# Contextual Reasoning for Scene Generation (Technical Report)

arXiv: https://arxiv.org/abs/2305.02255 · HTML: https://mlsanity.com/paper/2305.02255/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 19 references that are in the map.
- First posted on arXiv 3 May 2023.

## Builds on (most cited first, 5 of 5)

- [Black-box Generation of Adversarial Text Sequences to Evade Deep Learning Classifiers](https://mlsanity.com/paper/1801.04354/) (2018)
- [Clingo = ASP + Control: Preliminary Report](https://mlsanity.com/paper/1405.3694/) (2014)
- [Adversarial Diversity and Hard Positive Generation](https://mlsanity.com/paper/1605.01775/) (2016)
- [Adversarial example generation with AdaBelief Optimizer and Crop Invariance](https://mlsanity.com/paper/2102.03726/) (2021)
- [Reasoning on Multi-Relational Contextual Hierarchies via Answer Set Programming with Algebraic Measures](https://mlsanity.com/paper/2108.03100/) (2021)

## Abstract (arXiv)

We present a continuation to our previous work, in which we developed the MR-CKR framework to reason with knowledge overriding across contexts organized in multi-relational hierarchies. Reasoning is realized via ASP with algebraic measures, allowing for flexible definitions of preferences. In this paper, we show how to apply our theoretical work to real autonomous-vehicle scene data. Goal of this work is to apply MR-CKR to the problem of generating challenging scenes for autonomous vehicle learning. In practice, most of the scene data for AV learning models common situations, thus it might be difficult to capture cases where a particular situation occurs (e.g. partial occlusions of a crossing pedestrian). The MR-CKR model allows for data organization exploiting the multi-dimensionality of such data (e.g., temporal and spatial). Reasoning over multiple contexts enables the verification and configuration of scenes, using the combination of different scene ontologies. We describe a framework for semantically guided data generation, based on a combination of MR-CKR and Algebraic Measures. The framework is implemented in a proof-of-concept prototype exemplifying some cases of scene generation.