Weighted model counting (WMC) has recently emerged as an effective and general approach to probabilistic inference, offering a computational framework for encoding a variety of formalisms, such as factor graphs and Bayesian networks.The advent of large-scale probabilistic knowledge bases has generated further interest in relational probabilistic representations, obtained by according weights to first-order formulas, whose semantics is given in terms of the ground theory, and solved by WMC. A fundamental limitation is that the domain of quantification, by construction and design, is assumed to be finite, which is at odds with areas such as vision and language understanding, where the existence of objects must be inferred from raw data. Dropping the finite-domain assumption has been known to improve the expressiveness of a first-order language for open-universe purposes, but these languages, so far, have eluded WMC approaches. In this paper, we revisit relational probabilistic models over an infinite domain, and establish a number of results that permit effective algorithms. We demonstrate this language on a number of examples, including a parameterized version of Pearl's Burglary-Earthquake-Alarm Bayesian network.

It may (2010) to estimate the probability of each possible goal be that one agent needs to monitor the activities of another based on the difference between the cost of the best plan agent, attempt to assist the other agent, or simply avoid getting for the goal given the observed actions, Cost(G O), and the in the way while performing its own duties. For all of cost of the best plan for the goal without the observed actions, these cases the agent needs to be able to realize what the Cost(G O). The big difference here is that the observations other agent is doing. In the absence of full and timely communication only indirectly give us probabilities for actions in of plans and goals, goal and plan recognition becomes the plan graph. We therefore first construct a Bayesian Network essential. Many goal recognition techniques allow the (BN) to estimate these action probabilities, and then sequence of observations to be incomplete, but few consider use this probability information in the plan graph to compute the possibility of noisy observations. In practice, this is not expected cost for each goal, given the observations.

While recent advances in offline reasoning techniques and online execution strategies have made planning under uncertainty more robust, the application of plans in partially-known environments is still a difficult and important topic. In this paper we present an approach for predicting new information about a partially-known initial state, represented as a multigraph utilizing Maximum-Margin Multi-Valued Regression. We evaluate this approach in four different domains, demonstrating high recall and accuracy.

Every cloud platform has a large number of software components, making it difficult to manage the security of the entire system. This paper discusses the requirement for an intelligent cloud security auditing solution, and an expert system architecture is presented. The solution can identify data confidentiality threats in the OpenStack cloud platform, as well as propose solutions to remove vulnerabilities before an attack occurs. Data confidentiality threats cover a wide range of security risks where attackers usually try to steal/corrupt personal data and are a major concern of users. For this reason, cloud infrastructures need frequent security auditing. The key features of the proposed expert system architecture include: acquisition of information detailing the latest cloud security threats and solutions, the conversion of acquired raw data into usable format, the application of a forward chaining inference algorithm, and the ability for the user to add/modify knowledge, which is then utilised to provide feasible solutions in ranked order. These components provide an automated mechanism to generate human-readable audit reports, improving the overall security status without the need for expert knowledge.

We introduce a novel approach for parallelizing MCMC inference in models with spatially determined conditional independence relationships, for which existing techniques exploiting graphical model structure are not applicable. Our approach is motivated by a model of seismic events and signals, where events detected in distant regions are approximately independent given those in intermediate regions. We perform parallel inference by coloring a factor graph defined over regions of latent space, rather than individual model variables. Evaluating on a model of seismic event detection, we achieve significant speedups over serial MCMC with no degradation in inference quality.

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.

When machine learning algorithms are used in life-critical or mission-critical applications (e.g., self driving cars, cyber security, surgical robotics), it is important to ensure that they provide some high-level correctness guarantees. We introduce a paradigm called Trusted Machine Learning (TML) with the goal of making learning techniques more trustworthy. We outline methods that show how symbolic analysis (specifi- cally parametric model checking) can be used to learn the dynamical model of a system where the learned model satis- fies correctness requirements specified in the form of temporal logic properties (e.g., safety, liveness). When a learned model does not satisfy the desired guarantees, we try two approaches: (1) Model Repair, wherein we modify a learned model directly, and (2) Data Repair, wherein we modify the data so that re-learning from the modified data will result in a trusted model. Model Repair tries to make the minimal changes to the trained model while satisfying the properties, whereas Data Repair tries to make the minimal changes to the dataset used to train the model for ensuring satisfaction of the properties. We show how the Model Repair and Data Repair problems can be solved for the case of probabilistic models, specifically Discrete-Time Markov Chains (DTMC) or Markov Decision Processes (MDP), when the desired properties are expressed in Probabilistic Computation Tree Logic (PCTL). Specifically, we outline how the parameter learning problem in the probabilistic Markov models under temporal logic constraints can be equivalently expressed as a non-linear optimization with non-linear rational constraints, by performing symbolic transformations using a parametric model checker. We illustrate the approach on two case studies: a controller for automobile lane changing, and query router for a wireless sensor network.

Procedural content generation (PCG) for videogames relies on a commitment to the semantics of the game. Concepts such as enemies or solidity are required for the creation of levels for platformer games. As humans, we can instantly identify the underlying semantics of a game from brief snippets of game play video or from playing the game. Previous PCG systems have needed humans to identify the semantic properties of objects in the game, either implicitly or explicitly. We propose a system that can automatically learn the semantic properties of game objects by observation of events in the game via a causal learning framework. We apply this learning approach to play traces from the Super Mario Bros. series.

A common problem in disciplines of applied Statistics research such as Astrostatistics is of estimating the posterior distribution of relevant parameters. Typically, the likelihoods for such models are computed via expensive experiments such as cosmological simulations of the universe. An urgent challenge in these research domains is to develop methods that can estimate the posterior with few likelihood evaluations. In this paper, we study active posterior estimation in a Bayesian setting when the likelihood is expensive to evaluate. Existing techniques for posterior estimation are based on generating samples representative of the posterior. Such methods do not consider efficiency in terms of likelihood evaluations. In order to be query efficient we treat posterior estimation in an active regression framework. We propose two myopic query strategies to choose where to evaluate the likelihood and implement them using Gaussian processes. Via experiments on a series of synthetic and real examples we demonstrate that our approach is significantly more query efficient than existing techniques and other heuristics for posterior estimation.

There is a widely-accepted need to revise current forms of health-care provision, with particular interest in sensing systems in the home. Given a multiple-modality sensor platform with heterogeneous network connectivity, as is under development in the Sensor Platform for HEalthcare in Residential Environment (SPHERE) Interdisciplinary Research Collaboration (IRC), we face specific challenges relating to the fusion of the heterogeneous sensor modalities. We introduce Bayesian models for sensor fusion, which aims to address the challenges of fusion of heterogeneous sensor modalities. Using this approach we are able to identify the modalities that have most utility for each particular activity, and simultaneously identify which features within that activity are most relevant for a given activity. We further show how the two separate tasks of location prediction and activity recognition can be fused into a single model, which allows for simultaneous learning an prediction for both tasks. We analyse the performance of this model on data collected in the SPHERE house, and show its utility. We also compare against some benchmark models which do not have the full structure,and show how the proposed model compares favourably to these methods