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{plan, fun start_count_trigger/1,fun start_count_context/1,[Fun1, Fun2]}where the start_count_trigger/1 and start_count_context/1 functions implementthe trigger and the context respectively. The list at [Fun1,Fun2] representsthe plan body, where Fun1 implements the formula +actual_count(X)and Fun2 implements the formula !count.Intentions. The stack of partially instantiated plans that compose each of theJason intentions is represented as a list of Erlang records. Each of these recordsis composed of four elements. The first element is the event that triggered theplan. This element is kept as a meta-level information that can be accessedby the intention selection function. For instance, we could give priority to theexecution of intentions whose partially instantiated plan on top of the stackresolves a test goal. The second element is the plan record chosen by the optionselection function and, again, is intended to serve as a meta-level informationaccessible by the intention selection function. The third element is a tuple thatrepresents the intended means of the intention plan, i.e. the bindings for thevariables in the partially instantiated plan. The fourth element is a list of Erlangfunctions, representing the formulas of the partially instantiated plan that havenot been executed yet. If an intention is selected for execution, the record forthe partially instantiated plan on top of it (i.e. the first element of the list thatrepresents the intention) is obtained. Then, the function at the head of thelist of Erlang functions in the fourth element of this record is applied to thecurrent variable valuation. Finally, this last function is removed from the list.This process amounts to processing the formula on top of the intention stack asis required by the specification in [9].Selection functions. The event, plan and intention selection functions for aMAS can be customised by providing new implementations (in Erlang) of thefunctions selectEvent, selectPlan and selectIntention.3.5 Process Orchestration and CommunicationThe multiagent system generated by the translation from Jason to Erlang mapseach agent to an Erlang process, all executing on the same Erlang node. EachErlang agent process can be accessed using either its process identifier, or thename of the Jason agent. The name of the agent is associated with the Erlangprocess using the Erlang process registry. In case multiple agents are created withthe same name an integer (corresponding to the creation order) is appended tothe registered name to keep such names unique. An item for future work isto extend Jason with new mechanisms to create multiple agents from the sameagent definition, and to associate symbolic names with such agents, as the presentmechanisms are somewhat unwieldy.15
The communication between agents is implemented using Erlang messagepassing. As an example, consider a system where the agent alice sends differentmessages to agent bob by executing the internal action formulas:.send(bob, tell, counter(3)}.send(bob, untell, price(coffee,300)).send(bob, achieve, move_to(green_cell))The actions are mapped to the following Erlang expressions:bob ! {communication,alice,{tell,{counter,{3},[]}}}.bob ! {communication,alice,{untell,{price,{coffee,300},[]}}}.bob ! {communication,alice,{achieve,{move_to,{green_cell},[]}}}).The Erlang expression Receiver ! Message deposits Message into the mailboxof agent Receiver. The atom communication is used to declare the messagetype. It is included in the implementation to enable processes to exchange othertypes of messages, possibly not related to agent communication, in a futureextension of eJason.Agent bob can process the different messages sent by alice by checking itsmailbox, which is performed automatically in every iteration of the reasoningcycle. The Erlang expression that retrieves the message from the process mailbox:receive{communication,Sender,{Ilf,Message}} ->case Ilf oftell -> ... %% Process tell messageuntell -> ... %% Process untell messageachieve -> ... %% Process achieve messageend.These examples show how easily the agent communication between Jasonagents can be implemented in Erlang. The simple yet efficient process communicationmechanism of Erlang is one of the principal motivations to implementJason agents using the Erlang programming language.In the example above, all the agents are located in the same MAS architecture;messaging between agents in different architectures would be easy tosupport too, and would not require the use of a communication middleware likeSACI. However, such an extension is not yet implemented in eJason.3.6 Representing the Jason Reasoning Cycle in ErlangThe Jason reasoning cycle [8] must of course be represented in eJason. We implementthe reasoning cycle using an Erlang function reasoningCycle with a16
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Page 4 and 5: OrganisationOrganising CommitteeMeh
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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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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.
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Author IndexAbdel-Naby, S., 69Alelc
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