Ontological foundations for contrastive explanatory narration of robot plans
- URL: http://arxiv.org/abs/2509.22493v1
- Date: Fri, 26 Sep 2025 15:37:47 GMT
- Title: Ontological foundations for contrastive explanatory narration of robot plans
- Authors: Alberto Olivares-Alarcos, Sergi Foix, Júlia Borràs, Gerard Canal, Guillem Alenyà,
- Abstract summary: This article focuses on an approach to modeling and reasoning about the comparison of two competing plans.<n>A novel ontological model is proposed to formalize and reason about the differences between competing plans.<n>A novel algorithm is presented, leveraging divergent knowledge between plans and facilitating the construction of contrastive narratives.
- Score: 8.913500117780819
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Mutual understanding of artificial agents' decisions is key to ensuring a trustworthy and successful human-robot interaction. Hence, robots are expected to make reasonable decisions and communicate them to humans when needed. In this article, the focus is on an approach to modeling and reasoning about the comparison of two competing plans, so that robots can later explain the divergent result. First, a novel ontological model is proposed to formalize and reason about the differences between competing plans, enabling the classification of the most appropriate one (e.g., the shortest, the safest, the closest to human preferences, etc.). This work also investigates the limitations of a baseline algorithm for ontology-based explanatory narration. To address these limitations, a novel algorithm is presented, leveraging divergent knowledge between plans and facilitating the construction of contrastive narratives. Through empirical evaluation, it is observed that the explanations excel beyond the baseline method.
Related papers
- Personalised Explanations in Long-term Human-Robot Interactions [13.471816696693553]
Human-Robot Interaction (XHRI) investigates methods to generate explanations and evaluate their impact on human-robot interactions.<n>Previous works have highlighted the need to personalise the level of detail of these explanations to enhance usability and comprehension.<n>Our paper presents a framework designed to update and retrieve user knowledge-memory models, allowing for adapting the explanations' level of detail while referencing previously acquired concepts.
arXiv Detail & Related papers (2025-07-03T10:40:39Z) - Multimodal Coherent Explanation Generation of Robot Failures [1.3965477771846408]
We introduce an approach to generate coherent multimodal explanations by checking the logical coherence of explanations from different modalities.
Our experiments suggest that fine-tuning a neural network that was pre-trained to recognize textual entailment, performs well for coherence assessment.
arXiv Detail & Related papers (2024-10-01T13:15:38Z) - Chaining Simultaneous Thoughts for Numerical Reasoning [92.2007997126144]
numerical reasoning over text should be an essential skill of AI systems.
Previous work focused on modeling the structures of equations, and has proposed various structured decoders.
We propose CANTOR, a numerical reasoner that models reasoning steps using a directed acyclic graph.
arXiv Detail & Related papers (2022-11-29T18:52:06Z) - Interpreting Neural Policies with Disentangled Tree Representations [58.769048492254555]
We study interpretability of compact neural policies through the lens of disentangled representation.
We leverage decision trees to obtain factors of variation for disentanglement in robot learning.
We introduce interpretability metrics that measure disentanglement of learned neural dynamics.
arXiv Detail & Related papers (2022-10-13T01:10:41Z) - On Model Reconciliation: How to Reconcile When Robot Does not Know
Human's Model? [0.0]
The Model Reconciliation Problem (MRP) was introduced to address issues in explainable AI planning.
Most approaches to solving MRPs assume that the robot, who needs to provide explanations, knows the human model.
We propose a dialog-based approach for computing explanations of MRPs under the assumptions that the robot does not know the human model.
arXiv Detail & Related papers (2022-08-05T10:48:42Z) - Rethinking Explainability as a Dialogue: A Practitioner's Perspective [57.87089539718344]
We ask doctors, healthcare professionals, and policymakers about their needs and desires for explanations.
Our study indicates that decision-makers would strongly prefer interactive explanations in the form of natural language dialogues.
Considering these needs, we outline a set of five principles researchers should follow when designing interactive explanations.
arXiv Detail & Related papers (2022-02-03T22:17:21Z) - Counterfactual Explanations as Interventions in Latent Space [62.997667081978825]
Counterfactual explanations aim to provide to end users a set of features that need to be changed in order to achieve a desired outcome.
Current approaches rarely take into account the feasibility of actions needed to achieve the proposed explanations.
We present Counterfactual Explanations as Interventions in Latent Space (CEILS), a methodology to generate counterfactual explanations.
arXiv Detail & Related papers (2021-06-14T20:48:48Z) - Leveraging Neural Network Gradients within Trajectory Optimization for
Proactive Human-Robot Interactions [32.57882479132015]
We present a framework that fuses together the interpretability and flexibility of trajectory optimization (TO) with the predictive power of state-of-the-art human trajectory prediction models.
We demonstrate the efficacy of our approach in a multi-agent scenario whereby a robot is required to safely and efficiently navigate through a crowd of up to ten pedestrians.
arXiv Detail & Related papers (2020-12-02T08:43:36Z) - Axiom Learning and Belief Tracing for Transparent Decision Making in
Robotics [8.566457170664926]
A robot's ability to provide descriptions of its decisions and beliefs promotes effective collaboration with humans.
Our architecture couples the complementary strengths of non-monotonic logical reasoning, deep learning, and decision-tree induction.
During reasoning and learning, the architecture enables a robot to provide on-demand relational descriptions of its decisions, beliefs, and the outcomes of hypothetical actions.
arXiv Detail & Related papers (2020-10-20T22:09:17Z) - Joint Inference of States, Robot Knowledge, and Human (False-)Beliefs [90.20235972293801]
Aiming to understand how human (false-temporal)-belief-a core socio-cognitive ability unify-would affect human interactions with robots, this paper proposes to adopt a graphical model to the representation of object states, robot knowledge, and human (false-)beliefs.
An inference algorithm is derived to fuse individual pg from all robots across multi-views into a joint pg, which affords more effective reasoning inference capability to overcome the errors originated from a single view.
arXiv Detail & Related papers (2020-04-25T23:02:04Z) - iCORPP: Interleaved Commonsense Reasoning and Probabilistic Planning on
Robots [46.13039152809055]
We present a novel algorithm, called iCORPP, to simultaneously estimate the current world state, reason about world dynamics, and construct task-oriented controllers.
Results show significant improvements in scalability, efficiency, and adaptiveness, compared to competitive baselines.
arXiv Detail & Related papers (2020-04-18T17:46:59Z) - A general framework for scientifically inspired explanations in AI [76.48625630211943]
We instantiate the concept of structure of scientific explanation as the theoretical underpinning for a general framework in which explanations for AI systems can be implemented.
This framework aims to provide the tools to build a "mental-model" of any AI system so that the interaction with the user can provide information on demand and be closer to the nature of human-made explanations.
arXiv Detail & Related papers (2020-03-02T10:32:21Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.