An approach to generalizing the handling of preferences in argumentation-based decision-making systems

Teze, Juan Carlos; Gottifredi, Sebastián; García, Alejandro Javier; Simari, Guillermo Ricardo

doi:10.1016/j.knosys.2019.105112

Cited by 8 publications

(8 citation statements)

References 49 publications

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“…For instance, we could adopt an approach similar to [15], where the rules priorities criterion is used first to resolve attacks into defeats and, in case of undecidedness, the generalized specificity criterion is considered later. More generally, we will explore the possibility of using the operators defined in [45], which allow to combine multiple argument comparison criteria.…”

Section: Resultsmentioning

confidence: 99%

An informant-based approach to argument strength in Defeasible Logic Programming

Cohen

Gottifredi

Tamargo

et al. 2021

AAC

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This work formalizes an informant-based structured argumentation approach in a multi-agent setting, where the knowledge base of an agent may include information provided by other agents, and each piece of knowledge comes attached with its informant. In that way, arguments are associated with the set of informants corresponding to the information they are built upon. Our approach proposes an informant-based notion of argument strength, where the strength of an argument is determined by the credibility of its informant agents. Moreover, we consider that the strength of an argument is not absolute, but it is relative to the resolution of the conflicts the argument is involved in. In other words, the strength of an argument may vary from one context to another, as it will be determined by comparison to its attacking arguments (respectively, the arguments it attacks). Finally, we equip agents with the means to express reasons for or against the consideration of any piece of information provided by a given informant agent. Consequently, we allow agents to argue about the arguments' strength through the construction of arguments that challenge (respectively, defeat) or are in favour of their informant agents.

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Section: Resultsmentioning

confidence: 99%

An informant-based approach to argument strength in Defeasible Logic Programming

Cohen

Gottifredi

Tamargo

et al. 2021

AAC

Self Cite

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“…Many of them accommodate preference modelling for which preferences can be expressed over various elements of their decision making models, as indicated in the table. The majority of these works (Kakas & Moraitis, 2003;Matt et al, 2009;Marreiros, Santos, Novais, Machado, Ramos, Neves, & Bulas-Cruz, 2007;Dung et al, 2008;Müller & Hunter, 2012;Visser, Hindriks, & Jonker, 2012;Labreuche, 2013;Teze, Gottifredi, García, & Simari, 2020) do not explicitly consider explanations but rely on the underlying argumentation formalism to realise decision making interpretability. A few works (Fox, Glasspool, Grecu, Modgil, South, & Patkar, 2007;Amgoud & Prade, 2006, 2009 explicitly construct and label arguments for and against decision candidates as a form of explanations.…”

Section: Related Workmentioning

confidence: 99%

Explainable Decision Making with Lean and Argumentative Explanations

Fan¹,

Toni²

2022

Preprint

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It is widely acknowledged that transparency of automated decision making is crucial for deployability of intelligent systems, and explaining the reasons why some decisions are "good" and some are not is a way to achieving this transparency. We consider two variants of decision making, where "good" decisions amount to alternatives (i) meeting "most" goals, and (ii) meeting "most preferred" goals. We then define, for each variant and notion of "goodness" (corresponding to a number of existing notions in the literature), explanations in two formats, for justifying the selection of an alternative to audiences with differing needs and competences: lean explanations, in terms of goals satisfied and, for some notions of "goodness", alternative decisions, and argumentative explanations, reflecting the decision process leading to the selection, while corresponding to the lean explanations. To define argumentative explanations, we use assumption-based argumentation (ABA), a wellknown form of structured argumentation. Specifically, we define ABA frameworks such that "good" decisions are admissible ABA arguments and draw argumentative explanations from dispute trees sanctioning this admissibility. Finally, we instantiate our overall framework for explainable decision-making to accommodate connections between goals and decisions in terms of decision graphs incorporating defeasible and non-defeasible information.1. Here and in the remainder of the paper, we sometimes give

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“…In [21], a mechanism where the use of guards offers a particular way of associating conditions to the selection of a preference criterion was introduced. A guard is a set of literals γ, and a guard is satisfied in a state Ψ when for each literal L ∈ γ, we have (L, ω) ∈ Ψ for some weight ω.…”

Section: Case Study: P-delp-based Instantiationmentioning

confidence: 99%

“…A guard is a set of literals γ, and a guard is satisfied in a state Ψ when for each literal L ∈ γ, we have (L, ω) ∈ Ψ for some weight ω. Here, we use guards to guide the choice of a priority order associated with a given context that depends on the world's current state instead of selecting a criterion as was proposed in [21].…”

Section: Case Study: P-delp-based Instantiationmentioning

confidence: 99%

“…Note that conditional expressions are evaluated only on the basis of the strict part of the knowledge base. In [21], it is argued that restricting the guards to use only strict derivations has been a design choice. The rationale for using strict derivations for determining whether a guard is satisfied, is that there is no need for an auxiliary criterion to decide between conflicting information since the strict part is noncontradictory.…”

Section: Case Study: P-delp-based Instantiationmentioning

confidence: 99%

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An Architecture for Argumentation-Based Epistemic Planning: A First Approach With Contextual Preferences

Teze

Godo

2021

IEEE Intell. Syst.

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Argumentation-based planning systems present an interesting proposal for complex and dynamic domains where defeasible argumentation is used in the reasoning processes used during the construction of plans by considering the available knowledge. In many real-world application scenarios, knowledge is provided with explicit priorities. Despite its importance, existing planning systems do not provide additional reasoning capacities of dynamically changing the preferences expressed by these priorities when a plan is being constructed. In this work, we present an argumentation and planning architecture, and propose a set of software engineering guidelines to analyze and design planning systems leveraging this capacity.

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An approach to generalizing the handling of preferences in argumentation-based decision-making systems

Cited by 8 publications

References 49 publications

An informant-based approach to argument strength in Defeasible Logic Programming

An informant-based approach to argument strength in Defeasible Logic Programming

Explainable Decision Making with Lean and Argumentative Explanations

An Architecture for Argumentation-Based Epistemic Planning: A First Approach With Contextual Preferences

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