Retraction and Revocation in Agent Deliberation Dialogs

McBurney, Peter; Parsons, Simon

doi:10.1007/s10503-007-9057-8

Cited by 6 publications

(1 citation statement)

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“…The possible worlds (i.e., Kripkean) semantics is an example of denotational semantics for a logical language[48] 7. An example of analysis of the different pragmatical meanings existing between, say, 'commands' and 'promises' can be found in[58]. Furthermore, the work presented in[61] introduces a specific syntax that accounts for the pragmatical uptake and revocation of utterances over actions.…”

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confidence: 99%

Explanation–Question–Response dialogue: An argumentative tool for explainable AI

Castagna,

McBurney,

Parsons

2024

AAC

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Advancements and deployments of AI-based systems, especially Deep Learning-driven generative language models, have accomplished impressive results over the past few years. Nevertheless, these remarkable achievements are intertwined with a related fear that such technologies might lead to a general relinquishing of our lives’s control to AIs. This concern, which also motivates the increasing interest in the eXplainable Artificial Intelligence (XAI) research field, is mostly caused by the opacity of the output of deep learning systems and the way that it is generated, which is largely obscure to laypeople. A dialectical interaction with such systems may enhance the users’ understanding and build a more robust trust towards AI. Commonly employed as specific formalisms for modelling intra-agent communications, dialogue games prove to be useful tools to rely upon when dealing with user’s explanation needs. The literature already offers some dialectical protocols that expressly handle explanations and their delivery. This paper fully formalises the novel Explanation–Question–Response (EQR) dialogue and its properties, whose main purpose is to provide satisfactory information (i.e., justified according to argumentative semantics) whilst ensuring a simplified protocol, in comparison with other existing approaches, for humans and artificial agents.

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confidence: 99%