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This paper introduces a simple decentralized protocol that enables seperate planners to coordinate shared tasks and resolve conflicts over shared and private resources.
work offers evidence that distributed planning agents can greatly reduce communication costs by reasoning at abstract levels. While it is intuitive that improved search can reduce communication in such cases, there are other decisions about how to communicate plan information that greatly affect communication costs. This paper identifies cases independent of search where communicating at multiple levels of abstraction can exponentially decrease costs and where it can exponentially add costs. We conclude with a process for determining appropriate levels of communication based on characteristics of the domain.
Interacting agents that interleave planning and execution must reach consensus on their commitments to each other. For domains with varying degrees of interaction and different constraints on communication and computation, agents will require different coordination protocols in order to efficiently achieve their goals. ShAC (Shared Activity Coordination) is a framework for designing coordination protocols and an algorithm for continually coordinating agents using these protocols during execution. We show how a variety of protocols can be constructed using this framework and describe how ShAC coordinates two rovers and an orbiter in a simulated Mars scenario.
Interacting agents that interleave planning and execution must reach consensus on their commitments to each other. In domains where agents have varying degrees of interaction and different constraints on communication and computation, agents will require different coordination protocols in order to efficiently reach consensus. ShAC (Shared Activity Coordination) is a framework for designing coordination protocols with an algorithm for continually coordinating agents using these protocols during execution. We show how to construct a wide range of protocols using this framework and describe how ShAC coordinates two rovers and an orbiter in a simulated Mars scenario.
This paper examines the performance of simple learning rules in a complex adaptive system based on a coordination problem modeled on the El Farol problem. The key features of the El Farol problem are that it typically involves a medium number of agents and that agents' pay-off functions have a discontinuous response to increased congestion. First we consider a single adaptive agent facing a stationary environment. We demonstrate that the simple learning rules proposed by Roth and Er'ev can be extremely sensitive to small changes in the initial conditions and that events early in a simulation can affect the performance of the rule over a relatively long time horizon. In contrast, a reinforcement learning rule based on standard practice in the computer science literature converges rapidly and robustly. The situation is reversed when multiple adaptive agents interact: the RE algorithms often converge rapidly to a stable average aggregate attendance despite the slow and erratic behavior of individual learners, while the CS based learners frequently over-attend in the early and intermediate terms. The symmetric mixed strategy equilibria is unstable: all three learning rules ultimately tend towards pure strategies or stabilize in the medium term at non-equilibrium probabilities of attendance. The brittleness of the algorithms in different contexts emphasize the importance of thorough and thoughtful examination of simulation-based results.
NASA has initiated a significant thrust of research and development focused on providing the flight crew and air traffic managers automation aids to increase capacity in en route and terminal area operations through the use of flexible, more fuel-efficient routing, while improving the level of safety in commercial carrier operations. In that system development, definition of cognitive requirements for integrated multi-operator dynamic aiding systems is fundamental. In order to support that cognitive function definition, we have extended the Man Machine Integrated Design and Analysis System (MIDAS) to include representation of multiple cognitive agents (both human operators and intelligent aiding systems) operating aircraft, airline operations centers and air traffic control centers in the evolving airspace. The demands of this application require representation of many intelligent agents sharing world-models, and coordinating action/intention with cooperative scheduling of goals and actions in a potentially unpredictable world of operations. The MIDAS operator models have undergone significant development in order to understand the requirements for operator aiding and the impact of that aiding in the complex nondeterminate system of national airspace operations. The operator model's structure has been modified to include attention functions, action priority, and situation assessment. The cognitive function model has been expanded to include working memory operations including retrieval from long-term store, interference, visual-motor and verbal articulatory loop functions, and time-based losses. The operator's activity structures have been developed to include prioritization and interruption of multiple parallel activities among multiple operators, to provide for anticipation (knowledge of the intention and action of remote operators), and to respond to failures of the system and other operators in the system in situation-specific paradigms. The model's internal representation has been be modified so that multiple, autonomous sets of equipment will function in a scenario as the single equipment sets do now. In order to support the analysis requirements with multiple items of equipment, it is necessary for equipment to access the state of other equipment objects at initialization time (a radar object may need to access the position and speed of aircraft in its area, for example), and as a function of perception and sensor system interaction. The model has been improved to include multiple world-states as a function of equipment am operator interaction. The model has been used -1o predict the impact of warning and alert zones in aircraft operation, and, more critic-ally, the interaction of flight-deck based warning mechanisms and air traffic controller action in response to ground-based conflict prediction and alerting systems. In this operation, two operating systems provide alerting to two autonomous, but linked sets of operators, whose view of the system and whose dynamics in response are radically different. System stability and operator action was predicted using the MIDAS model.
Interacting agents that interleave planning and execution must reach consensus on their commitments to each other. In domains where agents have varying degrees of interaction and different constraints on communication and computation, agents will require different coordination protocols in order to efficiently reach consensus in real time. We briefly describe a largely unexplored class of realtime, distributed planning problems (inspired by interacting spacecraft missions), new challenges they pose, and a general approach to solving the problems. These problems involve self-interested agents that have infrequent communication but collaborate on joint activities. We describe a Shared Activity Coordination (SHAC) framework that provides a decentralized algorithm for negotiating the scheduling of shared activities over the lifetimes of separate missions, a soft, real-time approach to reaching consensus during execution with limited communication, and a foundation for customizing protocols for negotiating planner interactions. We apply SHAC to a realistic simulation of interacting Mars missions and illustrate the simplicity of protocol development.
Interacting agents that interleave planning and execution must reach consensus on their commitments to each other. In domains where agents have varying degrees of interaction and different constraints on communication and computation, agents will require different coordination protocols in order to efficiently reach consensus in real time. We briefly describe a largely unexplored class of real-time, distributed planning problems (inspired by interacting spacecraft missions), new challenges they pose, and a general approach to solving the problems. These problems involve self-interested agents that have infrequent communication but collaborate on joint activities. We describe a Shared Activity Coordination (SHAC) framework that provides a decentralized algorithm for negotiating the scheduling of shared activities in a dynamic environment, a soft, real-time approach to reaching consensus during execution with limited communication, and a foundation for customizing protocols for negotiating planner interactions. We apply SHAC to a realistic simulation of interacting Mars missions and illustrate the simplicity of protocol development.
This paper presents a set of studies in full mission simulation and the development of a predictive computational model of human performance in control of complex airspace operations. NASA and the FAA have initiated programs of research and development to provide flight crew, airline operations and air traffic managers with automation aids to increase capacity in en route and terminal area to support the goals of safe, flexible, predictable and efficient operations. In support of these developments, we present a computational model to aid design that includes representation of multiple cognitive agents (both human operators and intelligent aiding systems). The demands of air traffic management require representation of many intelligent agents sharing world-models, coordinating action/intention, and scheduling goals and actions in a potentially unpredictable world of operations. The operator-model structure includes attention functions, action priority, and situation assessment. The cognitive model has been expanded to include working memory operations including retrieval from long-term store, and interference. The operator's activity structures have been developed to provide for anticipation (knowledge of the intention and action of remote operators), and to respond to failures of the system and other operators in the system in situation-specific paradigms. System stability and operator actions can be predicted by using the model. The model's predictive accuracy was verified using the full-mission simulation data of commercial flight deck operations with advanced air traffic management techniques.
While past flight projects involved a single spacecraft in isolation, over forty proposed future missions involve multiple coordinated spacecraft. This paper presents characteristics of such missions in terms of properties of the phenomena being measured as well as the rationale for using multiple spacecraft. We describe the coordination problems associated with operating these missions and identify needed technologies.
r describes how an iterative repair planner/scheduler can reason about the activities of multiple spacecraft at abstract levels in order to greatly improve the scheduling of their use of shared resources.
r describes a way to schedule high level activities before distributing them across multiple rovers in order to coordinate the resultant use of shared resources regardless of how each rover decides how to perform its activities. We present an algorithm for summarizing the metric resource requirements of an abstract activity based n the resource usages of its potential refinements.
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t an algorithm for summarizing the metric resource requirements of an abstract activity based on the resource usages of its potential refinements.
This paper compares and contrasts several coordination schemes for a system that continuously plans to control collections of rovers (or spacecraft) using collective mission goals, instead of goals or command sequences for each spacecraft.
There is an increasing need for space missions to be able to collaboratively (and competitively) develop plans both within and across missions. In addition, interacting spacecraft that interleave onboard planning and execution must reach consensus on their commitments to each other prior to execution.
Distributed Network Scheduling is the scheduling of future communications of a network by nodes in the network. This report details software for doing this onboard spacecraft in a remote network. While prior work on distributed scheduling has been applied to remote spacecraft networks, the software reported here focuses on modeling communication activities in greater detail and including quality of service constraints. Our main results are based on a Mars network of spacecraft and include identifying a maximum opportunity of improving traverse exploration rate a factor of three; a simulation showing reduction in one-way delivery times from a rover to Earth from as much as 5 to 1.5 hours; simulated response to unexpected events averaging under an hour onboard; and ground schedule generation ranging from seconds to 50 minutes for 15 to 100 communication goals.