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to ensure that messages that form part of the same conversation 1 are interpretedas such. This section provides a brief overview of ACRE’s capabilities. For furtherinformation, it is presented in some detail in [1].The principal components of ACRE are as follows:– Protocol Manager (PM): A component that is shared amongst all agentsresiding on the same agent platform, the PM accesses online protocol repositoriesand downloads protocol definitions at the request of agents.– Conversation Manager (CM): Each agent has its own CM, which is responsiblefor monitoring all its communication so as to group individual messagesinto conversations. Messages are compared to known protocol definitions andexisting conversations to ensure that they are consistent with the availabledescriptions of how communication should be structured.– ACRE/Agent Interface (AAI): Unlike the PM and CM, the AAI is notplatform-independent, with its implementation dependent on the frameworkand/or AOP language being employed. The AAI serves as the API to theCM and PM: agents can perceive and act upon the ACRE components.Additionally, the wider ACRE ecosystem provides an XML format for definingcustom interaction protocols, a standard for the organisation of online protocolrepositories and a suite of tools to aid developers in communication handling.These tools include a graphical protocol designer, a runtime conversation debugger,a GUI to manage and explore protocol repositories and a runtime protocolview to show what protocols have been loaded on an agent platform.ACRE’s representation of protocols uses FSMs where transitions betweenstates are activated by the sending and receiving of messages. An example ofthis can be seen in Figure 3. Here, the conversation is begun by a message beingsent that matches any of the transitions emanating from the initial “Start” state.When this occurs, the variables (prefixed by the ? character) are bound to thevalues contained in the message itself. This will, for example, fix the name of theconversation initiator (bound to the ?player variable) and the other participant(?broker) so that subsequent messages must be sent to and from those agents.If a message fails to match the specification of the relevant protocol, the CMwill raise an error to make the agent aware of this. This feature is not readilyavailable in existing AOP languages, as these typically depend on event triggersto positively match an expected message, with unrecognised or malformedmessages being silently ignored if they match no rule.From an AOP developer’s point-of-view, ACRE facilitates the implementationof interaction protocols by automatically checking messages against knownprotocols and conversations, providing information about the state of conversationsas well as making available a set of actions that operate on them. Theinformation available includes the participants in a conversation, the conversationstate, the messages that make up the conversation history and the protocol1 In this work we draw a distinction between protocols, which define how a seriesof related messages should be structured and conversations, which are individualinstances of agents following a protocol.87
a conversation is following. Additionally, the agent is made aware of conversationevents such as the conversation being advanced by a new message, cancellation,timeouts and errors (such as messages failing to match a transition in a definedprotocol). Available actions include creating or advancing a conversation,cancelling existing conversations or communicating protocol errors to other participants.+initialized : true
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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
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 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: The code was analysed both objectiv
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
Page 119 and 120: Caching the results of previous que
Page 121 and 122: querying an agent’s beliefs and g
Page 123 and 124: or relative performance of each pla
Page 125 and 126: were run for 1.5 minutes; 1.5 minut
Page 127 and 128: Size N K n p c qry U c upd Update c
Page 129 and 130: epresentation. The cache simply act
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
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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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