Chongjie Zhang

Shelter from the storm: building a safe archive in a hostile world

The storing of data and configuration files related to scientific experiments is vital if those experiments are to remain reproducible, or if the data is to be shared easily. The prescence of historical (observed) data is also important in order to assist in model evaluation and development. This paper describes the design and implementation process for a data archive, which was required for a coastal modelling project. The construction of the archive is described in detail, from its design through to deployment and testing. As we will show, the archive has been designed to tolerate failures in its communications with external services, and also to ensure that no information is lost if the archive itself fails, i.e. upon restarting, the archive will still be in exactly the same state.

Grid portal solutions: a comparison of GridPortlets and OGCE

In this paper we discuss two of the major Grid portal solutions, the Open Grid Computing Environments Collaboratory (OGCE) and GridPortlets, both of which provide basic tools that portal developers can use to interact with Grid middleware when designing their own custom or application‐specific Grid portals. We investigate and compare what each of these packages provides, discuss their advantages and disadvantages, and identify missing features vital for Grid portal development. The main purpose of this paper is to identify what current toolkits provide, reveal some of their limitations, and provide motivation for the evolution of Grid portal solutions. Application groups should find this paper useful in helping to choose an appropriate Grid portal toolkit for building their Grid portals rapidly in a flexible and modular way. Copyright

An Ensemble Architecture for Learning Complex Problem-Solving Techniques from Demonstration

We present a novel ensemble architecture for learning problem-solving techniques from a very small number of expert solutions and demonstrate its effectiveness in a complex real-world domain. The key feature of our “Generalized Integrated Learning Architecture” (GILA) is a set of heterogeneous independent learning and reasoning (ILR) components, coordinated by a central meta-reasoning executive (MRE). The ILRs are weakly coupled in the sense that all coordination during learning and performance happens through the MRE. Each ILR learns independently from a small number of expert demonstrations of a complex task. During performance, each ILR proposes partial solutions to subproblems posed by the MRE, which are then selected from and pieced together by the MRE to produce a complete solution. The heterogeneity of the learner-reasoners allows both learning and problem solving to be more effective because their abilities and biases are complementary and synergistic. We describe the application of this novel learning and problem solving architecture to the domain of airspace management, where multiple requests for the use of airspaces need to be deconflicted, reconciled, and managed automatically. Formal evaluations show that our system performs as well as or better than humans after learning from the same training data. Furthermore, GILA outperforms any individual ILR run in isolation, thus demonstrating the power of the ensemble architecture for learning and problem solving.

An application portal for collaborative coastal modeling

We describe the background, architecture and implementation of a user portal for the SCOOP coastal ocean observing and modeling community. SCOOP is engaged in the real‐time prediction of severe weather events, including tropical storms and hurricanes, and provides operational information including wind, storm surge and resulting inundation, which are important for emergency management. The SCOOP portal, built with the GridSphere Framework, currently integrates customized Grid portlet components for data access, job submission, resource management and notification. Copyright

A workflow approach to designed reservoir study

Reservoir simulations are commonly used to predict the performance of oil and gas reservoirs, taking into account a myriad of uncertainties in the geophysical structure of the reservoir as well as operational factors such as well location. Designed reservoir study provides a robust tool to quantify the impact of uncertainties in model input variables, and can be used to simulate, analyze, and optimize reservoir development. However, such studies are computationally challenging, involving massive (terabyte or petabyte) geographically distributed datasets and requiring hundreds or tens of thousands of simulation runs. Providing petroleum engineers with integrated workflow through a secure and easy-to-use user interface will enable new advanced reservoir studies. This paper describes the workflow solution and user interface designed and implemented for reservoir uncertainty analysis in the UCoMS project ( Ubiquitous Computing and Monitoring System for discovery and management of energy resources).

Co-optimizing task and motion planning

Solutions to robotic manipulation problems can be substantially improved through integrated task and motion planning. Existing approaches typically focus on satisfaction, finding a feasible solution, instead of optimization. We formulate large-scale robotic manipulation problems as multi-level optimization, incorporating task, action, and motion planning. We develop an integrated planning approach for solving this optimization problem and generating a combined motion plan for a robot to optimize a task-level objective. This approach utilizes a combinatorial search algorithm for task planning and incrementally exploits information from lower-level optimization to improve the high-level task plan. Empirical results show that this integrated approach not only significantly outperforms a traditional top-down approach in solution quality, but also avoids infeasible lower-level motion plans.