This paper provides an overview of research and development activities in the field of autonomous agents and multi-agent systems. It aims to identify key concepts and applications, and to indicate how they relate to one-another. Some historical context to the field of agent-based computing is given, and contemporary research directions are presented. Finally, a range of open issues and future challenges are highlighted.

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Adjustable autonomy refers to entities dynamically varying their own autonomy, transferring decision-making control to other entities (typically agents transferring control to human users) in key situations. Determining whether and when such transfers-of-control should occur is arguably the fundamental research problem in adjustable autonomy. Previous work has investigated various approaches to addressing this problem but has often focused on individual agent-human interactions. Unfortunately, domains requiring collaboration between teams of agents and humans reveal twokey shortcomings of these previous approaches. First, these approaches use rigid one-shot transfers of control that can result in unacceptable coordination failures in multiagent settings. Second, they ignore costs (e.g., in terms of time delays or eects on actions) to an agent's team due to such transfers-ofcontrol.

Gesture recognition is an important skill for robots that work closely with humans. Gestures help to clarify spoken commands and are a compact means of relaying geometric information. We have developed a real-time, three-dimensional gesture recognition system that resides on-board a mobile robot. Using a coarse three-dimensional model of a human to guide stereo measurements of body parts, the system is capable of recognizing six distinct gestures made by an unadorned human in an unaltered environment. An active vision approach focuses the vision system's attention on small, moving areas of space to allow for frame rate processing even when the person and/or the robot are moving. This paper describes the gesture recognition system, including the coarse model and the active vision approach. This paper also describes how the gesture recognition system is integrated with an intelligent control architecture to allow for complex gesture interpretation and complex robot action. Results from e...

This paper describes the New Millennium Remote Agent #NMRA# architecture for autonomous spacecraft control systems. This architecture integrates traditional real-time monitoring and control with constraintbased planning and scheduling, robust multi-threaded execution, and model-based diagnosis and recon#guration.

We expect a variety of autonomous systems, from rovers to life-support systems, to play a critical role in the success of future space missions. The crew and ground support personnel will want to control and be informed by these systems at varying levels of detail depending on the situation. Moreover, these systems will need to operate safely in the presence of people and cooperate with them effectively. We call such autonomous systems human-centered in contrast with traditional "black-box" autonomous systems. Our goal is to design a framework for human-centered autonomous systems that enables users to interact with these systems at whatever level of control is most appropriate whenever they so choose, but minimize the necessity for such interaction. This paper discusses on-going research at the NASA Ames Research Center and the Johnson Space Center in developing human-centered autonomous systems that can be used for space missions.

All intelligence relies on search --- for example, the search for an intelligent agent's next action. Search is only likely to succeed in resource-bounded agents if they have already been biased towards finding the right answer. In artificial agents, the primary source of bias is engineering. This dissertation

The Simple Temporal Network formalism permits significant flexibility in specifying the occurrence time of events in temporal plans. However, to retain this flexibility during execution, there is a need to propagate the actual execution times of past events so that the occurrence windows of future events are adjusted appropriately. Unfortunately, this may run afoul of tight real-time control requirements that dictate extreme efficiency. The performance may be improved by restricting the propagation. However, a fast, locally propagating, execution controller may incorrectly execute a consistent plan. To resolve this dilemma, we identify a class of dispatchable networks that are guaranteed to execute correctly under local propagation. We show that every consistent temporal plan can be reformulated as an equivalent dispatchable network, and we present an algorithm that constructs such a network. Moreover, the constructed network is shown to have a minimum number of edges among all such n...

Hierarchical organization has become an unfashionable model of intelligent control within some communities of both natural and artificial intelligence. What has replaced it are models based on parallel distributed processes, both neural and behavior based, or dynamical systems theory, which denies modularity, let alone rigorous structure. In this paper we present experimental results demonstrating an artificial reactive hierarchybased system that outperforms fully parallel systems in a highly dynamic environment with a large number of conflicting goals. This work is conducted in Tyrrell’s (1993) Simulated Environment and can be seen as an extension of his work on comparing action selection mechanisms. We observe that the hierarchical strategy has also been well demonstrated in nature. We argue that, for complex intelligences, preserving full reactivity may not be worth the cost in terms of the complexity of action selection.

The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the official policies or endorsements, either expressed or implied, of Carnegie Mellon University. The problem of efficient multirobot coordination has risen to the forefront of robotics research in recent years. Interest in this problem is motivated by the wide range of application domains demanding multirobot solutions. In general, multirobot coordination strategies assume either a centralized approach, where a single robot/agent plans for the group, or a distributed approach, where each robot is responsible for its own planning. Inherent to many centralized approaches are difficulties such as intractable solutions for large groups, sluggish response to changes in the local environment, heavy communication requirements, and brittle systems with single points of failure. The key advantage of centralized approaches is that they can produce globally optimal plans. While most distributed approaches can overcome the obstacles inherent to centralized approaches, they can only produce suboptimal plans. This work explores the development of a market-based architecture that will be inherently distributed, but will also opportunistically form centralized sub-groups to improve efficiency, and thus