Name
Abstract | ||
|---|---|---|
Intelligent systems sometimes need to infer the probable goals of people, cars, and robots, based on partial observations of their motion. This paper introduces a class of probabilistic programs for formulating and solving these problems. The formulation uses randomized path planning algorithms as the basis for probabilistic models of the process by which autonomous agents plan to achieve their goals. Because these path planning algorithms do not have tractable likelihood functions, new inference algorithms are needed. This paper proposes two Monte Carlo techniques for these likelihood-free models, one of which can use likelihood estimates from neural networks to accelerate inference. The paper demonstrates efficacy on three simple examples, each using under 50 lines of probabilistic code. |
Year | Venue | Field |
|---|---|---|
2017 | arXiv: Artificial Intelligence | Motion planning,Autonomous agent,Intelligent decision support system,Computer science,Inference,Probabilistic analysis of algorithms,Artificial intelligence,Probabilistic logic,Robot,Artificial neural network,Machine learning |
DocType | Volume | Citations |
Journal | abs/1704.04977 | 1 |
PageRank | References | Authors |
0.37 | 6 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Marco F. Cusumano-Towner | 1 | 6 | 3.87 |
| Alexey Radul | 2 | 35 | 8.90 |
| David Wingate | 3 | 227 | 20.33 |
| Vikash K. Mansinghka | 4 | 452 | 34.78 |