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present in a number of plans so that the state of the contract net conversationis known at all times. However, as our evaluation has shown, less experiencedprogrammers are more prone to omitting this type of checking.Another issue arises in the choice of an identifier for the conversation (referredto as the CNPId variable in the extract shown). As presented, this ID is manuallyspecified in the original intention that triggers the start of the contract net (notshown). ACRE assigns IDs to conversations automatically, meaning that theprogrammer need not be concerned with this aspect of conversation handling.From this analysis, we can see that in the absence of integrated conversationhandling, AOP developers using Jason are also susceptible to the issues outlinedabove. We believe that the type of conversation handling ACRE provideswould help to avoid these pitfalls and so aid the development of reliable, scalableprotocol implementations.7 Conclusions and Future WorkWe have described an experiment whereby two groups of students were requiredto solve a communication-focused problem with and without the use of ACRE.Objective metrics indicated that ACRE can reduce the amount of code requiredto implement the protocols provided when compared to implementing protocolswithout ACRE.On further subjective analysis, a number of issues arose with non-ACRE code.These would require substantial modification of the code if the scenario were extendedby the addition of additional Player agents, duplicate core agents, similarprotocols or malicious agents of any type. The issues observed cannot occur withthe user of ACRE, as the automatic conversation management ensures that bothmessage senders and sequence are checked without developer intervention.We also analysed some best-practice conversation-handling code written forJason and observed that the issues identified are applicable to that platform. Wetherefore suggest that a conversation management framework such as ACRE isgenerally desirable to aid the development of communication-heavy MASs.The experiment described in this paper was carried out by a single group ofundergraduate students. It is intended to repeat the experiment for additionalstudent groups, as this becomes possible. In particular, it is intended to use thesame scenario in an upcoming Agent Oriented Software Engineering module.This is part of a part-time Masters course and the students are experienced industrysoftware developers, albeit not using agents. This will give an insight intoa different type of developer to the undergraduate students studied previously.References1. Lillis, D., Collier, R.W.: Augmenting Agent Platforms to Facilitate ConversationReasoning. In Dastani, M., Seghrouchni, A.E.F., Hubner, J.F., Leite, J., eds.: Postproceedingsof the 3rd International Workshop on LAnguages, methodologies andDevelopment tools for multi-agent systemS, Lyon, France, Springer (2011)99
2. Muldoon, C., O’Hare, G.M.P., Collier, R.W., O’Grady, M.J.: Towards PervasiveIntelligence : Reflections on the Evolution of the Agent Factory Framework. InEl Fallah Seghrouchni, A., Dix, J., Dastani, M., Bordini, R.H., eds.: Multi-AgentProgramming: Languages, Platforms and Applications and Applications. SpringerUS, Boston, MA (2009) 187–2123. Russell, S., Jordan, H., O’Hare, G.M.P., Collier, R.W.: Agent Factory : A Frameworkfor Prototyping Logic-Based AOP Languages. In: Proceedings of the NinthGerman Conference on Multi-Agent System Technologies (MATES 2011), Berlin,Germany (2011)4. Bellifemine, F., Caire, G., Trucco, T., Rimassa, G.: JADE Programmer’s Guide(JADE 4.0) (2010)5. Barbuceanu, M., Fox, M.S.: COOL: A language for describing coordination inmulti agent systems. In: Proceedings of the First International Conference onMulti-Agent Systems (ICMAS-95). (1995) 17–246. Cost, R.S., Finin, T., Labrou, Y., Luan, X., Peng, Y., Soboroff, I., Mayfield, J.,Boughannam, A.: Jackal: a Java-based Tool for Agent Development. In: WorkingPapers of the AAAI-98 Workshop on Software Tools for Developing Agents, AAAIPress (1998)7. Bradshaw, J.M., Dutfield, S., Benoit, P., Woolley, J.D.: KAoS: Toward anindustrial-strength open agent architecture. Software Agents (1997) 375–4188. Huget, M.P., Koning, J.L.: Interaction Protocol Engineering. Communications(2003) 291–3099. Parunak, H.: Visualizing Agent Conversations: Using Enhanced Dooley Graphs forAgent Design and Analysis. In: Proceedings of the Second International Conferenceon Multi-Agent Systems (ICMAS). (1996)10. Hochstein, L., Basili, V.R., Vishkin, U., Gilbert, J.: A pilot study to compareprogramming effort for two parallel programming models. Journal of Systems andSoftware 81(11) (2008) 1920–193011. Luff, M.: Empirically Investigating Parallel Programming Paradigms : A NullResult. In: Workshop on Evaluation and Usability of Programming Languagesand Tools (PLATEAU). (2009) 43–4912. Rossbach, C.J., Hofmann, O.S., Witchel, E.: Is Transactional Programming ActuallyEasier? ACM SIGPLAN Notices 45(5) (2010) 47–5613. VanderWiel, S.P., Nathanson, D., Lilja, D.J.: Complexity and performance inparallel programming languages. In: Second International Workshop on High-LevelProgramming Models and Supportive Environments. (1997) 3–1214. Rao, A.S.: AgentSpeak (L): BDI agents speak out in a logical computable language.Agents Breaking Away (1996)15. Bordini, R.H., Hübner, J.F., Wooldridge, M.: Programming multi-agent systemsin AgentSpeak using Jason. Wiley-Interscience (2007)100
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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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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
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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
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Page 93 and 94: Table 1. Core Agent ProtocolsAgent
Page 95 and 96: statistically significant using an
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Page 99: principal reasons. Firstly, it is a
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Page 105 and 106: DevelopmentProductionHuman Readabil
Page 107 and 108: will then evaluate this new format
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Page 111 and 112: nents themselves. However, since th
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Page 115 and 116: Java serialization and Jadex Binary
Page 117 and 118: 10. P. Hoffman and F. Yergeau, “U
Page 119 and 120: Caching the results of previous que
Page 121 and 122: querying an agent’s beliefs and g
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Page 127 and 128: Size N K n p c qry U c upd Update c
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Page 131 and 132: 6 ConclusionWe presented an abstrac
Page 133 and 134: Typing Multi-Agent Programs in simp
Page 135 and 136: 1 // agent ag02 iterations (" zero
Page 137 and 138: 3.1 simpAL OverviewThe main inspira
Page 139 and 140: 3.2 Typing Agents with Tasks and Ro
Page 141 and 142: Defining Agent Scripts in simpAL (F
Page 143 and 144: that sends a message to the receive
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Page 147 and 148: Given an organization model, it is
Page 149 and 150: 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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