Lüdtke, StefanPopko, MaximilianKirste, Thomas2021-04-232021-04-2320192019http://dx.doi.org/10.1007/s13218-019-00580-7https://dl.gi.de/handle/20.500.12116/36254Recognizing 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.AnglicanBayesian filteringCausal modelFigaroParticle filterProbabilistic programming languageWebPPLText/Journal Article10.1007/s13218-019-00580-71610-1987