References of "Kaci, Souhila"
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See detailPreference in Abstract Argumentation
Kaci, Souhila; van der Torre, Leon UL; Villata, Serena

in Computational Models of Argument (2018)

Consider an argument A that is attacked by an argument B, while A is preferred to B. Existing approaches will either ignore the attack or reverse it. In this paper we introduce a new reduction of ... [more ▼]

Consider an argument A that is attacked by an argument B, while A is preferred to B. Existing approaches will either ignore the attack or reverse it. In this paper we introduce a new reduction of preference and attack to defeat, based on the idea that in such a case, instead of ignoring the attack, the preference is ignored. We compare this new reduction with the two existing ones using a principle-based approach, for the four Dung semantics. The principle-based or axiomatic approach is a methodology to choose an argumentation semantics for a particular application, and to guide the search for new argumentation semantics. For this analysis, we also introduce a fourth reduction, and a semantics for preference-based argumentation based on extension selection. Our classification of twenty alternatives for preference-based abstract argumentation semantics using six principles suggests that our new reduction has some advantages over the existing ones, in the sense that if the set of preferences increases, the sets of accepted arguments increase as well. [less ▲]

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See detailA logical theory about dynamics in abstract argumentation
Booth, Richard UL; Kaci, Souhila; Rienstra, Tjitze UL et al

Scientific Conference (2013)

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See detailMonotonic and non-monotonic inference for abstract argumentation
Booth, Richard UL; Kaci, Souhila; Rienstra, Tjitze UL et al

in Proceedings of the Twenty-Sixth International Florida Artificial Intelligence Research Society Conference, FLAIRS 2013 (2013)

We present a new approach to reasoning about the outcome of an argumentation framework, where an agent’s reasoning with a framework and semantics is represented by an inference relation defined over a ... [more ▼]

We present a new approach to reasoning about the outcome of an argumentation framework, where an agent’s reasoning with a framework and semantics is represented by an inference relation defined over a logical labeling language. We first study a monotonic type of inference which is, in a sense, more general than an acceptance function, but equally expressive. In order to overcome the limitations of this expressiveness, we study a non-monotonic type of inference which allows counterfactual inferences. We precisely characterize the classes of frameworks distinguishable by the non-monotonic inference relation for the admissible semantics. [less ▲]

Detailed reference viewed: 51 (4 UL)
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See detailA logical theory about dynamics in abstract argumentation
Booth, Richard UL; Kaci, Souhila; Rienstra, Tjitze UL et al

Scientific Conference (2013)

Detailed reference viewed: 49 (7 UL)
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See detailA logical theory about dynamics in abstract argumentation
Booth, Richard UL; Kaci, Souhila; Rienstra, Tjitze UL et al

in Scalable Uncertainty Management - 7th International Conference, SUM 2013 (2013)

We address dynamics in abstract argumentation using a logical theory where an agent’s belief state consists of an argumentation framework (AF, for short) and a constraint that encodes the outcome the ... [more ▼]

We address dynamics in abstract argumentation using a logical theory where an agent’s belief state consists of an argumentation framework (AF, for short) and a constraint that encodes the outcome the agent believes the AF should have. Dynamics enters in two ways: (1) the constraint is strengthened upon learning that the AF should have a certain outcome and (2) the AF is expanded upon learning about new arguments/attacks. A problem faced in this setting is that a constraint may be inconsistent with the AF’s outcome. We discuss two ways to address this problem: First, it is still possible to form consistent fallback beliefs, i.e., beliefs that are most plausible given the agent’s AF and constraint. Second, we show that it is always possible to find AF expansions to restore consistency. Our work combines various individual approaches in the literature on argumentation dynamics in a general setting. [less ▲]

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See detailProperty-based preferences in abstract argumentation
Booth, Richard UL; Kaci, Souhila; Rienstra, Tjitze UL

in Algorithmic Decision Theory - Third International Conference, ADT 2013 (2013)

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See detailConditional acceptance functions
Booth, Richard UL; Kaci, Souhila; Rienstra, Tjitze UL et al

in 4th International Conference on Computational Models of Argument (COMMA 2012) (2012)

Dung-style abstract argumentation theory centers on argumentation frameworks and acceptance functions. The latter take as input a framework and return sets of labelings. This methodology assumes full ... [more ▼]

Dung-style abstract argumentation theory centers on argumentation frameworks and acceptance functions. The latter take as input a framework and return sets of labelings. This methodology assumes full awareness of the arguments relevant to the evaluation. There are two reasons why this is not satisfactory. Firstly, full awareness is, in general, not a realistic assumption. Second, frameworks have explanatory power, which allows us to reason abductively or counterfactually, but this is lost under the usual semantics. To recover this aspect, we generalize conventional acceptance, and we present the concept of a conditional acceptance function. [less ▲]

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See detailIndividual Opinions-Based Judgment Aggregation Procedures
Benamara, Farah; Kaci, Souhila; Pigozzi, Gabriella UL

in Modeling Decisions for Artificial Intelligence (2010)

Judgment aggregation is a recent formal discipline that studies how to aggregate individual judgments on logically connected propositions to form collective decisions on the same propositions. Despite the ... [more ▼]

Judgment aggregation is a recent formal discipline that studies how to aggregate individual judgments on logically connected propositions to form collective decisions on the same propositions. Despite the apparent simplicity of the problem, the aggregation of individual judgments can result in an inconsistent outcome. This seriously troubles this research field. Expert panels, legal courts, boards, and councils are only some examples of group decision situations that confront themselves with such aggregation problems. So far, the existing framework and procedures considered in the literature are idealized. Our goal is to enrich standard judgment aggregation by allowing the individuals to agree or disagree on the decision rule. Moreover, the group members have the possibility to abstain or express neutral judgments. This provides a more realistic framework and, at the same time, consents the definition of an aggregation procedure that escapes the inconsistent group outcome. [less ▲]

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See detailReasoning With Various Kinds of Preferences: Logic, Non-Monotonicity, and Algorithms
Kaci, Souhila; van der Torre, Leon UL

in Annals of Operations Research (2008), 163(1), 89114

As systems dealing with preferences become more sophisticated, it becomes essential to deal with various kinds of preference statements and their interaction. We introduce a non-monotonic logic ... [more ▼]

As systems dealing with preferences become more sophisticated, it becomes essential to deal with various kinds of preference statements and their interaction. We introduce a non-monotonic logic distinguishing sixteen kinds of preferences, ranging from strict to loose and from careful to opportunistic, and two kinds of ways to deal with uncertainty, either optimistically or pessimistically. The classification of the various kinds of preferences is inspired by a hypothetical agent comparing the two alternatives of a preference statement. The optimistic and pessimistic way of dealing with uncertainty correspond on the one hand to considering either the best or the worst states in the comparison of the two alternatives of a preference statement, and on the other hand to the calculation of least or most specific “distinguished” preference orders from a set of preference statements. We show that each way to calculate distinguished preference orders is compatible with eight kinds of preferences, in the sense that it calculates a unique distinguished preference order for a set of such preference statements, and we provide efficient algorithms that calculate these unique distinguished preference orders. In general, optimistic kinds of preferences are compatible with optimism in calculating distinguished preference orders, and pessimistic kinds of preferences are compatible with pessimism in calculating distinguished preference orders. However, these two sets of eight kinds of preferences are not exclusive, such that some kinds of preferences can be used in both ways to calculate distinguished preference orders, and other kinds of preferences cannot be used in either of them. We also consider the merging of optimistically and pessimistically constructed distinguished preferences orders. [less ▲]

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See detailPreference-based argumentation: Arguments supporting multiple values
Kaci, Souhila; van der Torre, Leon UL

in International Journal of Approximate Reasoning (2008), 48(3), 730751

In preference-based argumentation theory, an argument may be preferred to another one when, for example, it is more specific, its beliefs have a higher probability or certainty, or it promotes a higher ... [more ▼]

In preference-based argumentation theory, an argument may be preferred to another one when, for example, it is more specific, its beliefs have a higher probability or certainty, or it promotes a higher value. In this paper we generalize Bench-Capon’s value-based argumentation theory such that arguments can promote multiple values, and preferences among values or arguments can be specified in various ways. We assume in addition that there is default knowledge about the preferences over the arguments, and we use an algorithm to derive the most likely preference order. In particular, we show how to use non-monotonic preference reasoning to compute preferences among arguments, and subsequently the acceptable arguments, from preferences among values. We show also how the preference ordering can be used to optimize the algorithm to construct the grounded extension by proceeding from most to least preferred arguments. [less ▲]

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