Leveraging Organizational Guidance Policies with Learning to Self-Tune Multiagent Systems

Harmon, Scott J.; DeLoach, Scott A.; Robby, .; Caragea, Doina

doi:10.1109/saso.2008.46

Cited by 7 publications

(7 citation statements)

References 21 publications

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“…Figure 6c (note line on the x-axis) shows that without the generated policies, our system produced much lower quality products than with the generated policies. We have also run similar experiments with other multiagent system design models such as those described in [7] and obtained similar results.…”

Section: Discussionmentioning

confidence: 55%

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Abstract Requirement Analysis in Multiagent System Design

Harmon

DeLoach

Robby

2009

2009 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology

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Abstract-"Good, fast, or cheap, pick two." What drives designers to make decisions on how to architect a system? The stake-holder has certain abstract qualities in mind: efficiency, quality, reliability, and so forth. How do we make sure our system is guided by these qualities? What happens when the system cannot always provide all the qualities? We describe a framework for analyzing a design allowing decisions about what qualities are more important to be made at design-time.

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Section: Discussionmentioning

confidence: 55%

“…Some work has been done on model checking multiagent systems [10], [13]. Automated policy generation has been used for online learning [7]. These methods help the multiagent system better tune to the environment in which it is deployed.…”

Section: Introductionmentioning

confidence: 99%

Abstract Requirement Analysis in Multiagent System Design

Harmon

DeLoach

Robby

2009

2009 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology

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“…To demonstrate the application of the P-graph framework for assessing the design of OMACS-based multiagent systems, a survey is given of a simplified Cooperative Robotic Search Team 5 (CRST) system [20], [33]. Essentially, we are to design a team of robots whose goal is to search for different areas of a given location on a map.…”

Section: Motivational Example: Cooperative Robotic Search Team Systemmentioning

confidence: 99%

“…Unlike any of the available algorithmic methods for computing the quality of a proposed set of assignments based upon OMACS, i.e., agents, a i  A OMACS , assigned to play roles, r k  R OMACS , in order to achieve goals, g j  G OMACS , where no mathematical programming model is derived due to the approach adopted, i.e., step-by-step computation [4], [20], [30], [33], [37], [38]; we propose a simple mathematical programming model, which is derived from the maximal structure, generated by algorithm MSG, and does not impair the optimality of the resultant solution.…”

Section: Mathematical Programming Modelmentioning

confidence: 99%

A Preliminary Study of the Application of the P-graph Methodology for Organization-based Multiagent System Designs: Assessment

2015

APH

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At the outset, the design of an organization-based multiagent system is modeled according to the framework of Organization Model for Adaptive Complex Systems (OMACS). Subsequently, this design model is transformed into a process-network model.Eventually, the resultant process-network model in conjunction with the P-graph-based methodology give rise to: (i) the maximal structure of the process network, comprising all the feasible combinations of structures, i.e., OMACS-based design configurations, capable of yielding the specified products from the specified raw material; (ii) every feasible structure for the process of interest; and (iii) the optimal structure of the network, i.e., the optimal OMACS-based design configuration. Finally, in light of the tenet of a modelingtransformation-evaluation paradigm, an appraisal is made of the feasibility as well as the flexibility and cost of the optimal OMACS-based design configuration obtained.

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“…Policies have been used in multi-agent system engineering for some time and several languages, frameworks, enforcement and checking mechanisms have been developed (Bradshaw et. al, 2003;Shoham and Tennenholtz, 1995;Harmon et al, 2007Harmon et al, , 2008. In general, policies are used to restrict agent behaviour and may be enforced at design time or at runtime.…”

Section: Model Policiesmentioning

confidence: 99%

O-MaSE: a customisable approach to designing and building complex, adaptive multi-agent systems

DeLoach

García-Ojeda

2010

IJAOSE

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The complexity and scope of software systems continues to grow. One approach to dealing with this growing complexity is the use of intelligent, multi-agent systems. However, due in part to its relative infancy when compared to other software paradigms, the use of multi-agent systems has yet to be used extensively in industry. One reason is the lack of industrial strength methods and tools to support multi-agent development. This paper presents the organisation-based multi-agent software engineering (O-MaSE) methodology framework, which integrates a set of concrete technologies aimed at facilitating industrial acceptance. Specifically, O-MaSE is a customisable agent-oriented methodology based on consistent, well-defined concepts supported by plug-ins to an industrial strength development environment, agentTool III.

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Leveraging Organizational Guidance Policies with Learning to Self-Tune Multiagent Systems

Abstract: Abstract

Cited by 7 publications

References 21 publications

Abstract Requirement Analysis in Multiagent System Design

Abstract Requirement Analysis in Multiagent System Design

A Preliminary Study of the Application of the P-graph Methodology for Organization-based Multiagent System Designs: Assessment

O-MaSE: a customisable approach to designing and building complex, adaptive multi-agent systems

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