We are investigating techniques for developing distributed and adaptive collections of agents that coordinate to retrieve, lter and fuse information relevant to the user, task and situation, as well as anticipate a user's information needs. In our system of agents, information gathering is seamlessly integrated with decision support. The task for which particular information is requested of the agents does not remain in the user's head but it is explicitly represented and supported through agent collaboration. In this paper we present the distributed system architecture, agent collaboration interactions, and a reusable set of software components for constructing agents. We call this reusable multi-agent computational infrastructure RETSINA Reusable Task Structure-based Intelligent Network Agents. It has three types of agents. Interface agents interact with the user receiving user speci cations and delivering results. They acquire, model, and utilize user preferences to guide system coordination in support of the user's tasks. Task agents help users perform tasks by formulating problem solving plans and carrying out these plans through querying and exchanging information with other software agents. Information agents provide intelligent access to a heterogeneous collection of information sources. We h a ve implemented this system framework and are developing collaborating agents in diverse complex real world tasks, such as organizational decision making the PLEIADES system, and nancial portfolio management the WARREN system.

Summary: We have developed a web tool to predict Gene Ontology (GO) terms. The tool accepts an input DNA or protein sequence, and uses BLAST to identify homologous sequences in GO annotated databases. A graph is returned to the user via email. Availability: The tool is freely available at: http://udgenome.ags.udel.edu/frm_go.html/

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.

The distributed coordination problem can be described as "how should the local scheduling of activities at each agent be affected by non-local concerns and constraints?" Partial global planning (PGP) is a flexible approach to distributed coordination that allows agents to respond dynamically to their current situation. It is based on detecting relationships in the computational goal structures of the distributed agents. However, the detailed PGP mechanisms depend on the existence and availability of certain characteristics and structures that are idiosyncratic to the Distributed Vehicle Monitoring Testbed (DVMT). Generalized Partial Global Planning tries to extend the PGP approach by communicating more abstract and hierarchically organized information, detecting in a general way the coordination relationships that are needed by the partial global planning mechanisms, and separating the process of coordination from local scheduling. This new characterization of partial global planning has less communication overhead and can be more easily adapted and extended to new styles of problem solving and new multi-agent environments that have different characteristics from the original DVMT. This paper first describes the coordination problem as it was viewed by the PGP algorithm, and then extensions to that problem. It then briefly describes our model of task structures and coordination relationships. Finally, we show how the PGP algorithm, as an example, can be described using our method.

There are many formal approaches to specifying how the mental state of an agent entails the particular actions it will perform. These approaches put the agent at the center of analysis. For some questions and purposes, it is more realistic and convenient for the center of analysis to be the task environment, domain or society of which agents will be a part. This paper presents such a task environment‐oriented modeling framework that can work hand in hand with more agent‐centered approaches. Our approach features careful attention to the quantitative computational interrelationships between tasks, to what information is available (and when) to update an agent's mental state and to the general structure of the task environment rather than single‐instance examples. A task environment model can be used for both analysis and simulation, it avoids the methodological problems of relying solely on single‐instance examples and provides concrete, meaningful characterizations with which to state general theories. This paper is organized around an example model of co‐operative problem solving in a distributed sensor network.