Abstracting from Observation-Equivalent Entities in Human Behavior Modeling
Abstract: Recognizing human behavior from noisy and ambiguous sensor data is a prerequisite for many applications such as context-aware assistance. The sensor data, however, often do not allow to distinguish between multiple entities, e.g. a presence sensor does not allow to distinguish between two persons i.e. both are observation-equivalent. Conventional algorithms, however, consider each of these entities separately during the inference of human behavior, leading to a high computational burden in scenarios where a large number of entities have to be considered. Therefore, these algorithms can only be applied to very limited scenarios. We analyzed the challenges appearing in these scenarios and revealed that considering observation-equivalent entities separately is one reason for the huge computational effort. Thus, we propose to exploit observation-equivalence by representing entities as a group and inferring about these groups of entities. We sketch a mechanism that exploits observation-equivalencies which we call lifted probabilistic inference. To compare this approach with conventional inference approaches, we adapted an office scenario from the literature so that it parametrizes observation-equivalent entities and simulated a corresponding dataset. This dataset can be used as a benchmark for the evaluation of different inference approaches with respect to observation-equivalence. We compare the number of states this approach, and a conventional inference algorithm is considering during inference on this benchmark dataset. On average, the conventional approach uses almost 200,000 states to cover the situations of the scenario during the inference whereas our lifted probabilistic inference approach uses less than 100 states. Thus, an observation-equivalent approach seems promising for a more efficient inference in scenarios with many observation-equivalent entities.
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