SELECTING ORGANIZATIONAL STRUCTURES FOR ADVANCED MULTI-AUV SYSTEMS∗

Current multi-AUV systems rely on a human to define their organizations or else organize themselves into very simple kinds of organizations. While this is sufficient for simple missions involving few AUVs, it will not be adequate for future multi-AUV systems involving large numbers of AUVs that perform complex missions, are fielded for long periods of time, and/or cannot communicate with a human. In the CoDA project, we are developing techniques to allow such advanced systems to self-organize and to reorganize as necessary to effectively carry out their missions. This paper discusses autonomous organization design for such systems. The approach draws on the organization design literature and will represent organization structures (e.g., hierarchies, teams, markets, etc.) appropriate for the AUV domain. These will be linked to representations of contexts in which the organizations is predicted to be useful. Once CoDA recognizes the current situation as an instance of a known context, then the organization structure can be retrieved and instantiated. The work is being developed in the domain of autonomous oceanographic sampling networks (AOSNs), in which groups of AUVs and other instrument platforms cooperate to return data over a long time period from an area of interest in the ocean. The concept of autonomous multiagent systems for ocean applications dates back to the early MAUV (Albus, 1988) and MAVIS (Turner et al ., 1991) projects and was given significant substance and refinement by the idea of autonomous oceano∗This work was funded in part by grants N0001-14-96-15009 and N0001-14-98-1-0648 from the Office of Naval Research. The content of this paper does not necessarily reflect the position or the policy of the U.S. government, and no official endorsement should be inferred. The authors would like to thank Kim McKeage, Wenyu Zhu, and Sheri St. Pierre for their contributions to this work. graphic sampling networks (AOSNs; Curtin et al ., 1993). Recent years have seen the development and fielding of simple multiagent systems comprised of autonomous underwater vehicles (AUVs), many of which were reported at a recent workshop focused on multi-AUV systems. These have taken on real-world missions, typically under human control, and usually with few vehicles. Much remains to be done, however, before large-scale systems can function autonomously for long periods of time doing complex, demanding missions, especially when the system will need to be re-tasked or reconfigured over time. Issues of autonomous organization and reorganization, task assignment, common command languages, communication languages, and problemsolving protocols must all be addressed before the vision of a fully-capable AOSN can become a reality. The CoDA (Cooperative Distributed AOSN control) project (Turner & Turner, 1998, 2001) focuses on developing mechanisms to support the realization of heterogeneous, flexible multiagent systems of AUVs for ocean science. We are concerned with advanced AOSN-type systems that will be comprised of a large number of heterogeneous agents (AUVs, other instrument platforms, etc.). Furthermore, these systems will be deployed for long periods of time. They will be open systems (Hewitt, 1986), meaning that agents will come and go over time, for example, due to failure. Such systems must be able to self-organize to fit their initial circumstances and to reorganize as the situation—and the system itself—changes. Work in CoDA is in three areas: problem-solving protocols to support organization, reorganization, and work on mission goals; assigning mission tasks to agents based on their capabilities; and selecting the appropriate organization for the agents given the current situation. Previous pa1IEEE/OES Autonomous Underwater Vehicles 2004 (AUV’04): A Workshop on Multiple AUV Operations, Sebasco Estates, Maine. pers have reported on the first two of these (Turner & Turner, 1998, 2001). In this paper, we focus on work in progress on organization selection. The task of selecting an organization for a group of agents is context-dependent: which organization will work best depends on the environment, the mission, and which agents are available. For example, the severe communication bandwidth limitations of the underwater environment precludes some communication-intensive organizations, such as those based on consensus, from being used in most situations. Missions that are readily decomposable into independent or nearly-independent tasks lend themselves to hierarchies, whereas others may require an organization more like a team or a committee. If the system contains no agents capable of managing others, then hierarchies are impossible. The task of selecting an organization, then, is one of matching the current situation’s features to those for which a given organization is appropriate. Since the organization depends on these kinds of features of the situation, as the situation changes, the system will need to be reorganized. Our approach treats organization selection as a diagnostic process. One or more agents in the multiAUV system use features of the system’s current situation to diagnose the situation as being an instance of one or more known classes of situations, called contexts . The agents will have been given knowledge about the contexts, in particular how to behave when in the contexts, as part of knowledge structures called contextual schemas (c-schemas; Turner, 1998). Part of the knowledge contained in a cschema representing a kind of situation is what organizational structure (e.g., a hierarchy of some form, a team, a committee, etc.) is appropriate for the system to use in that context. For this approach, we draw on our past and ongoing work on context-sensitive reasoning for autonomous underwater vehicle control. We also are making use of the extensive literature on organizational design for human organizations for information about the kinds of organizations that are possible. Obviously, some human organizations may not be appropriate for AOSNs. However, many are, at least in broad outline, and we are identifying the features of situations in the AOSN domain that would suggest which organizational structure to use. In the remainder of the paper, we first discuss organizing multi-AUV systems, then turn to the particular approach being developed in CoDA, including how to represent organization structures, how to link situations to the organization structures appropriate for them, and how to assess the agents’ current situation. Organizing Multi-AUV Systems Not all multi-AUV systems will need the ability to create their own organizations. For example, current work on AOSNs either has a human design the agent’s organization or the agents self-organize into extremely simple organizations, such as a swarm. However, we are most interested here in those circumstances in which a multi-AUV system will need the capability to self-organize and reorganize as the situation demands. This need may arise, for instance, in missions in which there is uncertainty and little or no possibility of communication with human users once the system is deployed. An example would be a mine-hunting mission in which AUVs are deployed some distance away from the minefield (e.g., by submarine launch or air drop). It is likely in this case that: there is uncertainty about the conditions obtaining at the work site; some of the AUVs may not make it to the work site, and so the composition of the overall system is uncertain; vehicle failure is likely during the mission; and communication from the work site is likely to be extremely limited or nonexistent. Other examples include teams of AUVs operating under ice (AUV failures may occur, communication limited or impossible); and recovery of a downed aircraft’s “black box” under conditions of severe sea state (uncertain work environment, difficult to communicate with base); and long-duration AOSN missions (vehicles entering or leaving, environmental change, desire for no dedicated human controller). Once we decide to give a multi-AUV system the ability to self-organize (and reorganize), the questions naturally arise: • Which organizations are possible to choose from? • How to select the best organization for the current situation? These questions have long been examined for human organizations in the field of organizational design. We can look there for guidance for multi-AUV systems, realizing that some organizations may not work in the non-human case and that some changes will need to be made to some organizations. Organization Structures An organization structure is “the system of task, reporting, and authority relationships within which the work of the organization is done” (Moorhead & Griffin, 1998). Many different organization structures have evolved for human organizations, some of which have direct analogs for multi-AUV systems. These can be grouped into several high-level categories. Probably the most familiar type used in current multi-AUV systems (e.g., Phoha et al ., 2001) is the simple hierarchy, in which a single manager controls a group of subordinates. Other kinds of hierarchies exist as well. Multi-layer hierarchies, for example, introduce one or more layers of middle managers between the topmost manager and the workers. Hierarchies can also be distinguished by how work is distributed, as well. Product hierarchies are those with departments associated with products (e.g., consumer product division, etc.), while functional hierarchies are those with departments associated with functions (e.g., engineering, marketing, sales, etc.). For AUV systems, a product hierarchy might correspond, for example, to organizing the system with one “department” working on search while another works on data sampling, or organizing “departments” by location. A functional hierarchy would correspond to having “departments” of AUVs with similar sensors, effectors, or problem-solving expertise. There are