Ontological modeling and reasoning for comparison and contrastive narration of robot plans: Extended Abstract

TY - CHAP

T1 - Ontological modeling and reasoning for comparison and contrastive narration of robot plans

T2 - Extended Abstract

AU - Olivares-Alarcos, Alberto

AU - Foix, Sergi

AU - Borràs, Júlia

AU - Canal, Gerard

AU - Alenyà, Guillem

N1 - Publisher Copyright: © 2024 International Foundation for Autonomous Agents and Multiagent Systems.

PY - 2024/5

Y1 - 2024/5

N2 - This extended abstract focuses on an approach to modeling and reasoning about the comparison of competing plans, so that robots can later explain the divergent result. First, the need for a novel ontological model that empowers robots to formalize and reason about plan divergences is identified. Then, a new ontological theory is proposed to facilitate the classification of plans (e.g., the shortest, the safest, the closest to human preferences, etc.). Finally, the limitations of a baseline algorithm for ontology-based explanatory narration are examined, and a novel algorithm is introduced to leverage the divergent knowledge between plans, enabling the construction of contrastive narratives. An empirical evaluation is conducted to assess the quality of the explanations provided by the proposed algorithm, which outperforms the baseline method.

AB - This extended abstract focuses on an approach to modeling and reasoning about the comparison of competing plans, so that robots can later explain the divergent result. First, the need for a novel ontological model that empowers robots to formalize and reason about plan divergences is identified. Then, a new ontological theory is proposed to facilitate the classification of plans (e.g., the shortest, the safest, the closest to human preferences, etc.). Finally, the limitations of a baseline algorithm for ontology-based explanatory narration are examined, and a novel algorithm is introduced to leverage the divergent knowledge between plans, enabling the construction of contrastive narratives. An empirical evaluation is conducted to assess the quality of the explanations provided by the proposed algorithm, which outperforms the baseline method.

UR - http://www.scopus.com/inward/record.url?scp=85196427771&partnerID=8YFLogxK

U2 - 10.5555/3635637.3663175

DO - 10.5555/3635637.3663175

M3 - Conference paper

VL - 2024-May

T3 - Proceedings of the International Joint Conference on Autonomous Agents and Multiagent Systems, AAMAS

SP - 2405

EP - 2407

BT - Proceedings of the 2024 International Conference on Autonomous Agents and Multiagent Systems

ER -