# On Generating Monolithic and Model Reconciling Explanations in Probabilistic Scenarios

arXiv: https://arxiv.org/abs/2405.19229 · HTML: https://mlsanity.com/paper/2405.19229/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 83 references that are in the map.
- First posted on arXiv 29 May 2024.

## Builds on (most cited first, 16 of 16)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [A Survey Of Methods For Explaining Black Box Models](https://mlsanity.com/paper/1802.01933/) (2018)
- [Explaining Explanations: An Overview of Interpretability of Machine Learning](https://mlsanity.com/paper/1806.00069/) (2018)
- [Towards Robust Interpretability with Self-Explaining Neural Networks](https://mlsanity.com/paper/1806.07538/) (2018)
- [Neurosymbolic AI: The 3rd Wave](https://mlsanity.com/paper/2012.05876/) (2020)
- [Principles and Practice of Explainable Machine Learning](https://mlsanity.com/paper/2009.11698/) (2020)
- [Explainable Planning](https://mlsanity.com/paper/1709.10256/) (2017)
- [Interactive Explanations by Conflict Resolution via Argumentative Exchanges](https://mlsanity.com/paper/2303.15022/) (2023)
- [On Computing Probabilistic Abductive Explanations](https://mlsanity.com/paper/2212.05990/) (2022)
- [Contrastive Explanations of Plans Through Model Restrictions](https://mlsanity.com/paper/2103.15575/) (2021)
- [Safe Learning of Lifted Action Models](https://mlsanity.com/paper/2107.04169/) (2021)
- [Improving Interpretability of Deep Neural Networks with Semantic Information](https://mlsanity.com/paper/1703.04096/) (2017)
- [On Exploiting Hitting Sets for Model Reconciliation](https://mlsanity.com/paper/2012.09274/) (2020)
- [Defining Explanation in Probabilistic Systems](https://mlsanity.com/paper/1302.1526/) (2013)
- [Dialectical Reconciliation via Structured Argumentative Dialogues](https://mlsanity.com/paper/2306.14694/) (2023)
- [Contrastive Explanations of Centralized Multi-agent Optimization Solutions](https://mlsanity.com/paper/2308.05984/) (2023)

## Built on by (most cited first, 1 of 1)

- [The Observer-Situation Lattice: A Unified Formal Basis for Perspective-Aware Cognition](https://mlsanity.com/paper/2603.01407/) (2026)

## Abstract (arXiv)

Explanation generation frameworks aim to make AI systems' decisions transparent and understandable to human users. However, generating explanations in uncertain environments characterized by incomplete information and probabilistic models remains a significant challenge. In this paper, we propose a novel framework for generating probabilistic monolithic explanations and model reconciling explanations. Monolithic explanations provide self-contained reasons for an explanandum without considering the agent receiving the explanation, while model reconciling explanations account for the knowledge of the agent receiving the explanation. For monolithic explanations, our approach integrates uncertainty by utilizing probabilistic logic to increase the probability of the explanandum. For model reconciling explanations, we propose a framework that extends the logic-based variant of the model reconciliation problem to account for probabilistic human models, where the goal is to find explanations that increase the probability of the explanandum while minimizing conflicts between the explanation and the probabilistic human model. We introduce explanatory gain and explanatory power as quantitative metrics to assess the quality of these explanations. Further, we present algorithms that exploit the duality between minimal correction sets and minimal unsatisfiable sets to efficiently compute both types of explanations in probabilistic contexts. Extensive experimental evaluations on various benchmarks demonstrate the effectiveness and scalability of our approach in generating explanations under uncertainty.