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learn or gain knowledge from experience. We believe this learning mechanismwill facilitate robots to adjust to or understand environments more efficientlyand reliably in the future.References1. Beetz, M., Mosenlechner, L., Tenorth, M. In: CRAM - A Cognitive Robot AbstractMachine for Everyday Manipulation in Human Environments. IEEE (2010) 1012–10172. Kelley, T.D.: Developing a psychologically inspired cognitive architecture forrobotic control : The symbolic and subsymbolic robotic intelligence control system.Advanced Robotic 3 (2006) 219–2223. Hanford, S.D., Janrathitikarn, O., Long, L.N.: Control of mobile robots usingthe soar cognitive architecture. Journal of Aerospace Computing Information andCommunication 5 (2009) 1–474. Hawes, N., Sloman, A., Wyatt, J., Zillich, M., Jacobsson, H., Kruijff, G., Brenner,M., Berginc, G., Skocaj, D. In: Towards an Integrated Robot with Multiple CognitiveFunctions. Volume 22. Menlo Park, CA; Cambridge, MA; London; AAAIPress; MIT Press; 1999 (2007) 1548–15535. Hindriks, K.: Programming rational agents in goal. In: Multi-Agent Programming:Languages, Tools and Applications. Springer US (2009) 119–1576. http://ii.tudelft.nl/trac/goal (2012)7. Hindriks, K.V., van Riemsdijk, M.B., Behrens, T.M., Korstanje, R., Kraaijenbrink,N., Pasman, W., de Rijk, L.: Unreal goal agents. In: AGS 2010. (2010)8. Behrens, T., Hindriks, K., Dix, J.: Towards an environment interface standard foragent platforms. Annals of Mathematics and Artificial Intelligence (2010) 1–359. Shanahan, M., Witkowski, M.: High-level robot control through logic. EventLondon (2000) 104–12110. Soutchanski, M. In: High-level Robot Programming and Program Execution.(2003)11. Coffey, S., Clark, K. In: A Hybrid, Teleo-Reactive Architecture for Robot Control.(2006)12. Burghart, C., Mikut, R., Stiefelhagen, R., Asfour, T., Holzapfel, H., Steinhaus, P.,Dillmann, R.: A cognitive architecture for a humanoid robot: a first approach.Architecture (2005) 357–36213. Anderson, J.R., Lebiere, C.: The atomic components of thought. Volume 3. Erlbaum(1998)14. Laird, J.E., Newell, A., Rosenbloom, P.S.: Soar: An architecture for general intelligence.Artificial Intelligence 33 (1987) 1–6415. Benjamin, P., Lyons, D., Lonsdale, D.: Designing a robot cognitive architecturewith concurrency and active perception. In: Proceedings of the AAAI Fall Symposiumon the Intersection of Cognitive Science and Robotics. (2004)16. Avery, E., Kelley, T., Davani, D.: Using cognitive architectures to improve robotcontrol : Integrating production systems , semantic networks , and sub-symbolicprocessing. System 77 (1990)17. Laird, J.E.: Toward cognitive robotics. Proceedings of SPIE 7332 (2009) 73320Z–73320Z–1118. Bekey, G.A.: Autonomous Robots: From Biological Inspiration to Implementationand Control. The MIT Press (2005)67
19. Baillie, J.C.: Urbi: Towards a universal robotic low-level programming language.2005 IEEERSJ International Conference on Intelligent Robots and Systems (2005)820–82520. Bradski, G.: The OpenCV Library. Dr. Dobb’s Journal of Software Tools (2000)21. Azad, P., Asfour, T., Dillmann, R.: Combining Harris interest points and theSIFT descriptor for fast scale-invariant object recognition. In: 2009 IEEE/RSJInternational Conference on Intelligent Robots and Systems, IEEE (2009) 4275–428022. Fischler, M.A., Bolles, R.C.: Random sample consensus: a paradigm for modelfitting with applications to image analysis and automated cartography. Communicationsof the ACM 24 (1981) 381–39523. Mika, S., Schaefer, C., Laskov, P., Tax, D., Mller, K.R. In: Support vector machines.Volume 1. Springer (2004) 1–3324. Qureshi, F., Terzopoulos, D., Gillett, R.: The cognitive controller: A hybrid, deliberative/reactivecontrol architecture for autonomous robots. Innovations in AppliedArtificial Intelligence 17th International Conference on Industrial and EngineeringApplications of Artificial Intelligence and Expert System IEAAIE 2004 3029(2004) 1102–111125. Arkin, R.C.: Integrating behavioral, perceptual, and world knowledge in reactivenavigation. Robotics and Autonomous Systems 6 (1990) 105–12226. Connell, J.H. In: SSS: a hybrid architecture applied to robot navigation. Volume 3.IEEE Comput. Soc. Press (1992) 2719–272427. Gat, E. In: Integrating Planning and Reacting in a Heterogeneous AsynchronousArchitecture for Controlling Real-World Mobile Robots. Citeseer (1992) 809–81528. Brooks, R.: A robust layered control system for a mobile robot. IEEE Journal ofRobotics and Automation 2 (1986) 14–2329. Vernon, D., Metta, G., Sandini, G.: A survey of artificial cognitive systems: Implicationsfor the autonomous development of mental capabilities in computationalagents. Evolutionary Computation, IEEE Transactions on 11 (2007) 151 –18030. Johnson, M., Jonker, C.M., Riemsdijk, B.V., Feltovich, P.J., Bradshaw, J.M.: Jointactivity testbed: Blocks world for teams (bw4t). Engineering Societies in the AgentsWorld X 442 (2009) 433–44268
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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
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 54 and 55: 5. Brooks, R. A. (1991) Intelligenc
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 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
Page 105 and 106: DevelopmentProductionHuman Readabil
Page 107 and 108: will then evaluate this new format
Page 109 and 110: encoder, it is first checked if the
Page 111 and 112: nents themselves. However, since th
Page 113 and 114: optimized for this format feature s
Page 115 and 116: Java serialization and Jadex Binary
Page 117 and 118: 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.
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Author IndexAbdel-Naby, S., 69Alelc
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