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JASON AND THE GOLDEN FLEECE OF AGENT-ORIENTED PROGRAMMING
"... This chapter describes Jason, an interpreter written in Java for an extended version of AgentSpeak, a logic-based agent-oriented programming language that is suitable for the implementation of reactive planning systems according to the BDI architecture. We describe both the language and the various ..."
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Cited by 75 (21 self)
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This chapter describes Jason, an interpreter written in Java for an extended version of AgentSpeak, a logic-based agent-oriented programming language that is suitable for the implementation of reactive planning systems according to the BDI architecture. We describe both the language and the various features and tools available in the platform.
Allocating Tasks in Extreme Teams
- AAMAS'05
, 2005
"... Extreme teams, large-scale agent teams operating in dynamic environments, are on the horizon. Such environments are problematic for current task allocation algorithms due to the lack of locality in agent interactions. We propose a novel distributed task allocation algorithm for extreme teams, called ..."
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Cited by 70 (20 self)
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Extreme teams, large-scale agent teams operating in dynamic environments, are on the horizon. Such environments are problematic for current task allocation algorithms due to the lack of locality in agent interactions. We propose a novel distributed task allocation algorithm for extreme teams, called LA-DCOP, that incorporates three key ideas. First, LA-DCOP's task allocation is based on a dynamically computed minimum capability threshold which uses approximate knowledge of overall task load. Second, LA-DCOP uses tokens to represent tasks and further minimize communication. Third, it creates potential tokens to deal with inter-task constraints of simultaneous execution. We show that LA-DCOP convincingly outperforms competing distributed task allocation algorithms while using orders of magnitude fewer messages, allowing a dramatic scale-up in extreme teams, upto a fully distributed, proxybased team of 200 agents. Varying threshold are seen as a key to outperforming competing distributed algorithms in the domain of simulated disaster rescue.
Scaling Teamwork to Very Large Teams
- IN PROCEEDINGS OF AAMAS’04
, 2004
"... As a paradigm for coordinating cooperative agents in dynamic environments, teamwork has been shown to be capable of leading to flexible and robust behavior. However, when we apply teamwork to the problem of building teams with hundreds of members, fundamental limitations become apparent. We have dev ..."
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Cited by 41 (12 self)
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As a paradigm for coordinating cooperative agents in dynamic environments, teamwork has been shown to be capable of leading to flexible and robust behavior. However, when we apply teamwork to the problem of building teams with hundreds of members, fundamental limitations become apparent. We have developed a model of teamwork that addresses the limitations of existing models as they apply to very large teams. A central idea of the model is to organize team members into dynamically evolving subteams. Additionally, we present a novel approach to sharing information, leveraging the properties of small worlds networks. The algorithm provides targeted, efficient information delivery. We have developed domain independant software proxies with which we demonstrate teams at least an order of magnitude bigger than previously published. Moreover, the same proxies proved effective for teamwork in two distinct domains, illustrating the generality of the approach.
Hybrid BDI-POMDP framework for multiagent teaming
- JAIR
, 2005
"... Many current large-scale multiagent team implementations can be characterized as following the “belief-desire-intention ” (BDI) paradigm, with explicit representation of team plans. Despite their promise, current BDI team approaches lack tools for quantitative performance analysis under uncertainty. ..."
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Cited by 37 (9 self)
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Many current large-scale multiagent team implementations can be characterized as following the “belief-desire-intention ” (BDI) paradigm, with explicit representation of team plans. Despite their promise, current BDI team approaches lack tools for quantitative performance analysis under uncertainty. Distributed partially observable Markov decision problems (POMDPs) are well suited for such analysis, but the complexity of finding optimal policies in such models is highly intractable. The key contribution of this article is a hybrid BDI-POMDP approach, where BDI team plans are exploited to improve POMDP tractability and POMDP analysis improves BDI team plan performance. Concretely, we focus on role allocation, a fundamental problem in BDI teams: which agents to allocate to the different roles in the team. The article provides three key contributions. First, we describe a role allocation technique that takes into account future uncertainties in the domain; prior work in multiagent role allocation has failed to address such uncertainties. To that end, we introduce RMTDP (Role-based Markov Team Decision Problem), a new distributed POMDP model for analysis of role allocations. Our
An integrated token-based algorithm for scalable coordination
- In AAMAS’05
, 2005
"... Efficient coordination among large numbers of heterogeneous agents promises to revolutionize the way in which some complex tasks, such as responding to urban disasters can be performed. However, state of the art coordination algorithms are not capable of achieving efficient and effective coordinatio ..."
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Cited by 36 (16 self)
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Efficient coordination among large numbers of heterogeneous agents promises to revolutionize the way in which some complex tasks, such as responding to urban disasters can be performed. However, state of the art coordination algorithms are not capable of achieving efficient and effective coordination when a team is very large. Building on recent successful token-based algorithms for task allocation and information sharing, we have developed an integrated and efficient approach to effective coordination of large scale teams. We use tokens to encapsulate anything that needs to be shared by the team, including information, tasks and resources. The tokens are efficiently routed through the team via the use of local decision theoretic models. Each token is used to improve the routing of other tokens leading to a dramatic performance improvement when the algorithms work together. We present results from an implementation of this approach which demonstrates its ability to coordinate large teams. 1.
The defacto system: Training tool for incident commanders
- In AAAI-05 (pp. 1555–1562). Pittsburgh. 123 Agent Multi-Agent Syst
, 2005
"... Techniques for augmenting the automation of routine coordination are rapidly reaching a level of effectiveness where they can simulate realistic coordination on the ground for large numbers of emergency response entities (e.g. fire engines, police cars) for the sake of training. Furthermore, it seem ..."
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Cited by 28 (12 self)
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Techniques for augmenting the automation of routine coordination are rapidly reaching a level of effectiveness where they can simulate realistic coordination on the ground for large numbers of emergency response entities (e.g. fire engines, police cars) for the sake of training. Furthermore, it seems inevitable that future disaster response systems will utilize such technology. We have constructed a new system, DE-FACTO (Demonstrating Effective Flexible Agent Coordination of Teams through Omnipresence), that integrates stateof-the-art agent reasoning capabilities and 3D visualization into a unique high fidelity system for training incident commanders. The DEFACTO system achieves this goal via three main components: (i) Omnipresent Viewer- intuitive interface, (ii) Proxy Framework- for team coordination, and (iii) Flexible Interaction- between the incident commander and the team. We have performed detailed preliminary experiments with DEFACTO in the fire-fighting domain. In addition, DEFACTO has been repeatedly demonstrated to key police and fire department personnel in Los Angeles area, with very positive feedback.
Human control for cooperating robot teams
- IN: PROC. HRI
, 2007
"... Human control of multiple robots has been characterized by the average demand of single robots on human attention or the distribution of demands from multiple robots. When robots are allowed to cooperate autonomously, however, demands on the operator should be reduced by the amount previously requir ..."
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Cited by 26 (8 self)
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Human control of multiple robots has been characterized by the average demand of single robots on human attention or the distribution of demands from multiple robots. When robots are allowed to cooperate autonomously, however, demands on the operator should be reduced by the amount previously required to coordinate their actions. The present experiment compares control of small robot teams in which cooperating robots explored autonomously, were controlled independently by an operator or through mixed initiative as a cooperating team. Mixed initiative teams found more victims and searched wider areas than either fully autonomous or manually controlled teams. Operators who switched attention between robots more frequently were found to perform better in both manual and mixed initiative conditions.
Modeling and Simulating Human Teamwork Behaviors Using Intelligent Agents
- In Journal of Physics of Life Reviews
, 2004
"... Among researchers in multi-agent systems there has been growing interest in using intelligent agents to model and simulate human teamwork behaviors. Teamwork modeling is important for training humans in gaining collaborative skills, for supporting humans in making critical decisions by proactively ..."
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Cited by 21 (1 self)
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Among researchers in multi-agent systems there has been growing interest in using intelligent agents to model and simulate human teamwork behaviors. Teamwork modeling is important for training humans in gaining collaborative skills, for supporting humans in making critical decisions by proactively gathering, fusing, and sharing information, and for building coherent teams with both humans and agents working effectively on intelligence-intensive problems. Teamwork modeling is also challenging because the research has spanned diverse disciplines from business management to cognitive science, human discourse, and distributed artificial intelligence. This article presents an extensive, but not exhaustive, list of work in the field, where the taxonomy is organized along two main dimensions: team social structure and social behaviors. Along the dimension of social structure, we consider agent-only teams and mixed human/agent teams. Along the dimension of social behaviors, we consider collaborative behaviors, communicative behaviors, helping behaviors, and the underpinning of effective teamwork--- shared mental models. The contribution of this article is that it presents an organizational framework for analyzing a variety of teamwork simulation systems and for further studying simulated teamwork behaviors.
The future of disaster response: Humans working with multiagent teams using DEFACTO, in: AAAI Spring Symp. AI Technologies for Homeland Security
, 2005
"... See next page for additional authors Follow this and additional works at: ..."
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Cited by 21 (6 self)
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See next page for additional authors Follow this and additional works at:
Using multiagent teams to improve the training of incident commanders
- In Industry Track of AAMAS
, 2006
"... The DEFACTO system is a multiagent based tool for training incident commanders for large scale disasters. In this paper, we highlight some of the lessons that we have learned from our interaction with the Los Angeles Fire Department (LAFD) and how they have affected the way that we continued the des ..."
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Cited by 19 (5 self)
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The DEFACTO system is a multiagent based tool for training incident commanders for large scale disasters. In this paper, we highlight some of the lessons that we have learned from our interaction with the Los Angeles Fire Department (LAFD) and how they have affected the way that we continued the design of our training system. These lessons were gleaned from LAFD feedback and initial training exercises and they include: system design, visualization, improving trainee situational awareness, adjusting training level of difficulty and situation scale. We have taken these lessons and used them to improve the DEFACTO system’s training capabilities. We have conducted initial training exercises to illustrate the utility of the system in terms of providing useful feedback to the trainee. 1.
