# Applying Probabilistic Programming to Affective Computing

arXiv: https://arxiv.org/abs/1903.06445 · HTML: https://mlsanity.com/paper/1903.06445/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Mar 2019.

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

- [Acting as Inverse Inverse Planning](https://mlsanity.com/paper/2305.16913/) (2023)
- [An Ethical Framework for Guiding the Development of Affectively-Aware Artificial Intelligence](https://mlsanity.com/paper/2107.13734/) (2021)
- [Towards an architectural framework for intelligent virtual agents using probabilistic programming](https://mlsanity.com/paper/2307.10693/) (2023)
- [Latent Emission-Augmented Perspective-Taking (LEAPT) for Human-Robot Interaction](https://mlsanity.com/paper/2308.06498/) (2023)
- [Teleology-Driven Affective Computing: A Causal Framework for Sustained Well-Being](https://mlsanity.com/paper/2502.17172/) (2025)
- [Logic of Montage](https://mlsanity.com/paper/2511.19063/) (2025)

## Abstract (arXiv)

Affective Computing is a rapidly growing field spurred by advancements in artificial intelligence, but often, held back by the inability to translate psychological theories of emotion into tractable computational models. To address this, we propose a probabilistic programming approach to affective computing, which models psychological-grounded theories as generative models of emotion, and implements them as stochastic, executable computer programs. We first review probabilistic approaches that integrate reasoning about emotions with reasoning about other latent mental states (e.g., beliefs, desires) in context. Recently-developed probabilistic programming languages offer several key desidarata over previous approaches, such as: (i) flexibility in representing emotions and emotional processes; (ii) modularity and compositionality; (iii) integration with deep learning libraries that facilitate efficient inference and learning from large, naturalistic data; and (iv) ease of adoption. Furthermore, using a probabilistic programming framework allows a standardized platform for theory-building and experimentation: Competing theories (e.g., of appraisal or other emotional processes) can be easily compared via modular substitution of code followed by model comparison. To jumpstart adoption, we illustrate our points with executable code that researchers can easily modify for their own models. We end with a discussion of applications and future directions of the probabilistic programming approach.