A Multi-agent Argumentation Framework to Support Collective Reasoning

There are many benefits of using argumentation-based techniques in multi-agent systems, as clearly shown in the literature. Such benefits come not only from the expressiveness that argumentation-based techniques bring to agent communication but also from the reasoning and decision-making capabilities under conditions of conflicting and uncertain information that argumentation enables for autonomous agents. When developing multi-agent applications in which argumentation will be used to improve agent communication and reasoning, argumentation schemes (reasoning patterns for argumentation) are useful in addressing the requirements of the application domain in regards to argumentation (e.g., defining the scope in which argumentation will be used by agents in that particular application). In this work, we propose an argumentation framework that takes into account the particular structure of argumentation schemes at its core. This paper formally defines such a framework and experimentally evaluates its implementation for both argumentation-based reasoning and dialogues.

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Epistemic Argumentation Framework: Theory and Computation

Journal of Artificial Intelligence Research ◽

10.1613/jair.1.12121 ◽

2020 ◽

Vol 69 ◽

pp. 1103-1126

Author(s):

Chiaki Sakama ◽

Tran Cao Son

Keyword(s):

Logic Programming ◽

Epistemic Logic ◽

Argumentation Framework ◽

Multi Agent ◽

Epistemic Constraint

The paper introduces the notion of an epistemic argumentation framework (EAF) as a means to integrate the beliefs of a reasoner with argumentation. Intuitively, an EAF encodes the beliefs of an agent who reasons about arguments. Formally, an EAF is a pair of an argumentation framework and an epistemic constraint. The semantics of the EAF is defined by the notion of an ω-epistemic labelling set, where ω is complete, stable, grounded, or preferred, which is a set of ω-labellings that collectively satisfies the epistemic constraint of the EAF. The paper shows how EAF can represent different views of reasoners on the same argumentation framework. It also includes representing preferences in EAF and multi-agent argumentation. Finally, the paper discusses complexity issues and computation using epistemic logic programming.

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A Bayesian Argumentation Framework for Distributed Fault Diagnosis in Telecommunication Networks

Sensors ◽

10.3390/s19153408 ◽

2019 ◽

Vol 19 (15) ◽

pp. 3408

Author(s):

Álvaro Carrera ◽

Eduardo Alonso ◽

Carlos A. Iglesias

Keyword(s):

Fault Diagnosis ◽

Data Privacy ◽

Telecommunication Network ◽

Telecommunication Networks ◽

Multi Agent Systems ◽

Software Support ◽

Argumentation Framework ◽

Geographically Distributed ◽

Multi Agent ◽

Fault Diagnosis System

Traditionally, fault diagnosis in telecommunication network management is carried out by humans who use software support systems. The phenomenal growth in telecommunication networks has nonetheless triggered the interest in more autonomous approaches, capable of coping with emergent challenges such as the need to diagnose faults’ root causes under uncertainty in geographically-distributed environments, with restrictions on data privacy. In this paper, we present a framework for distributed fault diagnosis under uncertainty based on an argumentative framework for multi-agent systems. In our approach, agents collaborate to reach conclusions by arguing in unpredictable scenarios. The observations collected from the network are used to infer possible fault root causes using Bayesian networks as causal models for the diagnosis process. Hypotheses about those fault root causes are discussed by agents in an argumentative dialogue to achieve a reliable conclusion. During that dialogue, agents handle the uncertainty of the diagnosis process, taking care of keeping data privacy among them. The proposed approach is compared against existing alternatives using benchmark multi-domain datasets. Moreover, we include data collected from a previous fault diagnosis system running in a telecommunication network for one and a half years. Results show that the proposed approach is suitable for the motivational scenario.

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A rule-based argumentation framework for distributed contextual reasoning in dynamic environments

DYNA ◽

10.15446/dyna.v88n217.90858 ◽

2021 ◽

Vol 88 (217) ◽

pp. 120-130

Author(s):

Helio Henrique Lopes Costa Monte Alto ◽

Ayslan Trevizan Possebom ◽

Miriam Mariela Mercedes Morveli Espinoza ◽

Cesar Augusto Tacla

Keyword(s):

Dynamic Environments ◽

Multi Agent System ◽

Argumentation Framework ◽

Defeasible Argumentation ◽

Distributed Reasoning ◽

Contextual Reasoning ◽

Multi Agent ◽

Inconsistent Knowledge ◽

Partial View ◽

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In this study, we tackled the problem of distributed reasoning in environments in which agents may have incomplete and inconsistent knowledge. Conflicts between agents are resolved through defeasible argumentation-based semantics with a preference function. Support for dynamic environments, where agents constantly enter and leave the system, was achieved by means of rules whose premises can be held by arbitrary agents. Moreover, we presented a formalism that enables agents to share information about their current situation or focus when issuing queries to other agents. This is necessary in environments where agents have a partial view of the world and must be able to cooperate with one another to reach conclusions. Hence, we presented the formalization of a multi-agent system and the argument construction and semantics that define its reasoning approach. Using example scenarios, we demonstrated that our system enables the modeling of a broader range of scenarios than related work.

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