# Goal Agnostic Planning using Maximum Likelihood Paths in Hypergraph World Models

arXiv: https://arxiv.org/abs/2110.09442 · HTML: https://mlsanity.com/paper/2110.09442/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Latent predictive world models, one of 11 directions in World models.
- Builds on 1 of its 41 references that are in the map.
- First posted on arXiv 18 Oct 2021.

## Placed in

- World models / [Latent predictive world models](https://mlsanity.com/wm/latent-predictive-world-models/)

## Builds on (most cited first, 1 of 1)

- [An Overview of Hierarchical Task Network Planning](https://mlsanity.com/paper/1403.7426/) (2014)

## Abstract (arXiv)

In this paper, we present a hypergraph--based machine learning algorithm, a datastructure--driven maintenance method, and a planning algorithm based on a probabilistic application of Dijkstra's algorithm. Together, these form a goal agnostic automated planning engine for an autonomous learning agent which incorporates beneficial properties of both classical Machine Learning and traditional Artificial Intelligence. We prove that the algorithm determines optimal solutions within the problem space, mathematically bound learning performance, and supply a mathematical model analyzing system state progression through time yielding explicit predictions for learning curves, goal achievement rates, and response to abstractions and uncertainty. To validate performance, we exhibit results from applying the agent to three archetypal planning problems, including composite hierarchical domains, and highlight empirical findings which illustrate properties elucidated in the analysis.