14. Džeroski, S., Raedt, L.D., Driessens, K.: Relational reinforcement learning. MachineLearning 43 (2001) 7–52 10.1023/A:1007694015589.15. Bordini, R.H., Dix, J., Dastani, M., Seghrouchni, A.E.F.: Multi-Agent Programming:Languages, Platforms and Applications. Volume 15 <strong>of</strong> Multiagent Systems,Artificial Societies, and Simulated Organizations. Springer (2005)16. Bordini, R.H., Dix, J., Dastani, M., Seghrouchni, A.E.F.: Multi-Agent Programming:Languages, Tools and Applications. Springer (2009)17. Broekens, J., Hindriks, K., Wiggers, P.: Reinforcement Learning as Heuristicfor Action-Rule Preferences. In: Programming Multi-Agent Systems (ProMAS).(2010)18. Hindriks, K.V., Riemsdijk, M.B.: Declarative agent languages and technologies vi.Springer-Verlag (2009) 215–23219. Bellman, R.E.: Dynamic Programming. Princeton <strong>University</strong> Press (1957)20. Watkins, C.J.: Learning from delayed rewards. PhD thesis, King’s College London(1989)21. Andre, D., Russell, S.J.: State abstraction for programmable reinforcement learningagents. In: Eighteenth national conference on Artificial intelligence, Menlo Park,CA, USA, American Association for Artificial Intelligence (2002) 119–12522. Pokahr, A., Braubach, L., Lamersdorf, W.: Jadex: A BDI reasoning engine. In:Multi-Agent Programming. Volume 15 <strong>of</strong> Multiagent Systems, Artificial Societies,And Simulated Organizations. Springer (2005) 149–17423. Subagdja, B., Sonenberg, L., Rahwan, I.: Intentional learning agent architecture.Autonomous <strong>Agents</strong> and Multi-Agent Systems 18 (2009) 417–47024. Singh, D., Sardina, S., Padgham, L.: Extending BDI plan selection to incorporatelearning from experience. Robotics and Autonomous Systems 58 (2010) 1067–107525. Singh, D., Sardina, S., Padgham, L., Airiau, S.: Learning context conditions forBDI plan selection. In: Proceedings <strong>of</strong> Autonomous <strong>Agents</strong> and Multi-Agent Systems(AAMAS). (May 2010) 325–33226. Singh, D., Sardina, S., Padgham, L., James, G.: Integrating learning into a BDIagent for environments with changing dynamics. In Toby Walsh, C.K., Sierra, C.,eds.: Proceedings <strong>of</strong> the International Joint Conference on Artificial Intelligence(IJCAI), Barcelona, Spain, AAAI Press (July 2011) 2525–253027. Anderson, J., Bothell, D., Byrne, M., Douglass, S., Lebiere, C., Qin, Y.: An integratedtheory <strong>of</strong> the mind. Psychological review 111(4) (2004) 103628. Fu, W., Anderson, J.: From recurrent choice to skill learning: A reinforcementlearningmodel. Journal <strong>of</strong> experimental psychology: General 135(2) (2006) 18429. Klahr, D., Langley, P., Neches, R.: Production system models <strong>of</strong> learning anddevelopment. The MIT Press (1987)30. Laird, J., Rosenbloom, P., Newell, A.: Chunking in soar: The anatomy <strong>of</strong> a generallearning mechanism. Machine Learning 1(1) (1986) 11–4631. Nason, S., Laird, J.: Soar-rl: Integrating reinforcement learning with soar. CognitiveSystems Research 6(1) (2005) 51–5932. Nejati, N., Langley, P., Konik, T.: Learning hierarchical task networks by observation.In: International Conference on Machine Learning, ACM Press (2006)665–67233. Slaney, J., Thiébaux, S.: Blocks world revisited. Artificial Intelligence 125(1-2)(2001) 119–15334. Gupta, N., Nau, D.: On the complexity <strong>of</strong> blocks-world planning. Artificial Intelligence56(2-3) (1992) 223–25435. Guyon, I., Elisseeff, A.: An introduction to variable and feature selection. TheJournal <strong>of</strong> Machine Learning Research 3 (2003) 1157–1182163
Author IndexAbdel-Naby, S., 69Alelchina, N., 117Behrens, T., 117Braubach, L., 23Broxvall, M., 69Collier, R., 85Díaz, A.F., 7Dastani, M., 39Dragone, M., 69Earle, C.B., 7Fredlund, L., 7Hindriks, K., 55, 117, 148Jander, K., 101Jordan, H.R., 85Lamersdorf, W., 101Lillis, D., 85Logan, B., 117Meyer, J., 39Pokahr, A., 23Ricci, A., 132Santi, A., 132Singh, D., 148Swords, D., 69Torre, L., 39Wei, C., 55Ziafati, P., 39
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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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tate them in the same way as in the
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01: public interface IChartService
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implementations being available for
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deliberative behavior in BDI archit
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layer modules (i.e. nodes) can be d
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different methods to choose the cur
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also a single scheduler module, imp
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andom choice (OR), conditional choi
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- Dealing with conflicts based on p
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5. Brooks, R. A. (1991) Intelligenc
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An Agent-Based Cognitive Robot Arch
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It has been argued that building ro
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EnvironmentHardwareLocal SoftwareC+
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a cognitive layer can connect as a
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can reliably be differentiated and
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4 ExperimentTo evaluate the feasibi
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learn or gain knowledge from experi
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A Programming Framework for Multi-A
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exchange and storage of tuples (key
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Although some success [13] [14] hav
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as well as important non-functional
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component plans have been instantia
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A in the example) can evaluate all
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1. robot-1 issues a Localization(ro
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ACKNOWLEDGMENTThis work has been su
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The code was analysed both objectiv
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a conversation is following. Additi
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the context of a communication-heav
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Table 1. Core Agent ProtocolsAgent
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statistically significant using an
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to the conversation and has a perfo
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principal reasons. Firstly, it is a
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2. Muldoon, C., O’Hare, G.M.P., C
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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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