On the Applicability of Probabilistic Programming Languages for Causal Activity Recognition
| dc.contributor.author | Lüdtke, Stefan | |
| dc.contributor.author | Popko, Maximilian | |
| dc.contributor.author | Kirste, Thomas | |
| dc.date.accessioned | 2021-04-23T09:28:38Z | |
| dc.date.available | 2021-04-23T09:28:38Z | |
| dc.date.issued | 2019 | |
| dc.description.abstract | Recognizing causal activities of human protagonists, and jointly inferring context information like location of objects and agents from noisy sensor data is a challenging task. Causal models can be used, which describe the activity structure symbolically, e.g. by precondition-effect actions. Recently, probabilistic programming languages (PPLs) arose as an abstraction mechanism that allow to concisely define probabilistic models by a general-purpose programming language, and provide off-the-shelf, general-purpose inference algorithms. In this paper, we empirically investigate whether PPLs provide a feasible alternative for implementing causal models for human activity recognition, by comparing the performance of three different PPLs (Anglican, WebPPL and Figaro) on a multi-agent scenario. We find that PPLs allow to concisely express causal models, but general-purpose inference algorithms that are typically implemented in PPLs are outperformed by an application-specific inference algorithm by orders of magnitude. Still, PPLs can be a valuable tool for developing probabilistic models, due to their expressiveness and simple applicability. | de |
| dc.identifier.doi | 10.1007/s13218-019-00580-7 | |
| dc.identifier.pissn | 1610-1987 | |
| dc.identifier.uri | http://dx.doi.org/10.1007/s13218-019-00580-7 | |
| dc.identifier.uri | https://dl.gi.de/handle/20.500.12116/36254 | |
| dc.publisher | Springer | |
| dc.relation.ispartof | KI - Künstliche Intelligenz: Vol. 33, No. 4 | |
| dc.relation.ispartofseries | KI - Künstliche Intelligenz | |
| dc.subject | Anglican | |
| dc.subject | Bayesian filtering | |
| dc.subject | Causal model | |
| dc.subject | Figaro | |
| dc.subject | Particle filter | |
| dc.subject | Probabilistic programming language | |
| dc.subject | WebPPL | |
| dc.title | On the Applicability of Probabilistic Programming Languages for Causal Activity Recognition | de |
| dc.type | Text/Journal Article | |
| gi.citation.endPage | 399 | |
| gi.citation.startPage | 389 |