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Autonomous Agents and Multi-Agent Systems
Erschienen in: Autonomous Agents and Multi-Agent Systems 1-2/2019

06.03.2019

A probabilistic argumentation framework for reinforcement learning agents

Towards a mentalistic approach to agent profiles

verfasst von: Régis Riveret, Yang Gao, Guido Governatori, Antonino Rotolo, Jeremy Pitt, Giovanni Sartor

Erschienen in: Autonomous Agents and Multi-Agent Systems | Ausgabe 1-2/2019

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Abstract

A bounded-reasoning agent may face two dimensions of uncertainty: firstly, the uncertainty arising from partial information and conflicting reasons, and secondly, the uncertainty arising from the stochastic nature of its actions and the environment. This paper attempts to address both dimensions within a single unified framework, by bringing together probabilistic argumentation and reinforcement learning. We show how a probabilistic rule-based argumentation framework can capture Markov decision processes and reinforcement learning agents; and how the framework allows us to characterise agents and their argument-based motivations from both a logic-based perspective and a probabilistic perspective. We advocate and illustrate the use of our approach to capture models of agency and norms, and argue that, in addition to providing a novel method for investigating agent types, the unified framework offers a sound basis for taking a mentalistic approach to agent profiles.
Vorheriger Artikel Strategic behavior and learning in all-pay auctions: an empirical study using crowdsourced data

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Fußnoten
1
Though there seems to be an emerging consensus in the literature conceiving ‘undercutting’ to mean an attack on a rule and ‘undermining’ to be an attack on premises, we prefer to adopt here a terminology closer to early work on rule-based argumentation, see e.g. [41].
 
2
Recall: the set of assumptive arguments supporting a set of assumptions \({Assum}\) is denoted \({\mathrm {AssumArg}}({Assum})\), see Notation  4.4.
 
3
Recall: the set of assumptive arguments supporting a set of assumptions \({Assum}\) is denoted \({\mathrm {AssumArg}}({Assum})\), see Notation 4.4.
 
4
We use the standard notation, so for \(\mathbf {Y} \subseteq \mathbf {X}\), we use \(\mathbf {x}(\mathbf {Y})\) to refer to the assignment within \(\mathbf {x}\) to the variables in \(\mathbf {Y}\). For example, if \(\mathbf {X}=\{X1,X2,X3\}\), \(\mathbf {Y}=\{X1,X2\}\) and \(\mathbf {x}=\{X1=1,X2=2,X3=3\}\), then \(\mathbf {x}(\mathbf {Y})=\{X1=1,X2=2\}\).
 
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Metadaten
Titel
A probabilistic argumentation framework for reinforcement learning agents
Towards a mentalistic approach to agent profiles
verfasst von
Régis Riveret
Yang Gao
Guido Governatori
Antonino Rotolo
Jeremy Pitt
Giovanni Sartor
Publikationsdatum
06.03.2019
Verlag
Springer US
Erschienen in
Autonomous Agents and Multi-Agent Systems / Ausgabe 1-2/2019
Print ISSN: 1387-2532
Elektronische ISSN: 1573-7454
DOI
https://doi.org/10.1007/s10458-019-09404-2

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