Since the seminal work on frequent itemset mining, there has been considerable effort on mining more structured patterns such as sequences or graphs. Additionally, the field of constraint programming has been linked to the field of pattern mining resulting in a more general and declarative constraint-based itemset mining framework. As a result, a number of recent papers have proposed to extend the declarative approach to structured pattern mining problems. Because the formalism and the solving mechanisms can be vastly different in specialised algorithm and declarative approaches, assessing the benefits and the drawbacks of each approach can be difficult. In this paper, we introduce a framework that formally defines the core components of itemset, sequence and graph mining tasks, and we use it to compare existing specialised algorithms to their declarative counterpart. This analysis allows us to draw clear connections between the two approaches and provide insights on how to overcome current limitations in declarative structured mining.

The role of decomposition-trees (also known as junction and clique trees) in probabilistic inference is widely known and has been the basis for many well known inference algorithms.Recent approaches have demonstrated that such trees have a "hidden structure," which enables the characterization of tractable problem instances as well as lead to insights that enable boosting the performance of inference algorithms. We consider the MPE problem on a Boolean formula in CNF where each literal in the formula is associated with a weight.We describe techniques for exploiting the junction-tree structure of these formulas in the context of a branch-and-bound algorithm for MPE.

While it is empirically confirmed folklore that conjunctive normal form (CNF) is not the ideal input format for QBF solvers, most tool developers and therefore also the users focus on formulas in this restricted structure. One important factor for establishing non-CNF solving is the input format. To overcome drawbacks of available formats, the QCIR format has recently been presented. The QCIR format is a circuit-based input format for quantified Boolean formulas which supports structure sharing. In contrast to previous formats, the representation is very compact, yet still easy to parse and to read for the human user. In this paper, we analyze the QCIR format in detail and provide tools and benchmarks which, we hope, will make its usage attractive and motivate tool developers to support this format as well as users to formulate their encodings in this format.

A new computational paradigm has emerged in computer both Renault and Toyota have deployed online configuration science over the past few decades, which is exemplified by systems based on knowledge compilation). QBF solvers the use of SAT solvers to tackle problems in the complexity have been used in model checking, verification, debugging, class NP. Finally, function problem solvers have and engineering investment is made towards developing been used in model-based diagnosis, design debugging, highly efficient solvers for a prototypical problem CAD and bioinformatics. The cost of this investment is then on a variety of topics, including algorithms; descriptions amortized as these solvers are applied to a broader class of of implementations and/or evaluations of beyond NP problems via reductions (in contrast to developing dedicated solvers; their applications (including encodings); the complexity algorithms for each encountered problem). SAT solvers, classes they reach; and their connections to one for example, are now routinely used to solve problems in another.

In this paper an extension to factorial hidden Semi Markov Models is introduced that allows modeling more than one sequence of emissions of the individual HMM chains, as well as a joint emission of all chains. Since exact inference in factorial hidden Markov Models is computationally intractable, an approximate inference technique is introduced that reduces the computational costs by first constraining the successor state space of the model, allowing state changes at statistically significant points in time (events) and by discarding low probability paths (truncating). Furthermore, by being agnostic about state durations the computational costs are further decreased. These assumptions allow for efficient inference that is less susceptible to local minima and allows one to specify the computational burden a priori. The performance of the inference technique is evaluated empirically on a synthetic data set whereas incorporating the feature emissions is evaluated on real world data in the context of energy disaggregation. Energy disaggregation tackles the problem of decomposing whole home energy measurements into the power traces of constituent appliances, and is a natural application for this type of models.

Selecting homes with energy-efficient infrastructure is important for renters, because infrastructure influences energy consumption more than in-home behavior.Personalized energy estimates can guide prospective tenants toward energy-efficient homes, but this information is not readily available. Utility estimates are not typically offered to house-hunters, and existing technologies like carbon calculators require users to answer (prohibitively) many questions that may require considerable research to answer. For the task of providing personalized utility estimates to prospective tenants, we present a cost-based model for feature selection at training time, where all features are available and costs assigned to each feature reflect the difficulty of acquisition. At test time, we have immediate access to some features but others are difficult to acquire (costly). In this limited-information setting, we strategically order questions we ask each user, tailored to previous information provided, to give the most accurate predictions while minimizing the cost to users. During the critical first 10 questions that our approach selects, prediction accuracy improves equally to fixed order approaches, but prediction certainty is higher.

In this paper, we propose an intelligent dialogue agent for the IoT home. The goal of the proposed system is to efficiently control IoT devices with natural spoken dialogue. This system is made up of the following components: Spoken Language Understanding for analyzing textual input and understanding user intention, Dialogue Management with a State Manager that consists of dialogue policies, Context Manager for understanding the environment, Action Planner responsible for generating a sequence of actions to achieve user intention, Things Manager for observing and controlling IoT devices, and Natural Language Generation that generates natural language from computer-based representation. This system is fully implemented in software and is evaluated in a real IoT home environment.

This paper presents a solution to Autonomous Underwater Vehicles (AUVs) large scale route planning and task assignment joint problem. Given a set of constraints (e.g., time) and a set of task priority values, the goal is to find the optimal route for underwater mission that maximizes the sum of the priorities and minimizes the total risk percentage while meeting the given constraints. Making use of the heuristic nature of genetic and swarm intelligence algorithms in solving NP-hard graph problems, Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) are employed to find the optimum solution, where each individual in the population is a candidate solution (route). To evaluate the robustness of the proposed methods, the performance of the all PS and GA algorithms are examined and compared for a number of Monte Carlo runs. Simulation results suggest that the routes generated by both algorithms are feasible and reliable enough, and applicable for underwater motion planning. However, the GA-based route planner produces superior results comparing to the results obtained from the PSO based route planner.

If you lie awake at night worrying about the'threat' of Artificial Intelligence (AI) taking over the world, then take heart from a recent episode involving robot waiters in China. Three restaurants in the southern Chinese city of Guangzhou have been forced to fire all of their robot staff after their utter incompetence began costing them money. Two of the restaurants have closed completely after discovering the clumsy waiters could not perform simple tasks like taking orders, pouring drinks and carrying soup, reports say. The slacking robot team also kept breaking down and after a string of complaints the third restaurant mentioned above decided to sack all but one and bring back human employees, the Workers' Daily newspaper reports.

We have bad news if you were hoping to live out your anime dreams and watch a Ghost in the Shell VR movie on your phone: it's not going to happen. Production IG has revealed that Virtual Reality Diver will only be shown in 31 internet cafés across the Kanto region (Tokyo, Yokohama and nearby areas) this May. Reportedly, it just became too big and ambitious to offer as a mobile app. That's somewhat understandable given its 15-minute length (a 360-degree clip that long is going to chew up a lot of space), but it's bound to be frustrating if you were hoping to return to the classic series on a mobile VR headset.