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Fig. 5. PEIS Connectivity Graph Visualization Tool6 Conclusions and future workThis paper has examined pre-existing middleware employed for the control ofrobotic ecologies and illustrated their use in conjuction with the Self -OSGi agentframework. The resulting framework provides re-usable, lightweight, modularend extensible mechanisms for the specification and the development of decentralizedcoordination mechanisms for robotic ecologies.While the adoption of a multi-agent approach is often adopted for robot systemdesign, a key and original result of our approach is that both system’s distributionand adaptation become hortogonal concerns freeing the developers totackle application requirements. Once application components are implementedand their semantic and inter-dependencies described with Self -OSGi XML files,they can be freely distributed over the network, as the distributed Self -OSGidescribed in this paper will automatically manage their instantiation and configurationto achieve application’s objectives.Future work will test the framework with larger scale problems and also seekto adapt agent/planning integration and agent learning techniques to tackle someof the main limitations of our architecture, such as its lack of look-ahead and itsreliance on hard-coded pre-conditions of component plans.83
ACKNOWLEDGMENTThis work has been supported by the EU FP7 RUBICON project (contract n.269914).References1. G. Amato, e.a.: Robotic ubiquitous cognitive network. In: 3rd International Symposiumon Ambient Intelligence, Salamanca, Spain (2012)2. : Robotic ubiquitous cognitive network. Web site http:/fp7rubicon.eu.3. Dragone, M.: Component & service-based agent systems: Self -osgi. In: Proc.of 4th International Conference on Agents and Artificial Intelligence - ICAART.,Albouvera, Portugal (2012)4. Dragone, M.: A bdi model for component & service-based systems: Self -osgi. In:10th International Conference on Practical Applications of Agents and Multi-AgentSystems., Salamanca, Spain (2012)5. : Open service gateway initiative. Web site http://www.osgi.org/Main/HomePage.6. : The PEIS ecology project. Official web site www.aass.oru.se/˜peis/.7. M. Broxvall, B.S. Seo, W.K.: The peis kernel: A middleware for ubiquitous robotics.In: Proc. of the IROS-07 Workshop on Ubiquitous Robotic Space Design and Applications,San Diego, California (2007)8. Kaminsky, A.: Infrastructure for distributed applications in ad hoc networks ofsmall mobile wireless devices. Tech. rep., Rochester Institute of Technology, ITLab (2001)9. M. Hellenschmidt, T.K.: Self-organization for multi-component multi-media environments.Proc of the UniComp Workshop on Ubiquitous Display Environments(2004)10. J. Rao, X.S.: A survey of automated web service composition methods. Procof the Int Workshop on Semantic Web Services and Web Process Composition(SWSWPC), San Diego, CA (2004)11. : T. j. community, the project jxta web site. Web site www.jxta.org.12. G. Tesauro, e.a.: A multi-agent systems approach to autonomic computing. Proc.of the Int Conf on Autonomous Agents and Multiagent Systems, pp. 464471 (2004)13. R. Lundh, L. Karlsson, A.S.: Plan-based configuration of an ecology of robots. In:ICRA, Rome, Italy (2007)14. Lundh, R.: Plan-based configuration of a group of robots. Licentiate Thesis.University of Örebro, Sweden (October 2006)15. M. Gritti, M. Broxvall, A.S.: Reactive self-configuration of an ecology of robots.Proc. of the ICRA-07 Workshop on Network Robot Systems. Roma, Italy, April(2007)16. D. Kinny, M. G., A.R.: A methodology and modeling technique for systems of bdiagents. In Proc. Of 7th European Workshop on Modelling Autonomous Agents inMulti-Agent Worlds, LNAI1038. Springer-Verlag (1996)17. A. Pokahr, L. Braubach, W.L.: A goal deliberation strategy for bdi agent systems.MATES, Springer-Verlag (2005)18. R. Ross, R. Collier, G.O.: Af-apl: Bridging principles & practices in agent orientedlanguages. Programming Multi-Agent Systems, Lecture Notes in ArtificialIntelligence (LNAI), Volume 3346, Springer-Verlag (2004)84
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Proceedings of the Tenth Internatio
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OrganisationOrganising CommitteeMeh
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Table of ContentseJason: an impleme
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in Sect. 3 the translation of the J
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init_count(0).max_count(2000).(a)(b
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For instance, a failure in the ERES
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{plan, fun start_count_trigger/1,fu
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single parameter, an Erlang record
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1. Belief annotations. Even though
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decisions taken during the design a
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Conceptual Integration of Agents wi
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Fig. 2. Active component structurep
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the service provider component. As
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Fig. 4. Web Service Invocationretri
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01: public interface IBankingServic
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Page 56 and 57: An Agent-Based Cognitive Robot Arch
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Page 131 and 132: 6 ConclusionWe presented an abstrac
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1 // agent ag02 iterations (" zero
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3.1 simpAL OverviewThe main inspira
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3.2 Typing Agents with Tasks and Ro
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Defining Agent Scripts in simpAL (F
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that sends a message to the receive
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* error: wrong type for the param v
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Given an organization model, it is
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Learning to Improve Agent Behaviour
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2.1 Agent Programming LanguagesAgen
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choosing actions is to find a good
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1 init module {2 knowledge{3 block(
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of a module. For example, to change
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if bel(on(X,Y), clear(X)), a-goal(c
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mance. Figure 2d shows the same A f
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the current percepts of the agent.
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Author IndexAbdel-Naby, S., 69Alelc
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