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5. Brooks, R. A. (1991) Intelligence Without Representation, Artificial Intelligence47:139-159.6. Buford, J. Jakobson, G. Lewis, L. (2006) Extending BDI Multi-Agent Systems withSituation Management. The Ninth International Conference on Information Fusion.Florence, Italy, 2006.7. Cap, M. Dastani, M. and Harbers, M. (2011) Belief/Goal Sharing Modules for BDILanguages, Proceedings of CSSE 2011, pp. 87-94.8. Chatila, R. (1995) Deliberation and reactivity in autonomous mobile robots, Volume75 Issue 2, June 1995 robots. Robotics and Autonomous Systems 16 (1995) 197-2119. Dastani, M. (2008) 2APL: a practical agent programming language, AutonomousAgents and Multi-Agent Systems, Volume 16 , Issue 3, Pages: 214 - 248, ISSN:1387-253210. Degroote, A. and Lacroix, S. (2011) ROAR: Resource oriented agent architecturefor the autonomy of robots, IEEE International Conference on Robotics and Automation(ICRA), Pages 6090 - 6095.11. Fisher, F. Gaines, D. Estlin, T. Schaffer, S. Chauinard, C. (2002) CLEaR: A Frameworkfor Balancing Deliberation and Reactive Control, Proceedings of the AIPSOn-line Planning and Scheduling Workshop, Toulouse, France, April 200212. Fisher, M. (2005) METATEM: The story so far. In Proceedings of the third internationalworkshop on programming multiagent systems (ProMAS-03), Vol. 3862 oflecture notes in artificial intelligence (pp. 322). Springer Verlag13. Freed, M. (1998) Managing Multiple Tasks in Complex, Dynamic Environments.In Proceedings of the National Conference on Artificial Intelligence. Madison, WI.14. Gat, E. (1998) On Three-Layer Architectures, Artificial Intelligence and MobileRobots, MIT press15. Georgeff, M. P. and Lansky, A. L. (1987). Reactive reasoning and planning. InProceedings of the Sixth National Conference on Artificial Intelligence (AAAI-87),pages 677-682, Seattle, WA.16. Heintz, F. Kvarnstrom, J. and Doherty, P. (2010) Bridging the sense-reasoning gap:DyKnow-stream-based middleware for knowledge processing. Journal of AdvancedEngineering Informatics, 24(1):14-25, 2010.17. Inigo-Blasco, P. et al. (2012) Robotics software frameworks for multiagentrobotic systems development, Robotics and Autonomous Systems,doi:10.1016/j.robot.2012.02.00418. Ingrand, F. Lacroix, S. Lemai-Chenevier, S. and Py, F. Decisional Autonomy ofPlanetary Rovers, Journal of Field Robotics, Volume 24, Issue 7, Pages 559 - 580,July 200719. Ingrand, F. F. and Coutance, V. (1993) Real-Time Reasoning using ProceduralReasoning. Technical Report 93-104, LAAS/CNRS, Toulouse, France20. Kramer, J. and Scheutz, M. Development environments for autonomous mobilerobots: A survey, Autonomous Robots, vol. 22, no. 2, pp. 101132, 2007.21. Verbeek, M. (2002) 3APL as programming language for cognitive robots. Mastersthesis, ICS,Utrecht University, 200222. Bratman, M. (1987) Intentions, Plans, and Practical Reason. Harvard UniversityPress, 1987. 24, 113, 14423. Myers, K. L. (1996) A procedural knowledge approach to task-level control, in:Proceedings of the third International Conference on AI Planning Systems (AAAIPress, 1996).24. Nilsson, N. J. (1984) Shakey the robot. Technical Report 323, AI Center, SRIInternational,333 Ravenswood Ave., Menlo Park, CA 94025, Apr 1984.53
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Proceedings of the Tenth Internatio
Page 4 and 5: OrganisationOrganising CommitteeMeh
Page 6: Table of ContentseJason: an impleme
Page 10 and 11: in Sect. 3 the translation of the J
Page 12 and 13: init_count(0).max_count(2000).(a)(b
Page 14 and 15: For instance, a failure in the ERES
Page 16 and 17: {plan, fun start_count_trigger/1,fu
Page 18 and 19: single parameter, an Erlang record
Page 20 and 21: 1. Belief annotations. Even though
Page 22 and 23: decisions taken during the design a
Page 24 and 25: Conceptual Integration of Agents wi
Page 26 and 27: Fig. 2. Active component structurep
Page 28 and 29: the service provider component. As
Page 30 and 31: Fig. 4. Web Service Invocationretri
Page 32 and 33: 01: public interface IBankingServic
Page 34 and 35: tate them in the same way as in the
Page 36 and 37: 01: public interface IChartService
Page 38 and 39: implementations being available for
Page 41: deliberative behavior in BDI archit
Page 44 and 45: layer modules (i.e. nodes) can be d
Page 46 and 47: different methods to choose the cur
Page 48 and 49: also a single scheduler module, imp
Page 50 and 51: andom choice (OR), conditional choi
Page 52 and 53: - Dealing with conflicts based on p
Page 56 and 57: An Agent-Based Cognitive Robot Arch
Page 58 and 59: It has been argued that building ro
Page 60 and 61: EnvironmentHardwareLocal SoftwareC+
Page 62 and 63: a cognitive layer can connect as a
Page 64 and 65: can reliably be differentiated and
Page 66 and 67: 4 ExperimentTo evaluate the feasibi
Page 68 and 69: learn or gain knowledge from experi
Page 70 and 71: A Programming Framework for Multi-A
Page 72 and 73: exchange and storage of tuples (key
Page 74 and 75: Although some success [13] [14] hav
Page 76 and 77: as well as important non-functional
Page 78 and 79: component plans have been instantia
Page 80 and 81: A in the example) can evaluate all
Page 83 and 84: 1. robot-1 issues a Localization(ro
Page 85 and 86: ACKNOWLEDGMENTThis work has been su
Page 87 and 88: The code was analysed both objectiv
Page 89 and 90: a conversation is following. Additi
Page 91 and 92: the context of a communication-heav
Page 93 and 94: Table 1. Core Agent ProtocolsAgent
Page 95 and 96: statistically significant using an
Page 97 and 98: to the conversation and has a perfo
Page 99 and 100: principal reasons. Firstly, it is a
Page 101 and 102: 2. Muldoon, C., O’Hare, G.M.P., C
Page 103 and 104: In the following section we will at
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DevelopmentProductionHuman Readabil
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will then evaluate this new format
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encoder, it is first checked if the
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nents themselves. However, since th
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optimized for this format feature s
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Java serialization and Jadex Binary
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10. P. Hoffman and F. Yergeau, “U
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Caching the results of previous que
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querying an agent’s beliefs and g
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or relative performance of each pla
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were run for 1.5 minutes; 1.5 minut
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Size N K n p c qry U c upd Update c
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epresentation. The cache simply act
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6 ConclusionWe presented an abstrac
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Typing Multi-Agent Programs in simp
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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.
Page 165:
Author IndexAbdel-Naby, S., 69Alelc
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