Computing Preferences in Abstract Argumentation

Mahesar, Quratul-ain; Oren, Nir; Vasconcelos, Wamberto

doi:10.1007/978-3-030-03098-8_24

Cited by 6 publications

(4 citation statements)

References 22 publications

(19 reference statements)

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“…[33,34] has examined one such inverse problem in the context of the gradual semantics, seeking to identify a set of initial weights given a semantics, a set of arguments, attacks and final acceptability degrees. In the context of abstract argumentation, such inverse problems have examined inferring preferences from justified arguments [26,27], and has applications in the context of belief revision [8].…”

Section: Discussion and Future Workmentioning

confidence: 99%

Inferring attack relations for gradual semantics

Oren

Yun

2023

AAC

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A gradual semantics takes a weighted argumentation framework as input and outputs a final acceptability degree for each argument, with different semantics performing the computation in different manners. In this work, we consider the problem of attack inference. That is, given a gradual semantics, a set of arguments with associated initial weights, and the final desirable acceptability degrees associated with each argument, we seek to determine whether there is a set of attacks on those arguments such that we can obtain these acceptability degrees. The main contribution of our work is to demonstrate that the associated decision problem, i.e., whether a set of attacks can exist which allows the final acceptability degrees to occur for given initial weights, is NP-complete for the weighted h-categoriser and card-based semantics, and is polynomial for the weighted max-based semantics, even for the complete version of the problem (where all initial weights and final acceptability degrees are known). We then briefly discuss how this decision problem can be modified to find the attacks themselves and conclude by examining the partial problem where not all initial weights or final acceptability degrees may be known.

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Section: Discussion and Future Workmentioning

confidence: 99%

Inferring attack relations for gradual semantics

Oren

Yun

2023

AAC

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“…An important reason for finding all possible preferences is that, in a multi-extension semantic, if there are more than two extensions, e.g., E 1 , E 2 , E 3 , then there might be a preference Pref 1 that is unique for E 1 in comparison to the preferences of E 2 , and a preference Pref 2 that is unique for E 1 in comparison to the preferences of E 3 , and the set of preferences {Pref [11] propose an extension-based approach for computing preferences for a given set of conflict-free arguments in an abstract argumentation framework. However, the preferences are computed over abstract arguments which are atomic and the algorithms do no not take into account structural components of arguments such as assumptions, contraries and inference rules in the context of structured argumentation formalisms in particular ABA and ABA + .…”

Section: Uniqueprefs ← Uniqueprefs ∪ P Return Uniqueprefsmentioning

confidence: 99%

“…In turn, computing all possible sets of preferences helps in finding all justifications. Previous work on the topic [11] -which we build on -considered only abstract argumentation frameworks (namely Dung's AAFs and PAFs), and this paper extends the state of the art by considering a structured setting, building on ABA and ABA + frameworks. Moving to a structured setting is non-trivial and requires additional issues to be addressed that do not arise in the abstract cases.…”

Section: Introductionmentioning

confidence: 99%

Preference Elicitation in Assumption-Based Argumentation

Mahesar¹,

Oren²,

Vasconcelos³

2020

Preprint

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Various structured argumentation frameworks utilize preferences as part of their standard inference procedure to enable reasoning with preferences. In this paper, we consider an inverse of the standard reasoning problem, seeking to identify what preferences over assumptions could lead to a given set of conclusions being drawn. We ground our work in the Assumption-Based Argumentation (ABA) framework, and present an algorithm which computes and enumerates all possible sets of preferences over the assumptions in the system from which a desired conflict free set of conclusions can be obtained under a given semantic. After describing our algorithm, we establish its soundness, completeness and complexity.

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“…While we do not discuss the applications of our results, we note that potential areas in which they can be used include persuasion [21] and preference elicitation [18].…”

Section: Introductionmentioning

confidence: 99%

The Inverse Problem for Argumentation Gradual Semantics

Oren¹,

Yun²,

Vesic³

et al. 2022

Preprint

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Gradual semantics with abstract argumentation provide each argument with a score reflecting its acceptability, i.e. how "much" it is attacked by other arguments. Many different gradual semantics have been proposed in the literature, each following different principles and producing different argument rankings. A sub-class of such semantics, the so-called weighted semantics, takes, in addition to the graph structure, an initial set of weights over the arguments as input, with these weights affecting the resultant argument ranking. In this work, we consider the inverse problem over such weighted semantics. That is, given an argumentation framework and a desired argument ranking, we ask whether there exist initial weights such that a particular semantics produces the given ranking. The contribution of this paper are: (1) an algorithm to answer this problem, (2) a characterisation of the properties that a gradual semantics must satisfy for the algorithm to operate, and (3) an empirical evaluation of the proposed algorithm.

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Computing Preferences in Abstract Argumentation

Cited by 6 publications

References 22 publications

Inferring attack relations for gradual semantics

Inferring attack relations for gradual semantics

Preference Elicitation in Assumption-Based Argumentation

The Inverse Problem for Argumentation Gradual Semantics

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