# Diversifying Agent's Behaviors in Interactive Decision Models

arXiv: https://arxiv.org/abs/2203.03068 · HTML: https://mlsanity.com/paper/2203.03068/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 37 references that are in the map.
- First posted on arXiv 6 Mar 2022.

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

- [The Complexity of Decentralized Control of Markov Decision Processes](https://mlsanity.com/paper/1301.3836/) (2013)
- [Autonomous Agents Modelling Other Agents: A Comprehensive Survey and Open Problems](https://mlsanity.com/paper/1709.08071/) (2017)
- [Identifying Unknown Unknowns in the Open World: Representations and Policies for Guided Exploration](https://mlsanity.com/paper/1610.09064/) (2016)
- [Networks of Influence Diagrams: A Formalism for Representing Agents' Beliefs and Decision-Making Processes](https://mlsanity.com/paper/1401.3426/) (2014)
- [Exploiting Model Equivalences for Solving Interactive Dynamic Influence Diagrams](https://mlsanity.com/paper/1401.4600/) (2014)

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

- [Automated Generation of Diverse Courses of Actions for Multi-Agent Operations using Binary Optimization and Graph Learning](https://mlsanity.com/paper/2506.20031/) (2025)

## Abstract (arXiv)

Modelling other agents' behaviors plays an important role in decision models for interactions among multiple agents. To optimise its own decisions, a subject agent needs to model what other agents act simultaneously in an uncertain environment. However, modelling insufficiency occurs when the agents are competitive and the subject agent can not get full knowledge about other agents. Even when the agents are collaborative, they may not share their true behaviors due to their privacy concerns. In this article, we investigate into diversifying behaviors of other agents in the subject agent's decision model prior to their interactions. Starting with prior knowledge about other agents' behaviors, we use a linear reduction technique to extract representative behavioral features from the known behaviors. We subsequently generate their new behaviors by expanding the features and propose two diversity measurements to select top-K behaviors. We demonstrate the performance of the new techniques in two well-studied problem domains. This research will contribute to intelligent systems dealing with unknown unknowns in an open artificial intelligence world.