Logical reorganization of DAI systems
ECAI-94 Proceedings of the workshop on agent theories, architectures, and languages on Intelligent agents
Organization Self-Design of Distributed Production Systems
IEEE Transactions on Knowledge and Data Engineering
Cloning for Intelligent Adaptive Information Agents
Revised Papers from the Second Australian Workshop on Distributed Artificial Intelligence: Multi-Agent Systems: Methodologies and Applications
A Market Protocol for Decentralized Task Allocation
ICMAS '98 Proceedings of the 3rd International Conference on Multi Agent Systems
An Adaptive Organizational Policy for Multi Agent Systems - AASMAN
ICMAS '98 Proceedings of the 3rd International Conference on Multi Agent Systems
On Resource-Oriented Multi-Commodity Market Computations
ICMAS '98 Proceedings of the 3rd International Conference on Multi Agent Systems
A framework for organizational self-design in distributed problem solving networks
A framework for organizational self-design in distributed problem solving networks
Mobile software agents: an overview
IEEE Communications Magazine
Multi-agent dependence by dependence graphs
Proceedings of the first international joint conference on Autonomous agents and multiagent systems: part 1
Co-evolutionary Auction Mechanism Design: A Preliminary Report
AAMAS '02 Revised Papers from the Workshop on Agent Mediated Electronic Commerce on Agent-Mediated Electronic Commerce IV, Designing Mechanisms and Systems
A key-based coordination algorithm for dynamic readiness and repair service coordination
AAMAS '03 Proceedings of the second international joint conference on Autonomous agents and multiagent systems
A survey of multi-agent organizational paradigms
The Knowledge Engineering Review
Hybrid negotiation for resource coordination in multiagent systems
Web Intelligence and Agent Systems
Analyzing the tradeoffs between breakup and cloning in the context of organizational self-design
Proceedings of The 8th International Conference on Autonomous Agents and Multiagent Systems - Volume 2
Code and Data Propagation on a PC's Multi-Agent System
Proceedings of the 2008 conference on New Trends in Multimedia and Network Information Systems
Hybrid BDI-POMDP framework for multiagent teaming
Journal of Artificial Intelligence Research
Coordinating teams in uncertain environments: a hybrid BDI-POMDP approach
ProMAS'04 Proceedings of the Second international conference on Programming Multi-Agent Systems
A classification framework of adaptation in multi-agent systems
CIA'06 Proceedings of the 10th international conference on Cooperative Information Agents
Iterative query-based approach to efficient task decomposition and resource allocation
CIA'06 Proceedings of the 10th international conference on Cooperative Information Agents
Formation of virtual organizations through negotiation
MATES'06 Proceedings of the 4th German conference on Multiagent System Technologies
Hi-index | 0.01 |
In this paper, we present an adaptive organizational policy for multi-agent systems called \acro{trace}. \acro{trace} allows a collection of multi-agent organizations to dynamically allocate tasks and resources between themselves in order to efficiently process an incoming stream of task requests. \acro{trace} is intended to cope with environments in which tasks have time constraints, and environments that are subject to load variations. \acro{trace} is made up of two key elements: the task allocation protocol (\acro{tap}) and the resource allocation protocol (\acro{rap}). The \acro{tap} allows agents to cooperatively allocate their tasks to other agents with the capability and opportunity to successfully carry them out. As requests arrive arbitrarily, at any instant, some organizations could have surplus resources while others could become overloaded. In order to minimize the number of lost requests caused by an overload, the allocation of resources to organizations is changed dynamically by the resource allocation protocol (\acro{rap}), which uses ideas from computational market systems to allocate resources (in the form of problem solving agents) to organizations. We begin by formally defining the task allocation problem, and show that it is \acro{NP}-complete, and hence that centralized solutions to the problem are unlikely to be feasible. We then introduce the task and resource allocation protocols, focussing on the way in which resources are allocated by the \acro{rap}. We then present some experimental results, which show that \acro{trace} exhibits high performance despite unanticipated changes in the environment.