A Dynamic Epistemic Framework for Conformant Planning

In this paper, we introduce a lightweight dynamic epistemic logical framework for automated planning under initial uncertainty. We reduce plan verification and conformant planning to model checking problems of our logic. We show that the model checking problem of the iteration-free fragment is PSPACE-complete. By using two non-standard (but equivalent) semantics, we give novel model checking algorithms to the full language and the iteration-free language.Comment: In Proceedings TARK 2015, arXiv:1606.0729

Dynamic Data Structures for Document Collections and Graphs

In the dynamic indexing problem, we must maintain a changing collection of text documents so that we can efficiently support insertions, deletions, and pattern matching queries. We are especially interested in developing efficient data structures that store and query the documents in compressed form. All previous compressed solutions to this problem rely on answering rank and select queries on a dynamic sequence of symbols. Because of the lower bound in [Fredman and Saks, 1989], answering rank queries presents a bottleneck in compressed dynamic indexing. In this paper we show how this lower bound can be circumvented using our new framework. We demonstrate that the gap between static and dynamic variants of the indexing problem can be almost closed. Our method is based on a novel framework for adding dynamism to static compressed data structures. Our framework also applies more generally to dynamizing other problems. We show, for example, how our framework can be applied to develop compressed representations of dynamic graphs and binary relations

Web Service Trust: Towards A Dynamic Assessment Framework

Trust in software services is a key prerequisite for the success and wide adoption of services-oriented computing (SOC) in an open Internet world. However, trust is poorly assessed by existing methods and technologies, especially in dynamically composed and deployed SOC systems. In this paper, we discuss current methods for assessing trust in service-oriented computing and identify gaps of current platforms, in particular with regards to runtime trust assessment. To address these gaps, we propose a model of runtime trust assessment of software services and introduce a framework for realizing the model. A key characteristic of our approach is the support that it offers for customizable assessment of trust based on evidence collected during the operation of software services and its ability to combine this evidence with subjective assessments coming from service clients

A Three-Dimensional Dynamic Supramolecular "Sticky Fingers" Organic Framework.

Engineering high-recognition host-guest materials is a burgeoning area in basic and applied research. The challenge of exploring novel porous materials with advanced functionalities prompted us to develop dynamic crystalline structures promoted by soft interactions. The first example of a pure molecular dynamic crystalline framework is demonstrated, which is held together by means of weak "sticky fingers" van der Waals interactions. The presented organic-fullerene-based material exhibits a non-porous dynamic crystalline structure capable of undergoing single-crystal-to-single-crystal reactions. Exposure to hydrazine vapors induces structural and chemical changes that manifest as toposelective hydrogenation of alternating rings on the surface of the [60]fullerene. Control experiments confirm that the same reaction does not occur when performed in solution. Easy-to-detect changes in the macroscopic properties of the sample suggest utility as molecular sensors or energy-storage materials

A profile-driven dynamic risk assessment framework for connected and autonomous vehicles

The Internet of Things has already demonstrated clear benefits when applied in many areas. In connected and autonomous vehicles (CAV), IoT data can help the autonomous systems make better decisions for safer and more secure transportation. For example, different IoT data sources can extend CAV's risk awareness, while the incoming data can update these risks in real-time for faster reactions that may mitigate possible damages. However, the current state of the art CAV research has not addressed this matter well enough. This paper proposes a profile-driven approach to manage IoT data in the context of CAV systems through a dynamic risk management framework. Unlike the current inflexible risk assessment strategies, the framework encourages more flexible investigation of risks through different risk profiles, each representing risk knowledge through a set of risk input considerations, assessment methods and optimal reaction strategies. As the risks change frequently with time and location, there will be no single profile that can cover all the risks that CAVs face on the road. The uses of different risk profiles, therefore can help interested parties to better understand the risks and adapt to various situations appropriately. Our framework includes the effective management of IoT data sources to enable the run-time risk assessment. We also describe a case study of using the proposed framework to manage the risks for the POD being developed in the Innovate UK-funded CAPRI project

A Regularized Graph Layout Framework for Dynamic Network Visualization

Many real-world networks, including social and information networks, are dynamic structures that evolve over time. Such dynamic networks are typically visualized using a sequence of static graph layouts. In addition to providing a visual representation of the network structure at each time step, the sequence should preserve the mental map between layouts of consecutive time steps to allow a human to interpret the temporal evolution of the network. In this paper, we propose a framework for dynamic network visualization in the on-line setting where only present and past graph snapshots are available to create the present layout. The proposed framework creates regularized graph layouts by augmenting the cost function of a static graph layout algorithm with a grouping penalty, which discourages nodes from deviating too far from other nodes belonging to the same group, and a temporal penalty, which discourages large node movements between consecutive time steps. The penalties increase the stability of the layout sequence, thus preserving the mental map. We introduce two dynamic layout algorithms within the proposed framework, namely dynamic multidimensional scaling (DMDS) and dynamic graph Laplacian layout (DGLL). We apply these algorithms on several data sets to illustrate the importance of both grouping and temporal regularization for producing interpretable visualizations of dynamic networks.Comment: To appear in Data Mining and Knowledge Discovery, supporting material (animations and MATLAB toolbox) available at http://tbayes.eecs.umich.edu/xukevin/visualization_dmkd_201