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10. JIProlog. http://www.ugosweb.com/jiprolog/ (2011)11. SWI-Prolog. http://www.swi-prolog.org/ (2011)12. Elevator Simulator. http://sourceforge.net/projects/elevatorsim/(2011)13. Dastani, M., Dix, J., Novak, P.: The first contest on multi-agent systems based on computationallogic. In: Proceedings of CLIMA ’05. (2006) 373–38414. Behrens, T., Dix, J., Köster, M., Hübner, J., eds.: Special Issue about Multi-Agent-ContestII. Volume 61 of Annals of Mathematics and Artificial Intelligence. Springer, Netherlands(2011)15. Dennis, L.: Plan indexing for state-based plans. In: Informal Proceedings of DALT 11.(2011)16. Thielscher, M.: Pushing the envelope: Programming reasoning agents. In: AAAI WorkshopTechnical Report WS-02-05: Cognitive Robotics, AAAI Press (2002)17. Giacomo, G.D., Lespérance, Y., Levesque, H.J., Sardina, S.: IndiGolog: A high-level programminglanguage for embedded reasoning agents. In Bordini, R.H., Dastani, M., Dix, J.,Fallah-Seghrouchni, A.E., eds.: Multi-Agent Programming: Languages, Platforms and Applications.Springer (2009) 31–7218. Koch, F., Dignum, F.: Enhanced deliberation in BDI-modelled agents. In: Advances inPractical Applications of Agents and Multiagent Systems (PAAMS 2010), Springer (2010)59–6819. Singh, D., Sardina, S., Padgham, L., James, G.: Integrating learning into a BDI agent forenvironments with changing dynamics. In: Proceedings of the International Joint Conferenceon Artificial Intelligence (IJCAI). (2011) 2525–2530131
Typing Multi-Agent Programs in simpALAlessandro Ricci and Andrea SantiUniversity of Bolognavia Venezia 52, 47023 Cesena, Italy{a.ricci,a.santi}@unibo.itAbstract. Typing is a fundamental mechanism adopted in mainstreamprogramming languages, important in particular when developing programsof a certain complexity to catch errors at compile time beforeexecuting a program and to improve the overall design of a system. Inthis paper we introduce typing also in agent-oriented programming, byusing a novel simple agent programming language called simpAL, whichhas been conceived from scratch to have this feature.1 IntroductionTyping is an important mechanism introduced in traditional programming languages,particularly useful if not indispensable when developing programs ofa certain complexity [9, 5, 3]. Generally speaking, the definition of a (strongand static) type system in a programming language brings two main benefits.First, it enables compile-time error checking, greatly reducing the cost of errorsdetection—from both a temporal and economic point of view. Second, itprovides developers with a conceptual tool for modeling generalization/specializationrelationships among concepts and abstractions, eventually specializingexisting ones through the definition of proper sub-types and making it possibleto fully exploit the principle of substitutability [17] for supporting a safeextension and reuse in programming.We argue that these features could be very useful and important also foragent-oriented programming (AOP), in particular as soon as AOP is investigatedas a paradigm for developing software systems in general [14]. To authors’knowledge, there are no agent programming languages in the state-of-the-artthat fully support typing and related features. Consequently, the support whichis provided by existing languages to catch errors before executing the system isquite weak. To this purpose, in this paper we describe an approach which introducestyping in agent oriented programming, in particular by means of a novelsimple agent programming language called simpAL which has been conceivedfrom scratch to have this feature. simpAL, whose general design and conceptshave been already introduced elsewhere vey recently [15], has been conceived onthe one side drawing inspiration from existing APLs based on the BDI model [12]– AgentSpeak(L) [11] / Jason [1] in particular – and existing meta-models suchas the A&A [10] (Agents and Artifacts), along with related frameworks such as132
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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
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
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
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Page 111 and 112: nents themselves. However, since th
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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
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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: 6 ConclusionWe presented an abstrac
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
Page 151 and 152: 2.1 Agent Programming LanguagesAgen
Page 153 and 154: choosing actions is to find a good
Page 155 and 156: 1 init module {2 knowledge{3 block(
Page 157 and 158: of a module. For example, to change
Page 159 and 160: if bel(on(X,Y), clear(X)), a-goal(c
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Page 163 and 164: the current percepts of the agent.
Page 165: Author IndexAbdel-Naby, S., 69Alelc
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