# Domain Authoring Assistant for Intelligent Virtual Agents

arXiv: https://arxiv.org/abs/1904.03266 · HTML: https://mlsanity.com/paper/1904.03266/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Apr 2019.

## Built on by (most cited first, 5 of 5)

- [COGMEN: COntextualized GNN based Multimodal Emotion recognitioN](https://mlsanity.com/paper/2205.02455/) (2022)
- [Shapes of Emotions: Multimodal Emotion Recognition in Conversations via Emotion Shifts](https://mlsanity.com/paper/2112.01938/) (2021)
- [The Importance of Multimodal Emotion Conditioning and Affect Consistency for Embodied Conversational Agents](https://mlsanity.com/paper/2309.15311/) (2023)
- [Towards Explainable Social Agent Authoring tools: A case study on FAtiMA-Toolkit](https://mlsanity.com/paper/2206.03360/) (2022)
- [CHARET: Character-centered Approach to Emotion Tracking in Stories](https://mlsanity.com/paper/2102.07537/) (2021)

## Abstract (arXiv)

Developing intelligent virtual characters has attracted a lot of attention in the recent years. The process of creating such characters often involves a team of creative authors who describe different aspects of the characters in natural language, and planning experts that translate this description into a planning domain. This can be quite challenging as the team of creative authors should diligently define every aspect of the character especially if it contains complex human-like behavior. Also a team of engineers has to manually translate the natural language description of a character's personality into the planning domain knowledge. This can be extremely time and resource demanding and can be an obstacle to author's creativity. The goal of this paper is to introduce an authoring assistant tool to automate the process of domain generation from natural language description of virtual characters, thus bridging between the creative authoring team and the planning domain experts. Moreover, the proposed tool also identifies possible missing information in the domain description and iteratively makes suggestions to the author.