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 <strong>of</strong> CLIMA ’05. (2006) 373–38414. Behrens, T., Dix, J., Köster, M., Hübner, J., eds.: Special Issue about Multi-Agent-ContestII. Volume 61 <strong>of</strong> Annals <strong>of</strong> Mathematics and Artificial Intelligence. Springer, Netherlands(2011)15. Dennis, L.: Plan indexing for state-based plans. In: Informal Proceedings <strong>of</strong> 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 <strong>of</strong> <strong>Agents</strong> 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 <strong>of</strong> the International Joint Conferenceon Artificial Intelligence (IJCAI). (2011) 2525–2530131
Typing Multi-Agent Programs in simpALAlessandro Ricci and Andrea Santi<strong>University</strong> <strong>of</strong> 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 programs<strong>of</strong> a certain complexity to catch errors at compile time beforeexecuting a program and to improve the overall design <strong>of</strong> 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 <strong>of</strong>a certain complexity [9, 5, 3]. Generally speaking, the definition <strong>of</strong> a (strongand static) type system in a programming language brings two main benefits.First, it enables compile-time error checking, greatly reducing the cost <strong>of</strong> errorsdetection—from both a temporal and economic point <strong>of</strong> view. Second, itprovides developers with a conceptual tool for modeling generalization/specializationrelationships among concepts and abstractions, eventually specializingexisting ones through the definition <strong>of</strong> proper sub-types and making it possibleto fully exploit the principle <strong>of</strong> 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 s<strong>of</strong>tware systems in general [14]. To authors’knowledge, there are no agent programming languages in the state-<strong>of</strong>-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 <strong>of</strong> 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] (<strong>Agents</strong> 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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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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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
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