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1. Belief annotations. Even though our Jason parser accepts code with beliefannotations, these annotations are not taken into account when resolvingplans (e.g., when checking whether a plan context is satisfied).2. Annotations on plan labels. The meta-level information associated withplans is removed during the lexical analysis.3. Plan failure handling. Whenever a plan fails, e.g., because test goal inthe plan body cannot be successfully resolved, the whole intention that theplan belongs to is dropped. Moreover, no new event is generated as a resultof the plan failure.4. Environment. The environment of eJason programs is not currently modelled.Therefore, no external actions, except console output, are allowed andno perception phase is required.5. Distribution. There is no support for distributed agents.6. Communication. The only illocutionary forces that are properly processedare tell, untell and achieve. Messages with any other kind of illocutionaryforce are ignored and dropped from the mailbox of the agent.7. Library of internal actions. The only internal actions considered are“.print” (to display text on the standard output) and “.send” (to interactwith other agents in the same multiagent system).8. Unbound plan triggers. The trigger of every plan must be either an atomor a predicate (whose parameters do not need to be bound) but never avariable.9. Decomposition operator. The binary operator “=..”, used to (de)constructliterals (i.e. predicates and terms), is not accepted by the parser.10. Code order. The grammar accepted by the parser is similar to the simplifiedone presented in Appendix 1 of [9]. Therefore, the source code to betranslated must state first the initial beliefs and rules, followed by the initialgoals and, finally, the different plans.11. Multiagent system architecture. There is only one kind of agent infrastructureimplemented. It runs all the agents in a multiagent system withinthe same Erlang node.4 ExperimentsTo test the performance of eJason, we use two simple Jason programs. The firstis the counter example of Fig. 2 in Section 3.1. The second represents an agentthat outputs two greeting messages on the console. To add some complexity tothe behaviour, the contents of those messages are obtained from the set of beliefsof the agent using queries which have both bound and unbound variables. Theexamples were run using different numbers of homogeneous agents, i.e., all theagents behaved the same. All of them were run under Ubuntu Linux version10.04 in a computer with two 2.53 GHz processors. With these examples, wewant to measure the execution time of the generated MAS and their scalabilitywith respect to the number of agents in the system.The preliminary results are presented in Tables 1 and 2.19
Number of Jason eJasonAgents Execute Time (magnitude) Execution Time (milliseconds)10 milliseconds 2100 milliseconds 461000 seconds 18110000 minutes 1916100000 not measurable 18674500000 not measurable 97086800000 not measurable 165522Table 1. Execution times for the counter multiagent systemNumber of Jason eJasonAgents Execute Time (magnitude) Execution Time (milliseconds)10 milliseconds 1100 milliseconds 151000 seconds 14310000 minutes 1550100000 not measurable 154415300000 not measurable 484371Table 2. Execution times for the greetings multiagent systemThe results indicate that the multiagent systems generated by eJason scaleto some hundreds of thousands of agents with an average execution time of afew seconds. Regarding the multiagents systems generated by Java-based Jason,we can see that they required more time to execute (the exact time quantitiescould not be precisely measured) and that it was not possible to increase thenumber of agents over a few thousands (in the cases labeled as not measurablea java.lang.OutOfMemoryError exception was raised).Clearly these are only preliminary findings as more thorough benchmarkingis needed.5 Conclusions and Future WorkIn this paper we have described a prototype implementation of eJason, an implementationof Jason, an agent-oriented programming language, in the Erlangconcurrent functional programming language. The implementation was ratherstraightforward due to the similarities of Jason and Erlang. eJason is able togenerate Erlang code for a significant subset of Jason. Early results are promising,as the multiagent systems running under the Erlang runtime system canmake use of the Erlang lightweight processes to compose systems of thousands ofagents, where the process generation, scheduling, and communication introducea negligible overhead. We also describe and motivate some of the implementation20
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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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