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.

The The valuation of a given security policy is often predicated varying extent to which a compromised account at one service upon assumptions that fail in practice (e.g, (Blythe, Koppel, can escalate to compromise accounts on other services and Smith 2013)). For example, a plethora of password further complicates matters. And we're just scratching the discussions begin with the password paradox: users must surface. In such complex environments, a mathematical pick strong passwords-so strong that the average user cannot analysis of security can quickly become unwieldy, while a remember them-yet they must never be written down.

While an immense amount of work has gone into novel clustering algorithms, little work has focused on developing compact, domain-specific explanations for the results of the clustering algorithms. Attaching semantic meaning to a cluster has numerous benefits, including the ability for such a description to be both human and machine readable. In this paper, we assume that the clusters are given to us, and find the minimal set of features that can differentiate one cluster from the remaining set of samples. We formulate this problem as an integer linear program. By using samples not belonging to the cluster in the optimization formulation, the resulting description will be minimal and contain no false positives. The efficacy of this method is demonstrated on simulation data and real-world malware data run in a sandbox that collects behavioral characteristics. In the case of malware, once it has been clustered, it would have been sent to a reverse engineer who would have been tasked with creating the actual meaning of the clustering results and disseminating this information through signatures or indicators of compromise. This is a time-consuming process that can take hours to weeks depending on the complexity of the malware family. The methods presented in this paper automatically generate optimal signatures, which can then be quickly propagated to help contain the spread of a malware family.

Much existing work examining the ethical behaviors of robots does not consider the impact and effects of long- term human-robot interactions. A robot teammate, col- laborator or helper is often expected to increase task performance, individually or of the team, but little dis- cussion is usually devoted to how such a robot should balance the task requirements with building and main- taining a “working relationship” with a human partner, much less appropriate social relations outside that team. We propose the “Relational Enhancement” framework for the design and evaluation of long-term interactions, which composed of interrelated concepts of efficiency, solidarity, and prosocial concern. We discuss how this framework can be used to evaluate common existing ap- proaches in cognitive architectures for robots and then examine how social norms and mental simulation may contribute to each of the components of the framework.

In the field of AI, expected utility maximizers are commonly used as a model for idealized agents. However, expected utility maximization can lead to unintended solutions when the utility function does not quantify everything the operators care about: imagine, for example, an expected utility maximizer tasked with winning money on the stock market, which has no regard for whether it accidentally causes a market crash. Once AI systems become sufficiently intelligent and powerful, these unintended solutions could become quite dangerous. In this paper, we describe an alternative to expected utility maximization for powerful AI systems, which we call expected utility quantilization. This could allow the construction of AI systems that do not necessarily fall into strange and unanticipated shortcuts and edge cases in pursuit of their goals.

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.

The activities of daily living of a patient in a smart home environment can be detected to a large extent by the real-time analysis of characteristics of the habitat's electrical consumption. However, reasoning over the conduct of these activities occurs at a much higher level of abstraction than what the sensors generally produce. In this paper, we leverage the concept of Complex Event Processing (CEP), in which low-level data streams are progressively transformed into higher-level ones, to the task of activity recognition. We show how the use of an appropriate representation for each level of abstraction can greatly simplify the process. We also report on the use of an existing event stream processor to successfully implement the complete chain, from low-level sensor data up to a sequence of discrete and high-level actions.

In this paper, we propose a novel approach to performactivities of daily living (ADL) recognition usinga tracking system based on the radio-frequency identification(RFID) technology.