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36
Towards flexible teamwork
- JOURNAL OF ARTIFICIAL INTELLIGENCE RESEARCH
, 1997
"... 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 obst ..."
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Cited by 570 (59 self)
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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 tting individual agents with precomputed coordination plans will not do, for their in flexibility can cause severe failures in teamwork, and their domain-specificity hinders reusability. Our central hypothesis is that the key to such flexibility and reusability isproviding 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.
Collaborative plans for complex group action
, 1996
"... The original formulation of SharedPlans by B. Grosz and C. Sidner ( 1990) was developed to provide a model of collaborative planning in which it was not necessary for one agent to have intentions-to toward an act of a different agent. Unlike other contemporaneous approaches (J.R. Searle, 1990), this ..."
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Cited by 543 (30 self)
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The original formulation of SharedPlans by B. Grosz and C. Sidner ( 1990) was developed to provide a model of collaborative planning in which it was not necessary for one agent to have intentions-to toward an act of a different agent. Unlike other contemporaneous approaches (J.R. Searle, 1990), this formulation provided for two agents to coordinate their activities without introducing any notion of irreducible joint intentions. However, it only treated activities that directly decomposed into single-agent actions, did not address the need for agents to commit to their joint activity, and did not adequately deal with agents having only partial knowledge of the way in which to perform an action. This paper provides a revised and expanded version of SharedPlans that addresses these shortcomings. It also reformulates Pollack’s ( 1990) definition of individual plans to handle cases in which a single agent has only partial knowledge; this reformulation meshes with the definition of SharedPlans. The new definitions also allow for contracting out certain actions. The formalization that results has the features required by Bratrnan’s ( 1992) account of shared cooperative activity and is more general than alternative accounts (H. Levesque et al., 1990; E. Sonenberg et al., 1992).
Reaching agreements through argumentation: a logical model and implementation
- ARTIFICIAL INTELLIGENCE
, 1998
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The Communicative Multiagent Team Decision Problem: Analyzing Teamwork Theories and Models
- JOURNAL OF ARTIFICIAL INTELLIGENCE RESEARCH
, 2002
"... Despite the significant progress in multiagent teamwork, existing research does not address the optimality of its prescriptions nor the complexity of the teamwork problem. Without a characterization of the optimality-complexity tradeoffs, it is impossible to determine whether the assumptions and app ..."
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Cited by 233 (21 self)
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Despite the significant progress in multiagent teamwork, existing research does not address the optimality of its prescriptions nor the complexity of the teamwork problem. Without a characterization of the optimality-complexity tradeoffs, it is impossible to determine whether the assumptions and approximations made by a particular theory gain enough efficiency to justify the losses in overall performance. To provide a tool for use by multiagent researchers in evaluating this tradeoff, we present a unified framework, the COMmunicative Multiagent Team Decision Problem (COM-MTDP). The COM-MTDP model combines and extends existing multiagent theories, such as decentralized partially observable Markov decision processes and economic team theory. In addition to their generality of representation, COM-MTDPs also support the analysis of both the optimality of team performance and the computational complexity of the agents' decision problem. In analyzing complexity, we present a breakdown of the computational complexity of constructing optimal teams under various classes of problem domains, along the dimensions of observability and communication cost. In analyzing optimality, we exploit the COM-MTDP's ability to encode existing teamwork theories and models to encode two instantiations of joint intentions theory taken from the literature. Furthermore, the COM-MTDP model provides a basis for the development of novel team coordination algorithms. We derive a domain-independent criterion for optimal communication and provide a comparative analysis of the two joint intentions instantiations with respect to this optimal policy. We have implemented a reusable, domain-independent software package based on COM-MTDPs to analyze teamwork coordination strategies, and we demons...
Agent Architectures for Flexible, Practical Teamwork
- In Proceedings of the National Conference on Artificial Intelligence
, 1997
"... Teamwork in complex, dynamic, multi-agent domains mandates highly flexible coordination and communication. 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 ..."
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Cited by 128 (15 self)
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Teamwork in complex, dynamic, multi-agent domains mandates highly flexible coordination and communication. 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 agent architectures with integrated teamwork capabilities. This fundamental shift in agent architectures is illustrated via an implemented candidate: STEAM. While STEAM is founded on the joint intentions theory, practical operationalization has required it to integrate several key novel concepts: (i) team synchronization to establish joint intentions; (ii) constructs for monitoring joint intentions and repair; and (iii) decision-theoretic communication selectivity (to pragmatically extend the joint intentions theory). Applications in three different complex domains, with empirical results, are presented. 1 1 In...
Planned Team Activity
, 1992
"... Agents situated in dynamic environments benefit from having a repertoire of plans, supplied in advance, that permit them to rapidly generate appropriate sequences of actions in response to important events. When agents can form teams, new problems emerge regarding the representation and execution of ..."
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Cited by 115 (20 self)
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Agents situated in dynamic environments benefit from having a repertoire of plans, supplied in advance, that permit them to rapidly generate appropriate sequences of actions in response to important events. When agents can form teams, new problems emerge regarding the representation and execution of joint actions. In this paper we introduce a language for representing joint plans for teams of agents, we describe how agents can organize the formation of a suitably skilled team to achieve a joint goal, and we explain how such a team can execute these plans to generate complex, synchronized team activity. The formalism provides a framework for representing and reasoning about joint actions in which various approaches to co-ordination and commitment can be explored. 1 Introduction A rational agent can be viewed as a system continuously receiving perceptual input from the environment in which it is embedded and responding by taking actions that affect that environment. It can be characte...
A Survey of Research in Distributed, Continual Planning
, 2000
"... Complex, real-world domains require a rethinking of traditional approaches to AI planning. Planning and executing the resulting plans in a dynamic environment requires a continual approachinwhich planning and execution are interleaved, there may be uncertaintyin the current and projected world ..."
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Cited by 89 (2 self)
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Complex, real-world domains require a rethinking of traditional approaches to AI planning. Planning and executing the resulting plans in a dynamic environment requires a continual approachinwhich planning and execution are interleaved, there may be uncertaintyin the current and projected world state, and replanning may be required when the situation changes or planned actions fail. Furthermore, complex planning and execution problems may require multiple computational agents and human planners to collaborate on a solution. In this article, we describe a new paradigm for planning in complex, dynamic environments, whichweterm distributed,continual planning (DCP). We argue that developing DCP systems will be necessary in order for planning applications to be successful in these environments. We give a historical overview of research leading up to the current state of the art in DCP, and describe research in distributed and continual planning. The increasing emphasis on r...
Guided Team Selection
- In Proceedings of the Second International Conference on Multi-Agent Systems
, 1996
"... Team selection or the process of selecting a group of agents that have complimentary skills to achieve a given goal(s) is an important aspect of collaborative activity in multi-agent systems. Traditionally, team selection is done by agents exchanging their skills, goals, plans, or current beliefs at ..."
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Cited by 40 (0 self)
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Team selection or the process of selecting a group of agents that have complimentary skills to achieve a given goal(s) is an important aspect of collaborative activity in multi-agent systems. Traditionally, team selection is done by agents exchanging their skills, goals, plans, or current beliefs at run-time and then forming a team. This method is impractical in time-critical domains. In this paper, we provide an alternative mechanism whereby users can specify the expertise of agents and teams and these specifications can be used to prune the number of potential teams at runtime. In addition, we also introduce the notion of allocations, which allow the user to specify the run-time constraints that need to be met before a team can be formed. We provide definitions of these concepts and illustrate through algorithms how they can be used in the team selection process. We conclude by analysing the algorithms and discussing related work. Keywords: Team Selection, Organization, Collaborative...
Socially Conscious Decision-Making
- In Proceedings of the Fourth International Conference on Autonomous Agents
"... The growing need for individually motivated agents to work collaboratively to satisfy shared goals has made it increasingly important to design agents that can make intelligent decisions in the context of commitments to group activities. Agents need to reconcile their intentions to do group-related ..."
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Cited by 36 (2 self)
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The growing need for individually motivated agents to work collaboratively to satisfy shared goals has made it increasingly important to design agents that can make intelligent decisions in the context of commitments to group activities. Agents need to reconcile their intentions to do group-related actions with other, conflicting actions. We describe the SPIRE experimental system which allows the process of intention reconciliation in team contexts to be simulated and studied. We define a measure of social consciousness, discuss its incorporation into the SPIRE system, and present several experiments that investigate the interaction in decision-making of measures of group and individual good. In particular, we investigate the effect of infinite and limited time horizons, different task densities, and varying levels of social consciousness on the utility of the group and the individuals it comprises. A key finding is that an intermediate level of social consciousness yields better results than an extreme commitment. We suggest preliminary principles for designers of collaborative agents based on the results.
Temporal Agent Programs
, 2000
"... The "agent program" framework introduced by Eiter, Subrahmanian and Pick (Artificial Intelligence, 108(1-2), 1999), supports developing agents on top of arbitrary legacy code. Such agents are continuously engaged in an "event occurs--> think--> act--> event occurs . . . " ..."
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Cited by 10 (2 self)
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The "agent program" framework introduced by Eiter, Subrahmanian and Pick (Artificial Intelligence, 108(1-2), 1999), supports developing agents on top of arbitrary legacy code. Such agents are continuously engaged in an "event occurs--> think--> act--> event occurs . . . " cycle. However, this framework has two major limitations: (1) all actions are assumed to have no duration, and (2) all actions are taken now, but cannot be scheduled for the future. In this paper, we present the concept of a "temporal agent program" (tap for short) and show that using taps, it is possible to build agents on top of legacy code that can reason about the past and about the future, and that can make temporal commitments for the future now. We develop a formal semantics for such agents, extending the concept of a status set proposed by Eiter et al., and develop algorithms to compute the status sets associated with temporal agent programs. Last, but not least, we show how taps support the decision making of collab...
