The GPGP/TAEMS domain-independent coordination framework for small agent groups was first described in 1992 and then more fully detailed in an ICMAS'95 paper. In this paper, we discuss the evolution of this framework which has been motivated by its use in a number of applications, including: information gathering and management, intelligent home automation, distributed situation assessment, coordination of concurrent engineering activities, hospital scheduling, travel planning, repair service coordination and supply chain management. First, we review the basic architecture of GPGP and then present extensions to the TAEMS domain-independent representation of agent activities. We next describe extensions to GPGP that permit the representation of situation-specific coordination strategies and social laws as well as making possible the use of GPGP in large agent organizations. Additionally, we discuss a more encompassing view of commitments that takes into account uncertainty in commitments. We then present new coordination mechanisms for use in resource sharing and contracting, and more complex coordination mechanisms that use a cooperative search among agents to find appropriate commitments. We conclude with a summary of the major ideas underpinning GPGP, an analysis of the applicability of the GPGP framework including performance issues, and a discussion of future research directions.

Process-based composition of Web services has recently gained significant momentum for the implementation of inter-organizational business collaborations. In this approach, individual Web services are choreographed into composite Web services whose integration logics are expressed as composition schema. In this paper, we present a goal-directed composition framework to support on-demand business processes. Composition schemas are generated incrementally by a rule inference mechanism based on a set of domain-specific business rules enriched with contextual information. In situations where multiple composition schemas can achieve the same goal, we must first select the best composition schema, wherein the best schema is selected based on the combination of its estimated execution quality and schema quality. By coupling the dynamic schema creation and quality-driven selection strategy in one single framework, we ensure that the generated composite service comply with business rules when being adapted and optimized. 46 Distrib Parallel Databases (2008) 24: 45-72

At the dawn of the 21 st century, environmental scientists are collecting more data more rapidly than at any time in the past. Nowhere is this change more evident than in the advent of sensor networks able to collect and process (in real time) simultaneous measurements over broad areas and at high sampling rates. At the same time there has been great progress in the development of standards, methods, and tools for data analysis and synthesis, including a new standard for descriptive metadata for ecological datasets (Ecological Metadata Language) and new workflow tools that help scientists to assemble datasets and to diagram, record, and execute analyses. However these developments (important as they are) are not yet sufficient to guarantee the reliability of datasets created by a scientific process -the complex activity that scientists carry out in order to create a dataset. We define a dataset to be reliable when the scientific process used to create it is (1) reproducible and (2) analyzable for potential defects.To address this problem we propose the use of an analytic web, a formal representation of a scientific process that consists of three coordinated graphs (a data-flow graph, a dataset-derivation graph, and a process-derivation graph) originally developed for use in software engineering. An analytic web meets the two key requirements for ensuring dataset reliability: (1) a complete audit trail of all artifacts (e.g., datasets, code, models) used or created in the execution of the scientific process that created the dataset, and (2) detailed process metadata that precisely describe all sub-processes of the scientific process. Construction of such metadata requires the semantic features of a high-level process definition language.In this paper we illustrate the use of an analytic web to represent the scientific process of constructing estimates of ecosystem water flux from data gathered by a complex, realtime multi-sensor network. We use Little-JIL, a high-level process definition language, to precisely and accurately capture the analytical processes involved. We believe that incorporation of this approach into existing tools and evolving metadata specifications (such as EML) will yield significant benefits to science. These benefits include: complete and accurate representations of scientific processes; support for rigorous evaluation of such processes for logical and statistical errors and for propagation of measurement error; and assurance of dataset reliability for developing sound models and forecasts of environmental change.3

Abstract. This paper describes experience in applying a resource management system to problems in two areas of agent and activity coordination. In the paper we argue that precise speci cation of resources is important in activity a n d a g e n t coordination. The tasks and actions that are to be coordinated invariably require resources, and the scarcity or abundance of resources can make a considerable di erence in how t o b e s t coordinate the tasks and actions. That being the case, we propose the use of a resource model. We observe t h a t p a s t w ork on resource modeling does not meet our needs, as the models tend to be either too informal (as in management resource modeling) to support de nitive analysis, or too narrow in scope (as in the case of operating system resource modeling) to support speci cation of the diverse tasks we h a ve in mind. In this paper we i n troduce a general approach and some key concepts in a resource modeling and management system that we h a ve d e v eloped. Although rigorous and complete speci cation of the model and system is beyond the scope of this paper, the descriptions provided su ce to support explanation of two experiences we have had in applying our resource system. In one case we have added resource speci cations to a process program. In another case we used resource speci cations to augment a m ultiagent s c heduling system. In both cases, the result was far greater clarity and precision in the process and agent coordination speci cations, and validation of the e ectiveness of our resource modeling and management approaches. A range of future work in this area is indicated at the conclusion of the paper.

This paper describes our experiences in exploring the applicability of software engineering approaches to scientific data management problems. Specifically, this paper describes how process definition languages can be used to expedite production of scientific datasets as well as to generate documentation of their provenance. Our approach uses a process definition language that incorporates powerful semantics to encode scientific processes in the form of a Process Definition Graph (PDG). The paper describes how execution of the PDG-defined process can generate Dataset Derivation Graphs (DDGs), metadata that document how the scientific process developed each of its product datasets. The paper uses an example to show that scientific processes may be complex and to illustrate why some of the more powerful semantic features of the process definition language are useful in supporting clarity and conciseness in representing such processes. This work is similar in goals to work generally referred to as Scientific Workflow. The paper demonstrates the contribution that software engineering can make to this domain.