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Teamwork in Cyberspace: Using TEAMCORE to Make Agents Team-Ready

TL;DR: In complex, dynamic and uncertain environments extending from disaster rescue missions, to future battlefields, to monitoring and surveillance tasks, to virtual training environments, tofuture robotic space missions, intelligent agents will play a key role in information gathering and filtering, as well as in task planning and execution.
Abstract: In complex, dynamic and uncertain environments extending from disaster rescue missions, to future battlefields, to monitoring and surveillance tasks, to virtual training environments, to future robotic space missions, intelligent agents will play a key role in information gathering and filtering, as well as in task planning and execution. Although physically distributed on a variety of platforms, these agents will interact with information sources, network facilities, and other agents via cyberspace, in the form of the Internet, Intranet, the secure defense communication network, or other forms of cyberspace. Indeed, it now appears well accepted that cyberspace will be (if it is not already) populated by a vast number of such distributed, individual agents.

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Citations
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Journal ArticleDOI
TL;DR: This analysis is based on some of the principles of human-centered computing that have developed individually and jointly over the years, and is adapted from a more comprehensive examination of common ground and coordination.
Abstract: We propose 10 challenges for making automation components into effective "team players" when they interact with people in significant ways. Our analysis is based on some of the principles of human-centered computing that we have developed individually and jointly over the years, and is adapted from a more comprehensive examination of common ground and coordination.

532 citations

Book ChapterDOI
01 Oct 2003
TL;DR: This work summarizes the interim results of the study on the problem of work practice modeling and human-agent collaboration in space applications, the development of a broad model of human- agent teamwork grounded in practice, and the integration of the Brahms, KAoS, and NOMADS agent frameworks.
Abstract: We give a preliminary perspective on the basic principles and pitfalls of adjustable autonomy and human-centered teamwork. We then summarize the interim results of our study on the problem of work practice modeling and human-agent collaboration in space applications, the development of a broad model of human-agent teamwork grounded in practice, and the integration of the Brahms, KAoS, and NOMADS agent frameworks We hope our work will benefit those who plan and participate in work activities in a wide variety of space applications, as well as those who are interested in design and execution tools for teams of robots that can function as effective assistants to humans.

107 citations


Cites background from "Teamwork in Cyberspace: Using TEAMC..."

  • ...In extending traditional teamwork theory [28; 82], we seek to incorporate the best of previous research on human-centered collaboration and teamwork, while simultaneously grounding new findings in our own work practice study experience....

    [...]

Book ChapterDOI
16 Jun 2003
TL;DR: In this paper, the authors focus on the technical and social aspects of how to make agents acceptable to people and devise a computational structure that guarantees that from the technical standpoint all is under control, and provide reassurance to people that all is working according to plan.
Abstract: Because ever more powerful intelligent agents will interact with people in increasingly sophisticated and important ways, greater attention must be given to the technical and social aspects of how to make agents acceptable to people [4], p. 51]. The technical challenge is to devise a computational structure that guarantees that from the technical standpoint all is under control. We want to be able to help ensure the protection of agent state, the viability of agent communities, and the reliability of the resources on which they depend. To accomplish this, we must guarantee, insofar as is possible, that the autonomy of agents can always be bounded by explicit enforceable policy that can be continually adjusted to maximize the agents. effectiveness and safety in both human and computational environments. The social challenge is to ensure that agents and people interact gracefully and to provide reassurance to people that all is working according to plan. We want agents to be designed to fit well with how people actually work together. Explicit policies governing human-agent interaction, based on careful observation of work practice and an understanding of current social science research, can help assure that effective and natural coordination, appropriate levels and modalities of feedback, and adequate predictability and responsiveness to human control are maintained. These factors are key to providing the reassurance and trust that are the prerequisites to the widespread acceptance of agent technology for non-trivial applications.

65 citations

Journal ArticleDOI
TL;DR: A decision-theoretic technique based on Markov decision processes is presented to enable persistent teams to overcome limitations of the model-based approach to flexible teamwork.
Abstract: Teamwork is a critical capability in multi-agent environments. Many such environments mandate that the agents and agent-teams must be persistent i.e., exist over long periods of time. Agents in such persistent teams are bound together by their long-term common interests and goals. This paper focuses on flexible teamwork in such persistent teams. Unfortunately, while previous work has investigated flexible teamwork, persistent teams remain unexplored. For flexible teamwork, one promising approach that has emerged is model-based, i.e., providing agents with general models of teamwork that explicitly specify their commitments in teamwork. Such models enable agents to autonomously reason about coordination. Unfortunately, for persistent teams, such models may lead to coordination and communication actions that while locally optimal, are highly problematic for the team's long-term goals. We present a decision-theoretic technique based on Markov decision processes to enable persistent teams to overcome such limitations of the model-based approach. In particular, agents reason about expected team utilities of future team states that are projected to result from actions recommended by the teamwork model, as well as lower-cost (or higher-cost) variations on these actions. To accommodate real-time constraints, this reasoning is done in an any-time fashion. Implemented examples from an analytic search tree and some real-world domains are presented.

63 citations


Cites background from "Teamwork in Cyberspace: Using TEAMC..."

  • ...Another key issue is a team’s organizational adaptation with experience in a given environment, for instance, by changing tasks assigned to different individuals or subteams [5, 36]....

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References
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Book
01 Jan 1958
TL;DR: In this paper, the origins of epistemological theory are discussed and the layout of argument and modal arguments are discussed, as well as the history of working logic and idealised logic.
Abstract: Preface Introduction 1. Fields of argument and modals 2. Probability 3. The layout of arguments 4. Working logic and idealised logic 5. The origins of epistemological theory Conclusion References Index.

6,407 citations


"Teamwork in Cyberspace: Using TEAMC..." refers background in this paper

  • ...CONSA addresses the problems of negotiation to resolve these c onflicts, based o n an argumentation pattern proposed in [ Toulmin58 ]....

    [...]

  • ...CONSA addresses the problems of negotiation to resolve these conflicts, based on an argumentation pattern proposed in [Toulmin58]....

    [...]

Book
01 Dec 1990
TL;DR: In this paper, the authors propose a unified theory of cognition for the task of the Task of the Book Foundations of Cognitive Science Behaving Systems Knowledge Systems Representation Machines and Computation Symbols Architectures Intelligence Search and Problem Spaces Preparation and Deliberation Summary Human Cognitive Architecture The Human is a Symbol System System Levels The Time Scale of Human Action The Biological Band The Neural Circuit Level The Real-Time Constraint on Cognition The Cognitive Band The Level of Simple Operations The First Level of Composed Operations The Intendedly Rational Band Higher Bands: Social, Historical
Abstract: Introduction The Nature of Theories What Are Unified Theories of Cognition? Is Psychology Ready for Unified Theories? The Task of the Book Foundations of Cognitive Science Behaving Systems Knowledge Systems Representation Machines and Computation Symbols Architectures Intelligence Search and Problem Spaces Preparation and Deliberation Summary Human Cognitive Architecture The Human Is a Symbol System System Levels The Time Scale of Human Action The Biological Band The Neural Circuit Level The Real-Time Constraint on Cognition The Cognitive Band The Level of Simple Operations The First Level of Composed Operations The Intendedly Rational Band Higher Bands: Social, Historical, and Evolutionary Summary Symbolic Processing for Intelligence The Central Architecture for Performance Chunking The Total Cognitive System RI-Soar: Knowledge-Intensive and Knowledge-Lean Operation Designer-Soar: Difficult Intellectual Tasks Soar as an Intelligent System Mapping Soar onto Human Cognition Soar and the Shape of Human Cognition Summary Immediate Behavior The Scientific Role of Immediate-Response Data Methodological Preliminaries Functional Analysis of Immediate Responses The Simplest Response Task (SRI) The Two-Choice Response Task (2CRT) Stimulus-Response Compatibility (SRC) Discussion of the Three Analyses Item Recognition Typing Summary Memory, Learning, and Skill The Memory and Learning Hypothesis of Soar The Soar Qualitative Theory of Learning The Distinction between Episodic and Semantic Memory Data Chunking Skill Acquisition Short-Term Memory (STM) Summary Intendedly Rational Behavior Ciyptarithmetic Syllogisms Sentence Verification Summary Along the Frontiers Language Development The Biological Band The Social Band The Role of Applications How to Move toward Unified Theories of Cognition References Name Index Subject Index

4,129 citations

Journal ArticleDOI
TL;DR: A revised and expanded version of SharedPlans that reformulates Pollack's (1990) definition of individual plans to handle cases in which a single agent has only partial knowledge and has the features required by Bratman's (1992) account of shared cooperative activity.

1,112 citations

Posted Content
TL;DR: In STEAM, team members monitor the team's and individual members' performance, reorganizing the team as necessary, and decision-theoretic communication selectivity in STEAM ensures reduction in communication overheads of teamwork, with appropriate sensitivity to the environmental conditions.
Abstract: Many AI researchers are today striving to build agent teams for complex, dynamic multi-agent domains, with intended applications in arenas such as education, training, entertainment, information integration, and collective robotics. Unfortunately, uncertainties in these complex, dynamic domains obstruct coherent teamwork. In particular, team members often encounter differing, incomplete, and possibly inconsistent views of their environment. Furthermore, team members can unexpectedly fail in fulfilling responsibilities or discover unexpected opportunities. Highly flexible coordination and communication is key in addressing such uncertainties. Simply fitting individual agents with precomputed coordination plans will not do, for their inflexibility can cause severe failures in teamwork, and their domain-specificity hinders reusability. Our central hypothesis is that the key to such flexibility and reusability is providing agents with general models of teamwork. Agents exploit such models to autonomously reason about coordination and communication, providing requisite flexibility. Furthermore, the models enable reuse across domains, both saving implementation effort and enforcing consistency. This article presents one general, implemented model of teamwork, called STEAM. The basic building block of teamwork in STEAM is joint intentions (Cohen & Levesque, 1991b); teamwork in STEAM is based on agents' building up a (partial) hierarchy of joint intentions (this hierarchy is seen to parallel Grosz & Kraus's partial SharedPlans, 1996). Furthermore, in STEAM, team members monitor the team's and individual members' performance, reorganizing the team as necessary. Finally, decision-theoretic communication selectivity in STEAM ensures reduction in communication overheads of teamwork, with appropriate sensitivity to the environmental conditions. This article describes STEAM's application in three different complex domains, and presents detailed empirical results.

966 citations

Journal ArticleDOI
TL;DR: In this paper, the authors present a general, implemented model of teamwork, called STEAM, which is based on agents' building up a (partial) hierarchy of joint intentions (this hierarchy is seen to parallel Grosz & Kraus's partial Shared-Plans, 1996).
Abstract: Many AI researchers are today striving to build agent teams for complex, dynamic multi-agent domains, with intended applications in arenas such as education, training, entertainment, information integration, and collective robotics. Unfortunately, uncertainties in these complex, dynamic domains obstruct coherent teamwork. In particular, team members often encounter differing, incomplete, and possibly inconsistent views of their environment. Furthermore, team members can unexpectedly fail in fulfilling responsibilities or discover unexpected opportunities. Highly flexible coordination and communication is key in addressing such uncertainties. Simply fitting individual agents with precomputed coordination plans will not do, for their inflexibility can cause severe failures in teamwork, and their domain-specificity hinders reusability. Our central hypothesis is that the key to such flexibility and reusability is providing agents with general models of teamwork. Agents exploit such models to autonomously reason about coordination and communication, providing requisite flexibility. Furthermore, the models enable reuse across domains, both saving implementation effort and enforcing consistency. This article presents one general, implemented model of teamwork, called STEAM. The basic building block of teamwork in STEAM is joint intentions (Cohen & Levesque, 1991b); teamwork in STEAM is based on agents' building up a (partial) hierarchy of joint intentions (this hierarchy is seen to parallel Grosz & Kraus's partial Shared-Plans, 1996). Furthermore, in STEAM, team members monitor the team's and individual members' performance, reorganizing the team as necessary. Finally, decision-theoretic communication selectivity in STEAM ensures reduction in communication overheads of teamwork, with appropriate sensitivity to the environmental conditions. This article describes STEAM's application in three different complex domains, and presents detailed empirical results.

861 citations