# Visual Explanation by High-Level Abduction: On Answer-Set Programming Driven Reasoning about Moving Objects

arXiv: https://arxiv.org/abs/1712.00840 · HTML: https://mlsanity.com/paper/1712.00840/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Dec 2017.

## Built on by (most cited first, 6 of 6)

- [Explain Before You Answer: A Survey on Compositional Visual Reasoning](https://mlsanity.com/paper/2508.17298/) (2025)
- [AUTO-DISCERN: Autonomous Driving Using Common Sense Reasoning](https://mlsanity.com/paper/2110.13606/) (2021)
- [Assessing Drivers' Situation Awareness in Semi-Autonomous Vehicles: ASP based Characterisations of Driving Dynamics for Modelling Scene Interpretation and Projection](https://mlsanity.com/paper/2308.15895/) (2023)
- [ASP-based Multi-shot Reasoning via DLV2 with Incremental Grounding](https://mlsanity.com/paper/2412.17143/) (2024)
- [An Algebraic Approach to Learning and Grounding](https://mlsanity.com/paper/2204.02813/) (2022)
- [Think before You Simulate: Symbolic Reasoning to Orchestrate Neural Computation for Counterfactual Question Answering](https://mlsanity.com/paper/2506.10753/) (2025)

## Abstract (arXiv)

We propose a hybrid architecture for systematically computing robust visual explanation(s) encompassing hypothesis formation, belief revision, and default reasoning with video data. The architecture consists of two tightly integrated synergistic components: (1) (functional) answer set programming based abductive reasoning with space-time tracklets as native entities; and (2) a visual processing pipeline for detection based object tracking and motion analysis. We present the formal framework, its general implementation as a (declarative) method in answer set programming, and an example application and evaluation based on two diverse video datasets: the MOTChallenge benchmark developed by the vision community, and a recently developed Movie Dataset.