Interleaving planning and execution in a multiagent team planning environment, (2000)

by M Paolucci, O Shehory, K P Sycara
Venue:Electron. Trans. on Artif. Intell.
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Automating DAML-S Web Services Composition using SHOP2

by Dan Wu, Bijan Parsia, Evren Sirin, James Hendler, Dana Nau - In Proc. of ISWC2003 , 2003
"... Abstract. The DAML-S Process Model is designed to support the ap-plication of AI planning techniques to the automated composition of Web services. SHOP2 is an Hierarchical Task Network (HTN) planner well-suited for working with the Process Model. We have proven the cor-respondence between the semant ..."
Abstract - Cited by 155 (6 self) - Add to MetaCart
Abstract. The DAML-S Process Model is designed to support the ap-plication of AI planning techniques to the automated composition of Web services. SHOP2 is an Hierarchical Task Network (HTN) planner well-suited for working with the Process Model. We have proven the cor-respondence between the semantics of SHOP2 and the situation calculus semantics of the Process Model. We have also implemented a system which soundly and completely plans over sets of DAML-S descriptions using a SHOP2 planner, and then executes the resulting plans over the Web. We discuss the challenges and difficulties of using SHOP2 in the information-rich and human-oriented context of Web services. 1

Scaling Teamwork to Very Large Teams

by Paul Scerri , Yang Xu, Elizabeth Liao, Justin Lai, Katia Sycara - 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 ..."
Abstract - Cited by 41 (12 self) - Add to MetaCart
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.
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Team-Oriented Agent Coordination in the RETSINA Multi-Agent System

by Joseph A. Giampapa, Katia P. Sycara - Robotics Institute, Carnegie Mellon University , 2002
"... individual has the collective expertise, information, or resources required for the eective completion or performance of a task. This paper describes a prototype, implemented in the RETSINA multi-agent infrastructure, in which agents interact with each other via capability-based and team-oriented co ..."
Abstract - Cited by 32 (4 self) - Add to MetaCart
individual has the collective expertise, information, or resources required for the eective completion or performance of a task. This paper describes a prototype, implemented in the RETSINA multi-agent infrastructure, in which agents interact with each other via capability-based and team-oriented coordination. We propose a model of team-oriented agent coordination that is based on the joint intentions theory, so that agents can communicate their intended commitments to each other. Team-oriented agents communicate partial descriptions of the context in which a mission must be executed and the resources to do so via data structures that are analogous to the SharedPlans recipe. The agents then proceed, in a process reminiscent of SharedPlans partial plan re- nement, to re ne and revise their understanding of the mission context, via both team-oriented and capability-based coordination with other RETSINA agents, while executing their mission. The partial plan re nement behavior is made possible through the RETSINA Agent Architecture, which interleaves HTN planning and process execution. We enhance the above models of teamwork by adding our own characterizations of checkpoints, role and subgoal relations in software agent teamwork, and show how the software agents can acquire this information from their operating environment during plan execution time. Such enhancements create a scalable team-oriented multi-agent system architecture, in which team coordination strategies can be implemented in a general and domain-independent way.
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IMPACTing SHOP: Putting an AI Planner into a Multi-Agent Environment

by Jürgen Dix, Héctor Muñoz-avila, Dana S. Nau, Lingling Zhang - Annals of Mathematics and AI , 2003
"... In this paper we describe a formalism for integrating the SHOP HTN planning sys-tem with the IMPACT multi-agent environment. We define the A-SHOP algorithm, an agentized adaptation of the SHOP planning algorithm that takes advantage of IMPACT’s capabilities for interacting with external agents, perf ..."
Abstract - Cited by 21 (4 self) - Add to MetaCart
In this paper we describe a formalism for integrating the SHOP HTN planning sys-tem with the IMPACT multi-agent environment. We define the A-SHOP algorithm, an agentized adaptation of the SHOP planning algorithm that takes advantage of IMPACT’s capabilities for interacting with external agents, performing mixed sym-bolic/numeric computations, and making queries to distributed, heterogeneous in-formation sources (such as arbitrary legacy and/or specialized data structures or external databases). We show that A-SHOP is both sound and complete if certain conditions are met.

Integrating Intelligent Agents into Human Teams

by Katia Sycara, Michael Lewis , 2003
"... As the role of teams becomes more important in organizations, developing and maintaining high performance teams has been the goal of several researchers (Decker, Sycara, & Williamson 1997; Beyerlein, Johnson, & Beyerlein, 2001, Cannon-Bowers & Salas, 1998; McNeese, Salas & Endsley, ..."
Abstract - Cited by 21 (1 self) - Add to MetaCart
As the role of teams becomes more important in organizations, developing and maintaining high performance teams has been the goal of several researchers (Decker, Sycara, & Williamson 1997; Beyerlein, Johnson, & Beyerlein, 2001, Cannon-Bowers & Salas, 1998; McNeese, Salas & Endsley, 2001; Salas, Bowers, & Edens, 2001) . One major question is how to turn a team of experts into an expert team. Several strategies such as task-related cross training (Salas, Cannon-Bowers, & Johnson, 1997; CannonBowers and Salas, 1998) have emerged. In addition to traditional behaviorally-based methods for facilitating team development, advances in computer science and robotics are now allowing the introduction of artificial intelligence (sic; intelligent agents) into teamwork in a variety of roles and functions. This advent of agent technologies raises two questions: 1) what kinds of assistance can be provided and 2) what kinds of assistance prove beneficial. The reported research attempts to

The RETSINA MAS, a Case Study

by Katia Sycara , Joseph A. Giampapa, Brent Langley, Massimo Paolucci - IN SELMAS , 2003
"... In this paper we identify challenges that confront the largescale multi-agent system (LMAS) designer, and claim that these challenges can be successfully addressed by agent-based software engineering (ABSE), which we consider to be distinct from object-oriented software engineering for multi-age ..."
Abstract - Cited by 17 (4 self) - Add to MetaCart
In this paper we identify challenges that confront the largescale multi-agent system (LMAS) designer, and claim that these challenges can be successfully addressed by agent-based software engineering (ABSE), which we consider to be distinct from object-oriented software engineering for multi-agent systems (OOSE for MAS) in its consideration of agent goal, role, context and attitude as first class objects. We show how we have discovered these principles through our experiences in developing the RETSINA multi-agent system, in implementing specific test applications, and in the derivation of three distinct architectures that help guide and describe the designs of our systems: the individual agent architecture, the functional architecture, and the infrastructure architecture.
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Interleaving Web Services Composition and Execution Using Software Agents and Delegation

by Zakaria Maamar, Quan Z. Sheng, Boualem Benatallah - In Proc. Workshop on Web Services and Agent-Based Engineering , 2003
"... The paper describes a low cost software technique for transient fault detection and fault tolerance in a processing system. It is very cost effective tool for the application design engineers. ..."
Abstract - Cited by 13 (0 self) - Add to MetaCart
The paper describes a low cost software technique for transient fault detection and fault tolerance in a processing system. It is very cost effective tool for the application design engineers.

Techniques and Directions for Building Very Large Agent Teams

by Paul Scerri, et al. , 2005
"... We have developed probabilistic algorithms that leverage the associates network for distributed plan instantiation, role allocation, information sharing and adjustable autonomy with a team. By developing such new algorithms, we have been able to build teams of hundreds of cooperating agents, and tes ..."
Abstract - Cited by 11 (0 self) - Add to MetaCart
We have developed probabilistic algorithms that leverage the associates network for distributed plan instantiation, role allocation, information sharing and adjustable autonomy with a team. By developing such new algorithms, we have been able to build teams of hundreds of cooperating agents, and test specific behaviors among tens of thousands of agents. In this paper, we describe the algorithms that we have developed, the tests that we subjected them to, and sketch some of the key challenges that remain to be addressed.
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Balancing Deliberation and Reaction, Planning and Execution for Space Robotic Applications

by Russell Knight, Forest Fisher, Tara Estlin, Barbara Engelhardt, Steve Chien - Proc. 2001 IEEE Intl Conf on Intelligent Robotics and Systems, Maui
"... Intelligent behavior for robotic agents requires a careful balance of fast reactions and deliberate consideration of long-term ramifications. The need for this balance is particularly acute in space applications, where hostile environments demand fast reactions, and remote locations dictate careful ..."
Abstract - Cited by 9 (0 self) - Add to MetaCart
Intelligent behavior for robotic agents requires a careful balance of fast reactions and deliberate consideration of long-term ramifications. The need for this balance is particularly acute in space applications, where hostile environments demand fast reactions, and remote locations dictate careful management of consumables that cannot be replenished. However, fast reactions typically require procedural representations with limited scope and handling long-term considerations in a general fashion is often computationally expensive. In this paper, we describe three major areas for autonomous systems for space exploration: free-flying spacecraft, planetary rovers, and ground communications stations. In each of these broad applications areas, we identify operational considerations requiring rapid response and considerations of long-term ramifications. We describe these issues in the context of ongoing efforts to deploy autonomous systems using planning and task execution systems. 1

A Multiagent Approach for Electronic Travel Planning

by David Camacho, José Manuel Molina, Daniel Borrajo - In Proceedings of the Second International Bi-Conference Workshop on Agent-Oriented Information Systems (AOIS-2000 , 2000
"... In the last years, the amount of information stored in Internet has grown exponentially. This article presents a new approach to cooperative problem solving that use the Web as a source of data. The architecture has been designed using two main Artificial Intelligence techniques: Multiagent System d ..."
Abstract - Cited by 8 (5 self) - Add to MetaCart
In the last years, the amount of information stored in Internet has grown exponentially. This article presents a new approach to cooperative problem solving that use the Web as a source of data. The architecture has been designed using two main Artificial Intelligence techniques: Multiagent System design, and problem solving (planning). Both are used to obtain a new architecture that dynamically obtains knowledge from Internet. The system uses two different types of agents: planning agents and web agents. Planning agents pay attention to the user's queries and solve his/her problems at a high level of abstraction; web agents fill in the details obtaining the required information from Internet. Different partial solutions given by the web agents while combined by the planning agent to obtain a detailed solution (or solutions) to the user queries.