Abstract Managed multi-context systems (mMCSs) allow for the integration of heterogeneous knowledge sources in a modular and very general way. They were, however, mainly designed for static scenarios and are therefore not well-suited for dynamic environments in which continuous reasoning over such heterogeneous knowledge with constantly arriving streams of data is necessary. In this paper, we introduce reactive multi-context systems (rMCSs), a framework for reactive reasoning in the presence of heterogeneous knowledge sources and data streams. We show that rMCSs are indeed well-suited for this purpose by illustrating how several typical problems arising in the context of stream reasoning can be handled using them, by showing how inconsistencies possibly occurring in the integration of multiple knowledge sources can be handled, and by arguing that the potential non-determinism of rMCSs can be avoided if needed using an alternative, more skeptical well-founded semantics instead with beneficial computational properties. We also investigate the computational complexity of various reasoning problems related to rMCSs. Finally, we discuss related work, and show that rMCSs do not only generalize mMCSs to dynamic settings, but also capture extend relevant approaches w.r.t. dynamics in knowledge representation and stream reasoning.
Several systems and frameworks for modeling the dynamics of knowledge and the flow of information have been developed. These systems are obviously related to reactive multi-context systems. In this section we provide a comparison with those approaches we consider most relevant, stressing differences as well as commonalities with . Note that we focus entirely on dynamic approaches and do not include systems which handle heterogeneous information in a static setting. An interesting example of the latter type are Lierler and Truszczy 'nski's abstract modular systems @cite_12 . In a nutshell, modular systems realize the communication between different reasoning modules through joint vocabularies rather than bridge rules. These systems are best viewed as alternatives to classical MCSs. It is an open question how to adapt them to a dynamic setting.
@cite_12
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We propose a general framework for multi-context reasoning which allows us to combine arbitrary monotonic and nonmonotonic logics. Nonmonotonic bridge rules are used to specify the information flow among contexts. We investigate several notions of equilibrium representing acceptable belief states for our multi-context systems. The approach generalizes the heterogeneous monotonic multi-context systems developed by F. Giunchiglia and colleagues as well as the homogeneous nonmonotonic multi-context systems of Brewka, Serafini and Roelofsen. Integrating diverse formalisms into modular knowledge representation systems offers increased expressivity, modeling convenience, and computational benefits. We introduce the concepts of abstract inference modules and abstract modular inference systems to study general principles behind the design and analysis of model generating programs, or solvers, for integrated multi-logic systems. We show how modules and modular systems give rise to transition graphs, which are a natural and convenient representation of solvers, an idea pioneered by the SAT community. These graphs lend themselves well to extensions that capture such important solver design features as learning. In the paper, we consider two flavors of learning for modular formalisms, local and global. We illustrate our approach by showing how it applies to answer set programming, propositional logic, multi-logic systems based on these two formalisms and, more generally, to satisfiability modulo theories. When giving an analysis of knowledge in multiagent systems, one needs a framework in which higher-order information and its dynamics can both be represented. A recent tradition starting in original work by Plaza treats all of knowledge, higher-order knowledge, and its dynamics on the same foot. Our work is in that tradition. It also fits in approaches that not only dynamize the epistemics, but also epistemize the dynamics: the actions that (groups of) agents perform are epistemic actions. Different agents may have different information about which action is taking place, including higher-order information. We demonstrate that such information changes require subtle descriptions. Our contribution is to provide a complete axiomatization for an action language of van Ditmarsch, where an action is interpreted as a relation between epistemic states (pointed models) and sets of epistemic states. The applicability of the framework is found in every context where multiagent strategic decision making is at stake, and already demonstrated in game-like scenarios such as Cluedo and card games. Multi-level simulation models, i.e., models where different components are simulated using sub-models of varying levels of complexity, belong to the current state-of-the-art in simulation. The existing analysis practice for multi-level simulation results is to manually compare results from different levels of complexity, amounting to a very tedious and error-prone, trial-and-error exploration process. In this paper, we introduce hierarchical visual steering, a new approach to the exploration and design of complex systems. Hierarchical visual steering makes it possible to explore and analyze hierarchical simulation ensembles at different levels of complexity. At each level, we deal with a dynamic simulation ensemble — the ensemble grows during the exploration process. There is at least one such ensemble per simulation level, resulting in a collection of dynamic ensembles, analyzed simultaneously. The key challenge is to map the multi-dimensional parameter space of one ensemble to the multi-dimensional parameter space of another ensemble (from another level). In order to support the interactive visual analysis of such complex data we propose a novel approach to interactive and semi-automatic parameter space segmentation and comparison. The approach combines a novel interaction technique and automatic, computational methods — clustering, concave hull computation, and concave polygon overlapping — to support the analysts in the cross-ensemble parameter space mapping. In addition to the novel parameter space segmentation we also deploy coordinated multiple views with standard plots. We describe the abstract analysis tasks, identified during a case study, i.e., the design of a variable valve actuation system of a car engine. The study is conducted in cooperation with experts from the automotive industry. Very positive feedback indicates the usefulness and efficiency of the newly proposed approach. Coupled biological and chemical systems, neural networks, social interacting species, the Internet and the World Wide Web, are only a few examples of systems composed by a large number of highly interconnected dynamical units. The first approach to capture the global properties of such systems is to model them as graphs whose nodes represent the dynamical units, and whose links stand for the interactions between them. On the one hand, scientists have to cope with structural issues, such as characterizing the topology of a complex wiring architecture, revealing the unifying principles that are at the basis of real networks, and developing models to mimic the growth of a network and reproduce its structural properties. On the other hand, many relevant questions arise when studying complex networks’ dynamics, such as learning how a large ensemble of dynamical systems that interact through a complex wiring topology can behave collectively. We review the major concepts and results recently achieved in the study of the structure and dynamics of complex networks, and summarize the relevant applications of these ideas in many different disciplines, ranging from nonlinear science to biology, from statistical mechanics to medicine and engineering. © 2005 Elsevier B.V. All rights reserved. Online social networks, World Wide Web, media and technological networks, and other types of so-called information networks are ubiquitous nowadays. These information networks are inherently heterogeneous and dynamic. They are heterogeneous as they consist of multi-typed objects and relations, and they are dynamic as they are constantly evolving over time. One of the challenging issues in such heterogeneous and dynamic environments is to forecast those relationships in the network that will appear in the future. In this paper, we try to solve the problem of continuous-time relationship prediction in dynamic and heterogeneous information networks. This implies predicting the time it takes for a relationship to appear in the future, given its features that have been extracted by considering both the heterogeneity and the temporal dynamics of the underlying network. To this end, we first introduce a meta-path-based feature extraction framework to effectively extract features suitable for relationship prediction regarding the heterogeneity and dynamicity of the network. Next, we propose a supervised nonparametric approach, called Non-Parametric Generalized Linear Model (NP-GLM), which infers the hidden underlying probability distribution of the relationship building time given its features. We then present a learning algorithm to train NP-GLM and an inference method to answer time-related queries. Extensive experiments conducted on both synthetic dataset and real-world DBLP bibliographic citation network dataset demonstrate the effectiveness of Np-Glm in solving continuous-time relationship prediction problem vis-a-vis alternative baselines. With the proliferation of communication networks and mobile devices, the privacy and security concerns on their information flow are raised. Given a critical system that may leak confidential information, the problem consists of verifying and also enforcing opacity by designing supervisors, to conceal confidential information from unauthorized persons. To find out what the intruder sees, it is required to construct an observer of the system. In this paper, we consider incremental observer generation of modular systems, for verification and enforcement of current state opacity. The synchronization of the subsystems generate a large state space. Moreover, the observer generation with exponential complexity adds even larger state space. To tackle the complexity problem, we prove that observer generation can be done locally before synchronizing the subsystems. The incremental local observer generation along with an abstraction method lead to a significant state space reduction compared to traditional monolithic methods. The existence of shared unobservable events is also considered in the incremental approach. Moreover, we present an illustrative example, where the results of verification and enforcement of current state opacity are shown on a modular multiple floor elevator building with an intruder. Furthermore, we extend the current state opacity, current state anonymity, and language based opacity formulations for verification of modular systems. This paper presents a unified framework of model-learning algorithms, called contracting dynamical system primitives (CDSP), that can be used to learn pose (i.e., position and orientation) dynamics of point-to-point motions from demonstrations. The position and the orientation (represented using quaternions) trajectories are modeled as two separate autonomous nonlinear dynamical systems. The special constraints of the ( S ^ 3 ) manifold are enforced in the formulation of the system that models the orientation dynamics. To capture the variability in the demonstrations, the dynamical systems are estimated using Gaussian mixture models (GMMs). The parameters of the GMMs are learned subject to the constraints derived using partial contraction analysis. The learned models’ reproductions are shown to accurately reproduce the demonstrations and are guaranteed to converge to the desired goal location. Experimental results illustrate the CDSP algorithm’s ability to accurately learn position and orientation dynamics and the utility of the learned models in path generation for a Baxter robot arm. The CDSP algorithm is evaluated on a publicly available dataset and a synthetic dataset, and is shown to have the lowest and comparable average reproduction errors when compared to state-of-the-art imitation learning algorithms. Current dialogue systems focus more on textual and speech context knowledge and are usually based on two speakers. Some recent work has investigated static image-based dialogue. However, several real-world human interactions also involve dynamic visual context (similar to videos) as well as dialogue exchanges among multiple speakers. To move closer towards such multimodal conversational skills and visually-situated applications, we introduce a new video-context, many-speaker dialogue dataset based on live-broadcast soccer game videos and chats from Twitch.tv. This challenging testbed allows us to develop visually-grounded dialogue models that should generate relevant temporal and spatial event language from the live video, while also being relevant to the chat history. For strong baselines, we also present several discriminative and generative models, e.g., based on tridirectional attention flow (TriDAF). We evaluate these models via retrieval ranking-recall, automatic phrase-matching metrics, as well as human evaluation studies. We also present dataset analyses, model ablations, and visualizations to understand the contribution of different modalities and model components. Complex information-processing systems, for example, quantum circuits, cryptographic protocols, or multi-player games, are naturally described as networks composed of more basic information-processing systems. A modular analysis of such systems requires a mathematical model of systems that is closed under composition, i.e., a network of these objects is again an object of the same type. We propose such a model and call the corresponding systems causal boxes . Causal boxes capture superpositions of causal structures, e.g., messages sent by a causal box @math can be in a superposition of different orders or in a superposition of being sent to box @math and box @math . Furthermore, causal boxes can model systems whose behavior depends on time. By instantiating the abstract cryptography framework with causal boxes, we obtain the first composable security framework that can handle arbitrary quantum protocols and relativistic protocols. In this paper we present an approach to question answering over heterogeneous knowledge sources that makes use of different ontology management components within the scenario of a digital library application. We present a principled framework for integrating structured metadata and unstructured resource content in a seamless manner which can then be flexibly queried using structured queries expressed in natural language. The novelty of the approach lies in the combination of different semantic technologies providing a clear benefit for the application scenario considered. The resulting system is implemented as part of the digital library of British Telecommunications (BT). The original contribution of our paper lies in the architecture we present allowing for the nonstraightforward integration of the different components we consider. Motivated by the need to combine systems and logics, we develop a modular approach to the model expansion (MX) problem, a task which is common in applications such as planning, scheduling, computational biology, formal verification. We develop a modular framework where parts of a modular system can be written in different languages. We start our development from a previous work, [14], but modify and extend that framework significantly. In particular, we use a model-theoretic setting and introduce a feedback (loop) operator on modules. We study the expressive power of our framework and demonstrate that adding the feedback operator increases the expressive power considerably. We prove that, even with individual modules being polytime solvable, the framework is expressive enough to capture all of NP, a property which does not hold without loop. Moreover, we demonstrate that, using monotonicity and anti-monotonicity of modules, one can significantly reduce the search space of a solution to a modular system. Modeling and implementing dynamical systems is a central problem in artificial intelligence, robotics, software agents, simulation, decision and control theory, and many other disciplines. In recent years, a new approach to representing such systems, grounded in mathematical logic, has been developed within the AI knowledge-representation community. This book presents a comprehensive treatment of these ideas, basing its theoretical and implementation foundations on the situation calculus, a dialect of first-order logic. Within this framework, it develops many features of dynamical systems modeling, including time, processes, concurrency, exogenous events, reactivity, sensing and knowledge, probabilistic uncertainty, and decision theory. It also describes and implements a new family of high-level programming languages suitable for writing control programs for dynamical systems. Finally, it includes situation calculus specifications for a wide range of examples drawn from cognitive robotics, planning, simulation, databases, and decision theory, together with all the implementation code for these examples. This code is available on the book's Web site. SUMMARY Substantial research in software engineering focuses on understanding the dynamic nature of software systems in order to improve software maintenance and program comprehension. This research typically makes use of automated instrumentation and profiling techniques after the fact, that is, without considering domain knowledge. In this paper, we examine another source of dynamic information that is generated from statements that have been inserted into the code base during development to draw the system administrators' attention to important run-time phenomena. We call this source communicated information (CI). Examples of CI include execution logs and system events. The availability of CI has sparked the development of an ecosystem of Log Processing Apps (LPAs) that surround the software system under analysis to monitor and document various run-time constraints. The dependence of LPAs on the timeliness, accuracy and granularity of the CI means that it is important to understand the nature of CI and how it evolves over time, both qualitatively and quantitatively. Yet, to our knowledge, little empirical analysis has been performed on CI and its evolution. In a case study on two large open source and one industrial software systems, we explore the evolution of CI by mining the execution logs of these systems and the logging statements in the source code. Our study illustrates the need for better traceability between CI and the LPAs that analyze the CI. In particular, we find that the CI changes at a high rate across versions, which could lead to fragile LPAs. We found that up to 70 of these changes could have been avoided and the impact of 15 to 80 of the changes can be controlled through the use of robust analysis techniques by LPAs. We also found that LPAs that track implementation-level CI (e.g. performance analysis) and the LPAs that monitor error messages (system health monitoring) are more fragile than LPAs that track domain-level CI (e.g. workload modelling), because the latter CI tends to be long-lived. Copyright © 2013 John Wiley & Sons, Ltd. The paper presents the system modeling, controller design and numerical simulation results for thermal energy management of a real office building. Focus is set on an efficient and unitary approach which leads from detailed civil engineering specifications of the building elements to compact and effective models which are suitable for control. A modular semi-automated approach in used in order to derive the discrete state-space representation of the system model. This combines the key thermal dynamics of the constructions with a modular list of thermal loads and losses, defined as external heat fluxes. A balanced trade-off is thus achieved between model accuracy and complexity through a compact and effective representation of the plant dynamics. The control strategy is based on a predictive controller which evolves an optimized system input vector, in a closed loop. Paths for occupant feedback integration into a single framework, by using human-in-the-loop models via disturbance channels are also discussed. A multilayer (ML) formulation of the multiconfiguration time-dependent Hartree (MCTDH) theory is presented. In this new approach, the single-particle (SP) functions in the original MCTDH method are further expressed employing a time-dependent multiconfigurational expansion. The Dirac–Frenkel variational principle is then applied to optimally determine the equations of motion. Following this strategy, the SP groups are built in several layers, where each top layer SP can contain many more Cartesian degrees of freedom than in the previous formulation of the MCTDH method. As a result, the ML-MCTDH method has the capability of treating substantially more physical degrees of freedom than the original MCTDH method, and thus significantly enhances the ability of carrying out quantum dynamical simulations for complex molecular systems. The efficiency of the new formulation is demonstrated by converged quantum dynamical simulations for systems with a few hundred to a thousand degrees of freedom. We study the dynamic network of e-mail traffic and find that it develops self-organized coherent structures similar to those appearing in many nonlinear dynamic systems. Such structures are uncovered by a general information theoretic approach to dynamic networks based on the analysis of synchronization among trios of users. In the e-mail network, coherent structures arise from temporal correlations when users act in a synchronized manner. These temporally linked structures turn out to be functional, goal-oriented aggregates that must react in real time to changing objectives and challenges (e.g., committees at a university). In contrast, static structures turn out to be related to organizational units (e.g., departments). Most real systems consist of a large number of interacting, multi-typed components, while most contemporary researches model them as homogeneous networks, without distinguishing different types of objects and links in the networks. Recently, more and more researchers begin to consider these interconnected, multi-typed data as heterogeneous information networks, and develop structural analysis approaches by leveraging the rich semantic meaning of structural types of objects and links in the networks. Compared to widely studied homogeneous network, the heterogeneous information network contains richer structure and semantic information, which provides plenty of opportunities as well as a lot of challenges for data mining. In this paper, we provide a survey of heterogeneous information network analysis. We will introduce basic concepts of heterogeneous information network analysis, examine its developments on different data mining tasks, discuss some advanced topics, and point out some future research directions. Multi-category classification of short dialogues is a common task performed by humans. When assigning a question to an expert, a customer service operator tries to classify the customer query into one of N different classes for which experts are available. Similarly, questions on the web (for example questions at Yahoo Answers) can be automatically forwarded to a restricted group of people with a specific expertise. Typical questions are short and assume background world knowledge for correct classification. With exponentially increasing amount of knowledge available, with distinct properties (labeled vs unlabeled, structured vs unstructured), no single knowledge-transfer algorithm such as transfer learning, multi-task learning or selftaught learning can be applied universally. In this work we show that bag-of-words classifiers performs poorly on noisy short conversational text snippets. We present an algorithm for leveraging heterogeneous data sources and algorithms with significant improvements over any single algorithm, rivaling human performance. Using different algorithms for each knowledge source we use mutual information to aggressively prune features. With heterogeneous data sources including Wikipedia, Open Directory Project (ODP), and Yahoo Answers, we show 89.4 and 96.8 correct classification on Google Answers corpus and Switchboard corpus using only 200 features class. This reflects a huge improvement over bag of words approaches and 48-65 error reduction over previously published state of art (Gabrilovich et. al. 2006). To comprehend the multipartite organization of large-scale biological and social systems, we introduce an information theoretic approach that reveals community structure in weighted and directed networks. We use the probability flow of random walks on a network as a proxy for information flows in the real system and decompose the network into modules by compressing a description of the probability flow. The result is a map that both simplifies and highlights the regularities in the structure and their relationships. We illustrate the method by making a map of scientific communication as captured in the citation patterns of >6,000 journals. We discover a multicentric organization with fields that vary dramatically in size and degree of integration into the network of science. Along the backbone of the network—including physics, chemistry, molecular biology, and medicine—information flows bidirectionally, but the map reveals a directional pattern of citation from the applied fields to the basic sciences.