# Interactive POMDP Lite: Towards Practical Planning to Predict and Exploit Intentions for Interacting with Self-Interested Agents

arXiv: https://arxiv.org/abs/1304.5159 · HTML: https://mlsanity.com/paper/1304.5159/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Apr 2013.

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

- [Efficient Adaptation in Mixed-Motive Environments via Hierarchical Opponent Modeling and Planning](https://mlsanity.com/paper/2406.08002/) (2024)
- [Efficient Algorithms for Planning with Participation Constraints](https://mlsanity.com/paper/2205.07767/) (2022)
- [Unifying Agent Interaction and World Information for Multi-agent Coordination](https://mlsanity.com/paper/2509.25550/) (2025)
- [Open Human-Robot Collaboration using Decentralized Inverse Reinforcement Learning](https://mlsanity.com/paper/2410.01790/) (2024)
- [NestRL: A Nested Training Regime for Mutual Adaptation in Human-AI Teaming](https://mlsanity.com/paper/2602.17737/) (2026)

## Abstract (arXiv)

A key challenge in non-cooperative multi-agent systems is that of developing efficient planning algorithms for intelligent agents to interact and perform effectively among boundedly rational, self-interested agents (e.g., humans). The practicality of existing works addressing this challenge is being undermined due to either the restrictive assumptions of the other agents' behavior, the failure in accounting for their rationality, or the prohibitively expensive cost of modeling and predicting their intentions. To boost the practicality of research in this field, we investigate how intention prediction can be efficiently exploited and made practical in planning, thereby leading to efficient intention-aware planning frameworks capable of predicting the intentions of other agents and acting optimally with respect to their predicted intentions. We show that the performance losses incurred by the resulting planning policies are linearly bounded by the error of intention prediction. Empirical evaluations through a series of stochastic games demonstrate that our policies can achieve better and more robust performance than the state-of-the-art algorithms.