RT Journal Article
T1 Enriching interactive explanations with fuzzy temporal constraint networks
A1 Canabal Juanatey, Mariña
A1 Alonso Moral, José María
A1 Catalá Bolós, Alejandro
A1 Bugarín-Diz, Alberto
K1 Fuzzy temporal constraint networks
K1 Fuzzy temporal reasoning
K1 Knowledge graphs
K1 Language models
K1 Conversational agents
AB Humans often use expressions with vague terms which play a fundamental role for effective communication. These expressions are successfully modeled with fuzzy technology, but they are not usually integrated yet with Natural Language Processing models and techniques. Large-scale pre-trained language models yield excellent results in many language tasks, but they have some drawbacks such as their lack of transparency and thorough temporal reasoning capabilities. Therefore, the use of such models may provoke inconsistent or incorrect dialogues in the context of conversational agents which were aimed at providing users of intelligent systems with interactive explanations.In this paper, we propose a model for fuzzy temporal reasoning to overcome some inconsistencies detected in pre-trained language models in a specific application domain of a conversational agent carefully designed for providing users with explanations which are endowed with a good balance between naturalness and fidelity. More precisely, starting from a knowledge graph that provides an intuitive representation of the entities and relations in the application domain, we describe how to map the temporal information onto a fuzzy temporal constraint network. This formalism allows to represent imprecise temporal information and provides mechanisms for checking consistency in conversations.In addition, as a proof of concept, we have developed TimeVersa, a conversational agent which integrates the proposed model into an application domain (i.e., a virtual assistant for tourists) that requires handling imprecise temporal constraints. We illustrate in a use case how the agent can identify temporal inconsistencies and answer queries related to temporal information properly. Results after a user study report that users' perception of consistency is significantly higher in a conversation with TimeVersa than in a similar conversation using the well-known GPT-3 Large Language Model, when vague temporal information is involved. The proposed approach is a step forward for developing conversational agents operating in application domains that require temporal reasoning under uncertainty.
PB Elsevier
SN 0888-613X
YR 2024
FD 2024
LK http://hdl.handle.net/10347/34258
UL http://hdl.handle.net/10347/34258
LA eng
NO International Journal of Approximate Reasoning Volume 171, August 2024, 109128
NO Mariña Canabal-Juanatey is a PhD Researcher supported by the Galician Ministry of Culture, Education, Professional Training and University (ED481A 2022/212). All authors recognize the support of the Galician Ministry of Culture, Education, Professional Training and University (grants ED431G2019/04 and ED431C2022/19). This work is also supported by the Spanish Ministry of Science and Innovation (MCIN/AEI/10.13039/501100011033/) with grants PID2021-123152OB-C21, PID2020-112623GB-I00, and TED2021-130295B-C33. All previous grants are co-funded by the European Regional Development Fund (ERDF/FEDER program).
DS Minerva
RD 29 abr 2026