Engineering Multi-Agent Systems

Name: Engineering Multi-Agent Systems | 4th International Workshop, EMAS 2016, Singapore, Singapore, May 9-10, 2016, Revised, Selected, and Invited Papers
Brand: Springer
Price: 53.49 EUR
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4th International Workshop, EMAS 2016, Singapore, Singapore, May 9-10, 2016, Revised, Selected, and Invited Papers

Matteo Baldoni Jörg P. Müller Ingrid Nunes Rym Zalila-Wenkstern(Herausgeber*in)

Springer (Verlag)

Erschienen am 15. Dezember 2016

XII, 227 Seiten

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978-3-319-50983-9 (ISBN)

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Beschreibung

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Inhalt

Intro
Preface
Organization
Contents
nDrites: Enabling Laboratory Resource Multi-agent Systems
1 Introduction
2 Previous Work
3 The Laboratory Resource Multi-agent System (LR-MAS) Framework
3.1 The Laboratory Resources Multi Agent System Model
3.2 nDrites
4 The Laboratory Resource Multi Agent System Implementation
4.1 Communications
4.2 Sending Messages to nDrites
4.3 Sending Messages to Agents
4.4 Definition of LR-MAS Agents
5 The Analytical Monitoring Case Study
6 The Instrument Failure Prediction Case Study
7 Conclusions
References
Towards Data- and Norm-Aware Multiagent Systems
1 Introduction
2 Meyer's Forces: Processor, Action and Object
3 From Functional Decomposition to MAS
4 Need of Data and Norm Awareness
5 Steps Towards Data and Norm Awareness
6 Final Remarks
References
Monitoring Patients with Hypoglycemia Using Self-adaptive Protocol-Driven Agents: A Case Study
1 Introduction and Motivation
2 Related Work
3 Self-adaptive Protocol-Driven Agents
4 Modeling Medical Protocols for Hypoglycemia in the Newborns
5 Conclusions and Future Work
References
Using Automatic Failure Detection for Cognitive Agents in Eclipse (AAMAS 2016 DEMONSTRATION)
1 Introduction
2 Automated Testing of Cognitive Agents
2.1 Modules as Basic Unit for Testing
2.2 Test Language
2.3 Test Templates
2.4 Test Approach
2.5 Debugging, Testing, and Fault Localisation
3 Debugging a Multi-agent System
3.1 Stepping an Agent
3.2 User-Defined Breakpoints
3.3 User-Selectable Breakpoints
3.4 State Inspection
3.5 Interactive Console
3.6 Watch Expressions
3.7 Logging
4 Testing GOAL Agents in the Eclipse IDE
5 Evaluation
6 Conclusion
References
PriGuardTool: A Web-Based Tool to Detect Privacy Violations Semantically
1 Introduction
2 Background: PriGuard Approach
3 PriGuardTool
4 Implementation
5 Evaluation
5.1 Experiments with Facebook Users
5.2 Variations on Example Scenarios
6 Discussion
6.1 Related Work
6.2 Limitations and Future Developments
References
Application Framework with Abstractions for Protocol and Agent Role
Abstract
1 Introduction
2 Agent Model
3 Framework Overview
4 Application Framework: Agent System
5 Application Framework: Utility System
6 Test System
7 Evaluation
8 Discussion
9 Related Work
10 Summary
Acknowledgments
References
A Namespace Approach for Modularity in BDI Programming Languages
1 Introduction
2 Related Work
3 Modules and Namespaces
3.1 Syntax
3.2 Loading Modules
4 Implementation
5 Example
6 Evaluation
7 Discussion
7.1 Module Relationships
8 Conclusion
References
ARGO: An Extended Jason Architecture that Facilitates Embedded Robotic Agents Programming
1 Introduction
2 Programming BDI Agents
2.1 Jason
2.2 Javino
2.3 Embedding Robotic Agents
3 Perception Filters
3.1 Filtering Perceptions
3.2 Perception Filters in Jason
4 ARGO
4.1 Overview of a Robot's Architecture Using Javino
4.2 ARGO Architecture
4.3 Internal Actions
4.4 Customizing Jason for ARGO
5 Experiments
5.1 Case Study
5.2 Experiment Design
5.3 Implementation
5.4 Results
6 Related Work
7 Conclusions and Further Work
References
A Multi-agent Solution for the Deployment of Distributed Applications in Ambient Systems
1 Deployment of Smart Applications
2 Related Work
3 Scenario
4 Multi-agent Architecture
4.1 Agents and Artifacts
4.2 Sharing Policies
4.3 Deployment
4.4 Scenario Illustration
4.5 Summary
5 Agents' Behaviour
5.1 User Agent
5.2 Application Agent
5.3 Infrastructure Agent
6 Implementation and Experimentations
7 Conclusion and Future Work
References
Reasoning About the Executability of Goal-Plan Trees
1 Introduction
2 Related Work
3 Goal-Plan Trees
3.1 Actions
3.2 Plans
3.3 Goals
4 Execution Conditions
4.1 Actions
4.2 Plans
4.3 Goals
4.4 Sets of Execution Paths
5 Conclusion and Future Work
References
Augmenting Agent Computational Environments with Quantitative Reasoning Modules and Customizable Bridge Rules
1 Introduction
2 Specification of the Case Study
3 Resource-Based Reasoning
4 Enhancing the ACE Framework
5 Case Study: Bridge Rules Customization and Application
6 Semantics
7 Case Study: RASP Implementation
8 Concluding Remarks
References
How Testable are BDI Agents? An Analysis of Branch Coverage
1 Introduction
2 Belief-Desire-Intention (BDI) Agents
3 All-Edge Coverage Analysis
3.1 Removing Failure Handling
3.2 Simplifying for Uniform Programs
4 All-edges vs. All-paths
5 BDI vs. Procedural
6 Conclusion
References
Author Index

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