advancement of Artificial Intelligence (AAAI)
    Publication Date: 2021
    Location Call Number Limitation Availability
    BibTip Others were also interested in ...
    Online Resource
  • 8
    Online Resource
    Online Resource
    Reasoning over Assumption-Based Argumentation Frameworks via Direct Answer Set Programming Encodings
    Association for the Advancement of Artificial Intelligence (AAAI) ; 2019
    In:  Proceedings of the AAAI Conference on Artificial Intelligence Vol. 33, No. 01 ( 2019-07-17), p. 2938-2945
    Details
    In: Proceedings of the AAAI Conference on Artificial Intelligence, Association for the Advancement of Artificial Intelligence (AAAI), Vol. 33, No. 01 ( 2019-07-17), p. 2938-2945
    Abstract: 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.
    Type of Medium: Online Resource
    ISSN: 2374-3468 , 2159-5399
    URL: Article
    DOI: 10.1609/aaai.v33i01.33012938
    Language: Unknown
    Publisher: Association for the Advancement of Artificial Intelligence (AAAI)
    Publication Date: 2019
    Location Call Number Limitation Availability
    BibTip Others were also interested in ...
    Online Resource
  • 9
    Online Resource
    Online Resource
    Proportional Belief Merging
    Association for the Advancement of Artificial Intelligence (AAAI) ; 2020
    In:  Proceedings of the AAAI Conference on Artificial Intelligence Vol. 34, No. 03 ( 2020-04-03), p. 2822-2829
    Details
    In: Proceedings of the AAAI Conference on Artificial Intelligence, Association for the Advancement of Artificial Intelligence (AAAI), Vol. 34, No. 03 ( 2020-04-03), p. 2822-2829
    Abstract: In this paper we introduce proportionality to belief merging. Belief merging is a framework for aggregating information presented in the form of propositional formulas, and it generalizes many aggregation models in social choice. In our analysis, two incompatible notions of proportionality emerge: one similar to standard notions of proportionality in social choice, the other more in tune with the logic-based merging setting. Since established merging operators meet neither of these proportionality requirements, we design new proportional belief merging operators. We analyze the proposed operators against established rationality postulates, finding that current approaches to proportionality from the field of social choice are, at their core, incompatible with standard rationality postulates in belief merging. We provide characterization results that explain the underlying conflict, and provide a complexity analysis of our novel operators.
    Type of Medium: Online Resource
    ISSN: 2374-3468 , 2159-5399
    URL: Article
    DOI: 10.1609/aaai.v34i03.5671
    Language: Unknown
    Publisher: Association for the Advancement of Artificial Intelligence (AAAI)
    Publication Date: 2020
    Location Call Number Limitation Availability
    BibTip Others were also interested in ...
    Online Resource
  • 10
    Online Resource
    Online Resource
    Ranking Sets of Defeasible Elements in Preferential Approaches to Structured Argumentation: Postulates, Relations, and Characterizations
    Association for the Advancement of Artificial Intelligence (AAAI) ; 2021
    In:  Proceedings of the AAAI Conference on Artificial Intelligence Vol. 35, No. 7 ( 2021-05-18), p. 6435-6443
    Details
    In: Proceedings of the AAAI Conference on Artificial Intelligence, Association for the Advancement of Artificial Intelligence (AAAI), Vol. 35, No. 7 ( 2021-05-18), p. 6435-6443
    Abstract: Preferences play a key role in computational argumentation in AI, as they reflect various notions of argument strength vital for the representation of argumentation. Within central formal approaches to structured argumentation, preferential approaches are applied by lifting preferences over defeasible elements to rankings over sets of defeasible elements, in order to be able to compare the relative strength of two arguments and their respective defeasible constituents. To overcome the current gap in the scientific landscape, we give in this paper a general study of the critical component of lifting operators in structured argumentation. We survey existing lifting operators scattered in the literature of argumentation theory, social choice, and utility theory, and show fundamental relations and properties of these operators. Extending existing works from argumentation and social choice, we propose a list of postulates for lifting operations, and give a complete picture of (non-)satisfaction for the considered operators. Based on our postulates, we present impossibility results, stating for which sets of postulates there is no hope of satisfaction, and for two main lifting operators presented in structured argumentation, Elitist and Democratic, we give a full characterization in terms of our postulates.
    Type of Medium: Online Resource
    ISSN: 2374-3468 , 2159-5399
    URL: Article
    DOI: 10.1609/aaai.v35i7.16798
    Language: Unknown
    Publisher: Association for the Advancement of Artificial Intelligence (AAAI)
    Publication Date: 2021
    Location Call Number Limitation Availability
    BibTip Others were also interested in ...
    Online Resource
Close ⊗
This website uses cookies and the analysis tool Matomo. More information can be found here...