Learning accurate models of agent behaviours is crucial for the purpose of controlling systems where the agents' and environment's dynamics are unknown. This is a challenging problem, but structural assumptions can be leveraged to tackle it effectively. In particular, many systems exhibit mixed observability, when observations of some system components are essentially perfect and noiseless, while observations of other components are imperfect, aliased or noisy. In this paper we present a new model learning framework, the mixed observability predictive state representation (MO-PSR), which extends the previously known predictive state representations to the case of mixed observability systems. We present a learning algorithm that is scalable to large amounts of data and to large mixed observability domains, and show theoretical analysis of the learning consistency and computational complexity. Empirical results demonstrate that our algorithm is capable of learning accurate models, at a larger scale than with the generic predictive state representation, by leveraging the mixed observability properties.

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.

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.

POMDP (Partially Observable Markov Decision Process) is a mathematical framework that models planning under uncertainty. Solving a POMDP is an intractable problem and even the state of the art POMDP solvers are too computationally expensive for large domains. This is a major bottleneck. In this paper, we propose a new heuristic, called the pairwise heuristic, that can be used in a one-step greedy strategy to find a near optimal solution for POMDP problems very quickly. This approach is a good candidate for large problems where real-time solution is a necessity but exact optimality of the solution is not vital. The pairwise heuristic uses the optimal solutions for pairs of states. For each pair of states in the POMDP, we find the optimal sequence of actions to resolve the uncertainty and to maximize the reward, given that the agent is uncertain about which state of the pair it is in. Then we use these sequences as a heuristic and find the optimal action in each step of the greedy strategy using this heuristic. We have tested our method on the available large classical test benchmarks in various domains. The resulting total reward is close to, if not greater than, the total reward obtained by other state of the art POMDP solvers, while the time required to find the solution is always much less.

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.

Semi-qualitative probabilistic networks (SQPNs) merge two important graphical model formalisms: Bayesian networks and qualitative probabilistic networks. They provide a very general modeling framework by allowing the combination of numeric and qualitative assessments over a discrete domain, and can be compactly encoded by exploiting the same factorization of joint probability distributions that are behind the Bayesian networks.  This paper explores the computational complexity of semi-qualitative probabilistic networks, and takes the polytree-shaped networks as its main target. We show that the inference problem is coNP-Complete for binary polytrees with multiple observed nodes. We also show that inferences can be performed in time linear in the number of nodes if there is a single observed node. Because our proof is constructive, we obtain an efficient linear time algorithm for SQPNs under such assumptions. To the best of our knowledge, this is the first exact polynomial-time algorithm for SQPNs. Together these results provide a clear picture of the inferential complexity in polytree-shaped SQPNs.

Recent years have witnessed the tremendous development of social media, which attracts a vast number of Internet users. The high-dimension content generated by these users provides an unique opportunity to understand their behavior deeply. As one of the most fundamental topics, location estimation attracts more and more research efforts. Different from the previous literature, we find that user's location is strongly related to user interest. Based on this, we first build a detection model to mine user interest from short text. We then establish the mapping between location function and user interest before presenting an efficient framework to predict the user's location with convincing fidelity. Thorough evaluations and comparisons on an authentic data set show that our proposed model significantly outperforms the state-of-the-arts approaches. Moreover, the high efficiency of our model also guarantees its applicability in real-world scenarios.

Intermittent fault localization approaches account for the fact that faulty components may fail intermittently by considering a parameter (known as goodness) that quantifies the probability that faulty components may still exhibit correct behavior. Current, state-of-the-art approaches (1) assume that this goodness probability is context independent and (2) do not provide means for integrating past diagnosis experience in the diagnostic mechanism. In this paper, we present a novel approach, coined Non-linear Feedback-based Goodness Estimate (NFGE), that uses kernel density estimations (KDE) to address such limitations. We evaluated the approach with both synthetic and real data, yielding lower estimation errors, thus increasing the diagnosis performance.

Decentralized POMDPs (Dec-POMDPs) provide a rich, attractive model for planning under uncertainty and partial observability in cooperative multi-agent domains with a growing body of research. In this paper we formulate a qualitative, propositional model for multi-agent planning under uncertainty with partial observability, which we call Qualitative Dec-POMDP (QDec-POMDP). We show that the worst-case complexity of planning in QDec-POMDPs is similar to that of Dec-POMDPs. Still, because the model is more “classical” in nature, it is more compact and easier to specify. Furthermore, it eases the adaptation of methods used in classical and contingent planning to solve problems that challenge current Dec-POMDPs solvers. In particular, in this paper we describe a method based on compilation to classical planning, which handles multi-agent planning problems significantly larger than those handled by current Dec-POMDP algorithms.

While great strides have been made in multiagent teamwork, existing approaches typically assume extensive information exists about teammates and how to coordinate actions. This paper addresses how robust teamwork can still be created even if limited or no information exists about a specific group of teammates, as in the ad hoc teamwork scenario. The main contribution of this paper is the first empirical evaluation of an agent cooperating with teammates not created by the authors, where the agent is not provided expert knowledge of its teammates. For this purpose, we develop a general-purpose teammate modeling method and test the resulting ad hoc team agent's ability to collaborate with more than 40 unknown teams of agents to accomplish a benchmark task. These agents were designed by people other than the authors without these designers planning for the ad hoc teamwork setting. A secondary contribution of the paper is a new transfer learning algorithm, TwoStageTransfer, that can improve results when the ad hoc team agent does have some limited observations of its current teammates.