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On the Applicability of Probabilistic Programming Languages for Causal Activity Recognition

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2019

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Springer

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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.

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Lüdtke, Stefan; Popko, Maximilian; Kirste, Thomas (2019): On the Applicability of Probabilistic Programming Languages for Causal Activity Recognition. KI - Künstliche Intelligenz: Vol. 33, No. 4. DOI: 10.1007/s13218-019-00580-7. Springer. PISSN: 1610-1987. pp. 389-399

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