Results 1 - 10
of
13
Estimating information value in collaborative multi-agent planning systems
- In AAMAS’07
, 2007
"... This paper addresses the problem of identifying the value of infor-mation held by a teammate on a distributed, multi-agent team. It focuses on a distributed scheduling task in which computer agents support people who are carrying out complex tasks in a dynamic environment. The paper presents a decis ..."
Abstract
-
Cited by 13 (3 self)
- Add to MetaCart
(Show Context)
This paper addresses the problem of identifying the value of infor-mation held by a teammate on a distributed, multi-agent team. It focuses on a distributed scheduling task in which computer agents support people who are carrying out complex tasks in a dynamic environment. The paper presents a decision-theoretic algorithm for determining the value of information that is potentially relevant to schedule revisions, but is directly available only to the person and not the computer agent. The design of a “coordination autonomy” (CA) module within a coordination-manager system provided the empirical setting for this work. By design, the CA module depends on an external scheduler module to determine the specific effect of additional information on overall system performance. The paper describes two methods for reducing the number of queries the CA issues to the scheduler, enabling it to satisfy computational resource constraints placed on it. Experimental results indicate the algo-rithm improves system performance and establish the exceptional efficiency—measured in terms of the number of queries required for estimating the value of information—that can be achieved by the query-reducing methods.
Sharing Experiences to Learn User Characteristics in Dynamic Environments with Sparse Data
, 2007
"... This paper investigates the problem of estimating the value of probabilistic parameters needed for decision making in environments in which an agent, operating within a multi-agent system, has no a priori information about the structure of the distribution of parameter values. The agent must be able ..."
Abstract
-
Cited by 11 (3 self)
- Add to MetaCart
(Show Context)
This paper investigates the problem of estimating the value of probabilistic parameters needed for decision making in environments in which an agent, operating within a multi-agent system, has no a priori information about the structure of the distribution of parameter values. The agent must be able to produce estimations even when it may have made only a small number of direct observations, and thus it must be able to operate with sparse data. The paper describes a mechanism that enables the agent to significantly improve its estimation by augmenting its direct observations with those obtained by other agents with which it is coordinating. To avoid undesirable bias in relatively heterogeneous environments while effectively using relevant data to improve its estimations, the mechanism weighs the contributions of other agents’ observations based on a real-time estimation of the level of similarity between each of these agents and itself. The “coordination autonomy” module of a coordination-manager system provided an empirical setting for evaluation. Simulation-based evaluations demonstrated that the proposed mechanism outperforms estimations based exclusively on an agent’s own observations as well as estimations based on an unweighted aggregate of all other agents’ observations.
Collaborative health care plan support
- In Proceedings of the 12th international conference on Autonomous agents and multi-agent systems
, 2013
"... (Article begins on next page) The Harvard community has made this article openly available. Please share how this access benefits you. Your story matters. ..."
Abstract
-
Cited by 6 (3 self)
- Add to MetaCart
(Show Context)
(Article begins on next page) The Harvard community has made this article openly available. Please share how this access benefits you. Your story matters.
Resource-Driven Mission-Phasing Techniques for Constrained Agents in Stochastic Environments
"... Because an agent’s resources dictate what actions it can possibly take, it should plan which resources it holds over time carefully, considering its inherent limitations (such as power or payload restrictions), the competing needs of other agents for the same resources, and the stochastic nature of ..."
Abstract
-
Cited by 3 (0 self)
- Add to MetaCart
(Show Context)
Because an agent’s resources dictate what actions it can possibly take, it should plan which resources it holds over time carefully, considering its inherent limitations (such as power or payload restrictions), the competing needs of other agents for the same resources, and the stochastic nature of the environment. Such agents can, in general, achieve more of their objectives if they can use — and even create — opportunities to change which resources they hold at various times. Driven by resource constraints, the agents could break their overall missions into an optimal series of phases, optimally reconfiguring their resources at each phase, and optimally using their assigned resources in each phase, given their knowledge of the stochastic environment. In this paper, we formally define and analyze this constrained, sequential optimization problem in both the single-agent and multi-agent contexts. We present a family of mixed integer linear programming (MILP) formulations of this problem that can optimally create phases(whenphasesarenotpredefined)accountingforcostsandlimitationsinphasecreation. Because our formulations simultaneously also find the optimal allocations of resources at each phase and the optimal policies for using the allocated resources at each phase, they exploit structureacrossthesecoupledproblems. Thisallowsthemtofindsolutionssignificantlyfaster (orders of magnitude faster in larger problems) than alternative solution techniques, as we demonstrate empirically. 1.
Effective Information Value Calculation for Interruption Management in Multi-Agent Scheduling
, 2016
"... (Article begins on next page) The Harvard community has made this article openly available. Please share how this access benefits you. Your story matters. Citation Sarne, David, Barbara J. Grosz, and Peter Owotoki. 2008. Effectiveinformation value calculation for interruption management in multi-age ..."
Abstract
- Add to MetaCart
(Show Context)
(Article begins on next page) The Harvard community has made this article openly available. Please share how this access benefits you. Your story matters. Citation Sarne, David, Barbara J. Grosz, and Peter Owotoki. 2008. Effectiveinformation value calculation for interruption management in multi-agent scheduling. In Proceedings of the Eighteenth
Proceedings of the Eighteenth International Conference on Automated Planning and Scheduling (ICAPS 2008) Effective Information Value Calculation for Interruption Management in Multi-Agent Scheduling
"... This paper addresses the problem of deciding effectively whether to interrupt a teammate who may have information that is valuable for solving a collaborative scheduling problem. Two characteristics of multi-agent scheduling complicate the determination of the value of the teammate’s information, an ..."
Abstract
- Add to MetaCart
(Show Context)
This paper addresses the problem of deciding effectively whether to interrupt a teammate who may have information that is valuable for solving a collaborative scheduling problem. Two characteristics of multi-agent scheduling complicate the determination of the value of the teammate’s information, and hence whether it exceeds the costs of an interruption. First, in many scheduling contexts, task and scheduling knowledge reside in a scheduler module which is external to the agent, and the agent must query that module to estimate the value to the solution of knowing a specific piece of information. Second, the agent does not know the specific information its teammate has, resulting in the need for it to repeatedly query the scheduler. Choosing the right sequence of queries to the scheduler may enable the agent to make an interruption decision sooner, thus saving query time and computational load for both the agent and the external system. This paper defines two new sequencing heuristics which enhance the efficiency of the querying process. It also introduces three metrics for measuring the efficiency of a query sequence. It presents extensive simulation-based evidence that the new heuristics significantly outperform previously proposed methods for determining the value of information a teammate has.
Estimating Information Value in Collaborative Multi-Agent Planning Systems
"... This paper addresses the problem of identifying the value of information held by a teammate on a distributed, multi-agent team. It focuses on a distributed scheduling task in which computer agents support people who are carrying out complex tasks in a dynamic environment. The paper presents a decisi ..."
Abstract
- Add to MetaCart
(Show Context)
This paper addresses the problem of identifying the value of information held by a teammate on a distributed, multi-agent team. It focuses on a distributed scheduling task in which computer agents support people who are carrying out complex tasks in a dynamic environment. The paper presents a decision-theoretic algorithm for determining the value of information that is potentially relevant to schedule revisions, but is directly available only to the person and not the computer agent. The design of a “coordination autonomy” (CA) module within a coordination-manager system provided the empirical setting for this work. By design, the CA module depends on an external scheduler module to determine the specific effect of additional information on overall system performance. The paper describes two methods for reducing the number of queries the CA issues to the scheduler, enabling it to satisfy computational resource constraints placed on it. Experimental results indicate the algorithm improves system performance and establish the exceptional efficiency—measured in terms of the number of queries required for estimating the value of information—that can be achieved by the query-reducing methods.
te Brake et al. Self-organizing Mobile Teams Distributed Mobile Teams: Effects of Connectivity and Map Orientation on Teamwork
"... Fielded first responders are currently being equipped with support tools to improve their performance and safety. Novel information technology provides opportunities for improvement of task efficiency and situation awareness, but people can get in trouble when data networks fail. In this paper, we e ..."
Abstract
- Add to MetaCart
Fielded first responders are currently being equipped with support tools to improve their performance and safety. Novel information technology provides opportunities for improvement of task efficiency and situation awareness, but people can get in trouble when data networks fail. In this paper, we examine the effect of glitches in the data network on team performance and look into the strategies people use to cope with these disruptions. Teams of three responders collaborated in a search and rescue task, supported by a map showing their positions and the locations of victims. Data communication required for this support was interrupted, verbal communication remained possible. Two variants were used for the map: a north-up version and a heading-up version that was aligned with the orientation of the responder. Negative effects and changing strategies were found for the condition with interruptions, no differences were found for the two map variants.
Noname manuscript No. (will be inserted by the editor) Human-Agent Collaboration for Disaster Response
"... Abstract In the aftermath of major disasters, first responders are typically overwhelmed with large numbers of, spatially distributed, search and rescue tasks, each with their own requirements. Moreover, responders have to operate in highly uncertain and dynamic en-vironments where new tasks may app ..."
Abstract
- Add to MetaCart
(Show Context)
Abstract In the aftermath of major disasters, first responders are typically overwhelmed with large numbers of, spatially distributed, search and rescue tasks, each with their own requirements. Moreover, responders have to operate in highly uncertain and dynamic en-vironments where new tasks may appear and hazards may be spreading across the disaster space. Hence, rescue missions may need to be re-planned as new information comes in, tasks are completed, or new hazards are discovered. Finding an optimal allocation of resources to complete all the tasks is a major computational challenge. In this paper, we use decision theoretic techniques to solve the task allocation problem posed by emergency response plan-ning and then deploy our solution as part of an agent-based planning tool in real-world field trials. By so doing, we are able to study the interactional issues that arise when humans are guided by an agent. Specifically, we develop an algorithm, based on a Multi-Agent Markov Decision Process representation of the task allocation problem and show that it outperforms standard baseline solutions. We then integrate the algorithm into a planning agent that re-sponds to requests for tasks from participants in a mixed-reality location-based game, called AtomicOrchid, that simulates disaster response settings in the real-world. We then run a
Agile Planning for Real-World Disaster Response
"... We consider a setting where an agent-based planner instructs teams of human emergency responders to perform tasks in the real world. Due to uncertainty in the environment and the inability of the plan-ner to consider all human preferences and all at-tributes of the real-world, humans may reject plan ..."
Abstract
- Add to MetaCart
We consider a setting where an agent-based planner instructs teams of human emergency responders to perform tasks in the real world. Due to uncertainty in the environment and the inability of the plan-ner to consider all human preferences and all at-tributes of the real-world, humans may reject plans computed by the agent. A naı̈ve solution that re-plans given a rejection is inefficient and does not guarantee the new plan will be acceptable. Hence, we propose a new model re-planning problem us-ing a Multi-agent Markov Decision Process that integrates potential rejections as part of the plan-ning process and propose a novel algorithm to effi-ciently solve this new model. We empirically eval-uate our algorithm and show that it outperforms current benchmarks. Our algorithm is also shown to perform better in pilot studies with real humans. 1
