Search Results for author:
Found 7 papers, 1 papers with code
Explanation-based Belief Revision: Moving Beyond Minimalism to Explanatory Understanding
In contrast, the explanatory hypothesis indicates that people are inherently driven to seek explanations for inconsistencies, thereby striving for explanatory coherence rather than minimal changes when revising beliefs.
On Generating Monolithic and Model Reconciling Explanations in Probabilistic Scenarios
In this paper, we propose a novel framework for generating probabilistic monolithic explanations and model reconciling explanations.
Approximating Human Models During Argumentation-based Dialogues
Explainable AI Planning (XAIP) aims to develop AI agents that can effectively explain their decisions and actions to human users, fostering trust and facilitating human-AI collaboration.
Human-Modeling in Sequential Decision-Making: An Analysis through the Lens of Human-Aware AI
In this work, we retroactively try to provide an account of what constitutes a human-aware AI system.
DR-HAI: Argumentation-based Dialectical Reconciliation in Human-AI Interactions
We present DR-HAI -- a novel argumentation-based framework designed to extend model reconciliation approaches, commonly used in human-aware planning, for enhanced human-AI interaction.
On Exploiting Hitting Sets for Model Reconciliation
A popular approach to do this is called model reconciliation, where the agent tries to reconcile the differences in its model and the human's model such that the plan is also optimal in the human's model.
On the Relationship Between KR Approaches for Explainable Planning
In this paper, we build upon notions from knowledge representation and reasoning (KR) to expand a preliminary logic-based framework that characterizes the model reconciliation problem for explainable planning.
