Towards a Foundational Ontology for Identifying and Resolving Contradictions in Dialogue-based Human-Robot Interactions
Maitreyee Tewari, Michele Persiani
Abstract
Existing Human-Robot Interaction (HRI) literature has focused on identifying and structuring errors, failures, conflicts, and knowledge issues (called in this work as contradictions) in domain-specific dialogue-based interactions. However, there is still lack of a formal computational framework to represent and define these contradictions, interoperable and usable across HRI and human-agent interaction (HAI) domains. Thus, this research project aims to capture, represent, and evaluate the notion of (1) dialogue-based collaborative interaction and (2) related contradictions in a foundational ontology. METHONTOLOGY, a systematic approach to build domain-independent ontologies was applied. In the conceptualisation stage of the presented ontology, concepts and models from Activity Theory were used. Preliminary results presented in this short article are: (i) Natural language definitions of dialogues and related contradictions in HRI, (ii) Set Theoretic definitions of dialogues and contradictions, and (iii) First Order Logic (FoL) formulation of the contradiction concepts and three novel principles guiding dialogue-based interactions between humans and robots. In summary, we report on ongoing work to develop a foundational ontology based on Activity Theory called Activity Theory-based foundational ontology (ATFOt) to capture and represent the notion of contradictions in HRI.
Create a lesson
Related papers
BuildOcc: A Large Language Model Occupant Agent Platform for Building Energy Research
Wooyoung Jung
Large Language Model-Driven Context-Aware Eco-Feedback Generation and Evaluation
Wooyoung Jung, Prosper Babon-Ayeng
The PIONEER Project: A PrIvacy companion for mOtivatioN and knowlEdge transfER
Simon Althaus, Nina Gerber, Sara Hahn et al.
Beyond Problem Solving: Large Language Models for Emotional and Reflective Support in Mathematics Learning
Vera Rief, Mirella Hladký, Minju Yoo et al.
EEG-based Visual Retrieval and Reconstruction: From Neurally Visible Optimal Layer to Hierarchical Diffusion Generation
Minyi Wang, Zhenqin Wu, Rihui Li
Decoding Decision Correctness from EEG Under High Cognitive Workload in Virtual Reality: Implications for Collaborative Brain-Computer Interface Teams
Christopher Baker, Stephen Hinton, Tom Reed et al.