Name
Abstract | ||
|---|---|---|
Explainable Artificial Intelligence (XAI) systems need to include an explanation model to communicate the internal decisions, behaviours and actions to the interacting humans. Successful explanation involves both cognitive and social processes. In this paper we focus on the challenge of meaningful interaction between an explainer and an explainee and investigate the structural aspects of an interactive explanation to propose an interaction protocol. We follow a bottom-up approach to derive the model by analysing transcripts of different explanation dialogue types with 398 explanation dialogues. We use grounded theory to code and identify key components of an explanation dialogue. We formalize the model using the agent dialogue framework (ADF) as a new dialogue type and then evaluate it in a human-agent interaction study with 101 dialogues from 14 participants. Our results show that the proposed model can closely follow the explanation dialogues of human-agent conversations. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.5555/3306127.3331801 | Adaptive Agents and Multi-Agents Systems |
Keywords | DocType | Volume |
Explainable AI,Interpretable Machine Learning,Dialogue Model,Human-Agent Interaction | Conference | abs/1903.02409 |
Citations | PageRank | References |
1 | 0.35 | 0 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Prashan Madumal | 1 | 6 | 2.09 |
| Tim Miller | 2 | 196 | 16.45 |
| Liz Sonenberg | 3 | 802 | 119.89 |
| Frank Vetere | 4 | 1805 | 143.63 |