Multi-Agent Adaptive Mechanism Leading to Optimal Real-Time Load Sharing

We propose a new real-time load sharing policy (LSP), which optimally dispatches the incoming workload according to the current availability of the operators. Optimality means here that the global service permanently requires the engagement of a minimum number of operators while still respecting due dates. To cope with inherent randomness due to operator failures as well as non-stationary fluctuating incoming workload, any optimal LSP rule will necessarily rely on real-time updating mechanisms. Accordingly, a permanent monitoring of the traffic workload, of the queue contents and of other relevant dynamic state variables is often realized by a central workload dispatcher. In this contribution, we abandon such a "classical" approach and we propose a fully decentralized algorithm which fulfils the optimal load sharing process. The underlying decentralized decisions rely on a "smart tasks" paradigm in which each incoming task is endowed with an autonomous routing decision mechanism. Incoming jobs hence possess, in this paper, the status of autonomous agents endowed with "local intelligence". Stigmergic interactions between these agents cause the optimal LSP to emerge. We emphasize that beside a manifest strict relevance for applications, our class of models is analytically tractable, a rather uncommon feature when dealing with multi-agent dynamics and complex adaptive logistics systems

Debugging multi-agent systems with design documents

Debugging multi-agent systems, which are concurrent, distributed, and consist of complex components is difficult, yet crucial. The development of these complex systems is supported by agent-oriented software engineering methodologies which utilise agents as the central design metaphor. The systems that are developed are inherently complex since the components of these systems may interact in flexible and sophisticated ways and traditional debugging techniques are not appropriate. Despite this, very little effort has been applied to developing appropriate debugging tools and techniques. Debugging multi-agent systems without good debugging tools is highly impractical and without suitable debugging support developing and maintaining multi-agent systems will be more difficult than it need be. In this thesis we propose that the debugging process can be supported by following an agent-oriented design methodology, and then using the developed design artifacts in the debugging phase. We propose a domain independent debugging framework which comprises the developed processes and components that are necessary in using design artifacts as debugging artifacts. Our approach is to take a non-formal design artifact, such as an AUML protocol design, and encode it in a machine interpretable manner such that the design can be used as a model of correct system behaviour. These models are used by a run-time debugging system to compare observed behaviour against specified behaviour. We provide details for transforming two design artifact types into equivalent debugging artifacts and show how these can be used to detect bugs. During a debugging episode in which a bug has been identified our debugging approach can provide detailed information about the possible reason for the bug occurring. To determine if this information was useful in helping to debug programs we undertook a thorough empirical study and identified that use of the debugging tool translated to an improvement in debugging performance. We conclude that the debugging techniques developed in this thesis provide effective debugging support for multi-agent systems and by having an extensible framework new design artifacts can be explored and as translations are developed they can be added to the debugging system

Handling Emergent Resource Use Oscillations

Abstract — Distributed computing systems are increasingly being created as self-organizing collections of many autonomous (human or software) agents cooperating as peers. Peer-to-peer coordination introduces, however, unique and potentially serious challenges. When there is no one ‘in charge’, dysfunctions can emerge as the collective effect of locally reasonable decisions. In this paper, we consider the dysfunction wherein inefficient resource use oscillations occur due to delayed status information, and describe novel approaches, based on the selective use of misinformation, for dealing with this problem. A model of several servers offering equivalent service to independent clients is presented and studied numerically and analytically; the spreading of misinformation about the queue status is found to dampen oscillations and improve system performance for a wide range of parameters. 1 Index —emergent terms dysfunctions, resource oscillations, selective misinformation I

Handling Emergent Resource Use Oscillations

Abstract—Distributed computing systems are increasingly being created as self-organizing collections of many autonomous (human or software) agents cooperating as peers. Peer-to-peer coordination introduces, however, unique and potentially serious challenges. When there is no one “in charge, ” dysfunctions can emerge as the collective effect of locally reasonable decisions. In this paper, we consider the dysfunction wherein inefficient resource use oscillations occur due to delayed status information, and describe novel approaches, based on the selective use of misinformation, for dealing with this problem. A model of several servers offering equivalent service to independent clients is presented and studied numerically and analytically; the spreading of misinformation about the queue status is found to dampen oscillations and improve system performance for a wide range of parameters. Index Terms—Emergent dysfunctions, resource oscillations, selective misinformation. I