This paper aims at providing a preliminary discussion on how to deal with spatio-temporal information in the context of behaviour recognition. It draws comparison with how humans reason in other areas, such as law, and discusses some of the pros and cons of formalisms for handling uncertainty, starting with probability theory, continuing with the Dempster-Shafer theory, and concluding with fuzzy logic.

In spite of the significant work that has been done todiscover and recognize activities in the smart home re-search, less attention has been paid to predict the futureactivities that the resident is likely to perform. An ac-tivity prediction module can play a major role in designof a smart home. For instance, by taking advantage ofan activity prediction module, a smart home can learncontext-aware rules to prompt individuals to initiate im-portant activities. In this paper, we propose an activityprediction approach using Bayesian networks. We pro-pose a novel two-step inference process to predict thenext activity features and then to predict the next activ-ity label. We also propose an approach to predict thestart time of the next activity which is based on model-ing the relative start time of the predicted activity usinga continuous normal distribution and outlier detection.We evaluate our proposed models using real data col-lected from two smart home apartments.

Opponents are characterized by a Bayesian network intended to guide Monte-Carlo Tree Search through the game tree of No-Limit Texas Hold'em Poker. By using a probabilistic model of opponents, the network is able to integrate all available sources of information, including the infrequent revelations of hidden beliefs. These revelations are biased, and as such are difficult to incorporate into action prediction. The proposed network mitigates this bias via the expectation maximization algorithm and a probabilistic characterization of the hidden variables that generate observations. 

Archived data from the WSR-88D network of weather radars in the US hold detailed information about the continent-scale migratory movements of birds over the last 20 years. However, significant technical challenges must be overcome to understand this information and harness its potential for science and conservation. We present an approximate Bayesian inference algorithm to reconstruct the velocity fields of birds migrating in the vicinity of a radar station. This is part of a larger project to quantify bird migration at large scales using weather radar data.

Mixed probabilistic and deterministic graphical models are ubiquitous in real-world applications. Unfortunately, Gibbs sampling, a popular MCMC technique, does not converge to the correct answers in presence of determinism and therefore cannot be used for inference in such models. In this paper, we propose to remedy this problem by combining Gibbs sampling with SampleSearch, an advanced importance sampling technique which leverages complete SAT/CSP solvers to generate high quality samples from hard deterministic spaces. We call the resulting algorithm, GiSS. Unlike Gibbs sampling which yields unweighted samples, GiSS yields weighted samples. Computing these weights exactly can be computationally expensive and therefore we propose several approximations. We show that our approximate weighting schemes yield consistent estimates and demonstrate experimentally that GiSS is competitive in terms of accuracy with state-of-the-art algorithms such as SampleSearch, MC-SAT and Belief propagation.

One of the most difficult tasks in value function approximation for Markov Decision Processes is finding an approximation architecture that is expressive enough to capture the important structure in the value function, while at the same time not overfitting the training samples. Recent results in non-parametric approximate linear programming (NP-ALP), have demonstrated that this can be done effectively using nothing more than a smoothness assumption on the value function. In this paper we extend these results to the case where samples come from real world transitions instead of the full Bellman equation, adding robustness to noise. In addition, we provide the first max-norm, finite sample performance guarantees for any form of ALP. NP-ALP is amenable to problems with large (multidimensional) or even infinite (continuous) action spaces, and does not require a model to select actions using the resulting approximate solution.

The implication problem of probabilistic conditional independencies is investigated in the presence of missing data. Here, graph separation axioms fail to hold for saturated conditional independencies, unlike the known idealized case with no missing data. Several axiomatic, algorithmic, and logical characterizations of the implication problem for saturated conditional independencies are established. In particular, equivalences are shown to the implication problem of a propositional fragment under Levesque's situations, and that of Lien's class of multivalued database dependencies under null values.

We study m-transportability, a generalization of transportability, which offers a license to use causal information elicited from experiments and observations in m>=1 source environments to estimate a causal effect in a given targetenvironment. We provide a novel characterization of m-transportability that directly exploits the completeness of do-calculus to obtain the necessary and sufficient conditions for m-transportability. We provide an algorithm for deciding m-transportability that determines whether a causal relation is m-transportable; and if it is, produces a transport formula, that is, a recipe for estimating the desired causal effect by combining experimental information from m source environments with observational information from the target environment.

To help users quickly understand the major opinions from massive online reviews, it is important to automatically reveal the latent structure of the aspects, sentiment polarities, and the association between them. However, there is little work available to do this effectively. In this paper, we propose a hierarchical aspect sentiment model (HASM) to discover a hierarchical structure of aspect-based sentiments from unlabeled online reviews. In HASM, the whole structure is a tree. Each node itself is a two-level tree, whose root represents an aspect and the children represent the sentiment polarities associated with it. Each aspect or sentiment polarity is modeled as a distribution of words. To automatically extract both the structure and parameters of the tree, we use a Bayesian nonparametric model, recursive Chinese Restaurant Process (rCRP), as the prior and jointly infer the aspect-sentiment tree from the review texts. Experiments on two real datasets show that our model is comparable to two other hierarchical topic models in terms of quantitative measures of topic trees. It is also shown that our model achieves better sentence-level classification accuracy than previously proposed aspect-sentiment joint models.

By handling whole sets of indistinguishable objects together, lifted belief propagation approaches have rendered large, previously intractable, probabilistic inference problems quickly solvable. In this paper, we show that Kumar and Zilberstein's likelihood maximization (LM) approach to MAP inference is liftable, too, and actually provides additional structure for optimization. Specifically, it has been recognized that some pseudo marginals may converge quickly, turning intuitively into pseudo evidence. This additional evidence typically changes the structure of the lifted network: it may expand or reduce it. The current lifted network, however, can be viewed as an upper bound on the size of the lifted network required to finish likelihood maximization. Consequently, we re-lift the network only if the pseudo evidence yields a reduced network, which can efficiently be computed on the current lifted network. Our experimental results on Ising models, image segmentation and relational entity resolution demonstrate that this bootstrapped LM via "reduce and re-lift" finds MAP assignments comparable to those found by the original LM approach, but in a fraction of the time.