This paper examines the impact of neurosymbolic learning on sequence analysis in Structured Probability Spaces (SPS), comparing its effectiveness against a purely neural approach. Sequence analysis in SPS is challenging due to the combinatorial explosion of states and the difficulty of obtaining sufficient annotated training samples. Additionally, in SPS, the set of realizations with non-zero support is often a scattered, non-trivial subset of the Cartesian product of variables, adding complexity to learning and inference. The problem of sequence analysis in SPS emerges, for example, in reconstructing the activities of goal-directed agents from noisy and ambiguous sensor data. We explore the potential of neurosymbolic methods, which integrate symbolic background knowledge with neural learning, to constrain the hypothesis space and improve learning efficiency. Specifically, we conduct a simulation study in human activity recognition using DeepProbLog as a representative for neurosymbolic learning. Our results demonstrate that incorporating symbolic knowledge improves sample efficiency, generalization, and zero-shot learning, compared to a purely neural approach. Furthermore, we show that neurosymbolic models maintain robust performance under data scarcity while offering enhanced interpretability and stability. These findings suggest that neurosymbolic learning provides a promising foundation for sequence analysis in complex, structured domains, where purely neural approaches struggle with insufficient training data and limited generalization ability.

We present a model for exact recursive Bayesian filtering based on lifted multiset states. Combining multisets with lifting makes it possible to simultaneously exploit multiple strategies for reducing inference complexity when compared to list-based grounded state representations. The core idea is to borrow the concept of Maximally Parallel Multiset Rewriting Systems and to enhance it by concepts from Rao-Blackwellization and Lifted Inference, giving a representation of state distributions that enables efficient inference. In worlds where the random variables that define the system state are exchangeable -- where the identity of entities does not matter -- it automatically uses a representation that abstracts from ordering (achieving an exponential reduction in complexity) -- and it automatically adapts when observations or system dynamics destroy exchangeability by breaking symmetry.

Tasks such as social network analysis, human behavior recognition, or modeling biochemical reactions, can be solved elegantly by using the probabilistic inference framework. However, standard probabilistic inference algorithms work at a propositional level, and thus cannot capture the symmetries and redundancies that are present in these tasks. Algorithms that exploit those symmetries have been devised in different research fields, for example by the lifted inference-, multiple object tracking-, and modeling and simulation-communities. The common idea, that we call state space abstraction, is to perform inference over compact representations of sets of symmetric states. Although they are concerned with a similar topic, the relationship between these approaches has not been investigated systematically. This survey provides the following contributions. We perform a systematic literature review to outline the state of the art in probabilistic inference methods exploiting symmetries. From an initial set of more than 4,000 papers, we identify 116 relevant papers. Furthermore, we provide new high-level categories that classify the approaches, based on common properties of the approaches. The research areas underlying each of the categories are introduced concisely. Researchers from different fields that are confronted with a state space explosion problem in a probabilistic system can use this classification to identify possible solutions. Finally, based on this conceptualization, we identify potentials for future research, as some relevant application domains are not addressed by current approaches.

The reconstruction of human activities is an important prerequisite to provide assistance. In this paper, we present an activity and plan recognition approach which is based on causal models of human activities. We show, that it is possible to estimate current activities, the underlying goal of the user, and context information about the state of the environment from noisy sensor data. Therefore we use real world data obtained from a smart home system while observing unrestricted activities of daily living in an inhabited flat. We evaluate the accuracy of the recognition for simulated data of different granularity and data obtained from the smart home system. We furthermore show that performance measures solely based on action sequences are not sufficient to evaluate a recognition system.

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Activity recognition is a challenging research problem in ubiquitous computing domain and has to tackle omnipresent uncertainties, e.g., resulting from ambiguous or intermittent sensor readings. In this paper, we introduce an activity recognition approach based on causal modeling and probabilistic plan recognition. To evaluate the performance of our approach systematically, we generated sensor data with different error rates using a simulation. This data served as input for the activity recognition in a series of experiments. In these experiments we stepwise introduced and combined additional sources of uncertainty, i.e., different duration models and ignoring certain sensors, to demonstrate the robustness of our approach. Our evaluation shows that Computational Causal Behavior Models provide a basis for a robust activity recognition system.

In this paper we focus on measuring channel occupancy in the 2.4 GHz ISM band in a way that transmissions from Wireless LAN stations and other spectrum users can be distinguished. Most current 802.11 hardware does not offer methods to capture channel occupancy caused by non-802.11 transmissions accurately. We therefore combine a commodity 802.11 Wireless LAN card with an inexpensive 2.4 GHz RF transceiver IC which we use for spectrum measurements. We build a probabilistic model which allows to calculate the overall channel occupancy and to determine the fractions of 802.11 and non-802.11 activity. Our model covers adjacent channel interference, detects both narrow-band and wide-band interferers, and adapts to different signal strengths. We evaluate our approach in a test bed with controlled interference, estimate the achievable precision, and identify open problems.

In this paper, we show how to transfer the general idea of the subsumption architecture to the control of a smart environment. As in Brooks original idea, the control is implemented within small independent behaviours. But instead of controlling the actuators of the environment directly, our behaviours produce goals. These goals describe the desired state of the world. All behaviours create their goals solely based on the current state of the world, and independent of other behaviours. The created goals are then merged and a sequence of device actions is computed that lead to the desired state of the world. We show how to implement and configure such a controller, and how to use it for the control of an instrumented environment.