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for eventually refining and enriching how such concepts are currently modeledin simpAL.References1. R. Bordini, J. Hübner, and M. Wooldridge. Programming Multi-Agent Systems inAgentSpeak Using Jason. John Wiley & Sons, Ltd, 2007.2. R. Collier. Debugging agents in agent factory. Programming Multi-Agent Systems,pages 229–248, 2007.3. W. R. Cook, W. Hill, and P. S. Canning. Inheritance is not subtyping. In Proceedingsof the 17th ACM SIGPLAN-SIGACT symposium on Principles of programminglanguages, POPL ’90, pages 125–135, New York, NY, USA, 1990. ACM.4. F. Damiani, P. Giannini, A. Ricci, and M. Viroli. A calculus of agents and artifacts.In J. Cordeiro, A. Ranchordas, and B. Shishkov, editors, Software andData Technologies, volume 50 of Communications in Computer and InformationScience. Springer Berlin Heidelberg, 2011.5. S. Danforth and C. Tomlinson. Type theories and object-oriented programmimg.ACM Comput. Surv., 20(1):29–72, Mar. 1988.6. M. Dastani. 2apl: a practical agent programming language. Autonomous Agentsand Multi-Agent Systems, 16(3):214–248, 2008.7. M. Dezani-Ciancaglini, D. Mostrous, N. Yoshida, and S. Drossopoulou. Sessiontypes for object-oriented languages. In D. Thomas, editor, ECOOP 2006, volume4067 of LNCS, pages 328–352. Springer, 2006.8. K. V. Hindriks. Programmingrationalagents in goal. In Multi-Agent Programming:,pages 119–157. Springer US, 2009.9. B. Meyer. Static typing. In ACM SIGPLAN OOPS Messenger, volume 6, pages20–29. ACM, 1995.10. A. Omicini, A. Ricci, and M. Viroli. Artifacts in the A&A meta-model for multiagentsystems. Autonomous Agents and Multi-Agent Systems, 17(3), Dec. 2008.11. A. Rao. AgentSpeak (L): BDI agents speak out in a logical computable language.Lecture Notes in Computer Science, 1038:42–55, 1996.12. A. S. Rao and M. P. Georgeff. BDI Agents: From Theory to Practice. In FirstInternational Conference on Multi Agent Systems (ICMAS95), 1995.13. A. Ricci, M. Piunti, and M. Viroli. Environment programming in multi-agent systems:an artifact-based perspective. Autonomous Agents and Multi-Agent Systems,23:158–192, 2011.14. A. Ricci and A. Santi. Agent-oriented computing: Agents as a paradigm for computerprogramming and software development. In Proc. of the 3rd Int. Conf.on Future Computational Technologies and Applications (Future Computing ’11),Rome, Italy, 2011. IARIA.15. A. Ricci and A. Santi. Designing a general-purpose programming language based onagent-oriented abstractions: the simpAL project. In Proc. of AGERE!’11, SPLASH’11 Workshops, pages 159–170, New York, NY, USA, 2011. ACM.16. R. J. Ross, R. W. Collier, and G. M. P. O’Hare. Af-apl - bridging principles andpractice in agent oriented languages. In Programming Multi-Agent Systems, volume3346 of Lecture Notes in Computer Science, pages 66–88. Springer, 2004.17. P. Wegner and S. B. Zdonik. Inheritance as an incremental modification mechanismor what like is and isn’t like. In Proceedings of the European Conference onObject-Oriented Programming, ECOOP ’88, pages 55–77, London, UK, UK, 1988.Springer-Verlag.147
Learning to Improve Agent Behaviours in GOALDhirendra Singh and Koen V. HindriksInteractive Intelligence Group, Delft University of Technology, The NetherlandsAbstract. This paper investigates the issue of adaptability of behaviourin the context of agent-oriented programming. We focus on improvingaction selection in rule-based agent programming languages using a reinforcementlearning mechanism under the hood. The novelty is thatlearning utilises the existing mental state representation of the agent,which means that (i) the programming model is unchanged and usinglearning within the program becomes straightforward, and (ii) adaptivebehaviours can be combined with regular behaviours in a modular way.Overall, the key to effective programming in this setting is to balancebetween constraining behaviour using operational knowledge, and leavingflexibility to allow for ongoing adaptation. We illustrate this usingdifferent types of programs for solving the Blocks World problem.Keywords: Agent programming, rule selection, reinforcement learning1 IntroductionBelief-Desire-Intention (BDI) [1] is a practical and popular cognitive frameworkfor implementing practical reasoning in computer programs, that has inspiredmany agent programming languages such as AgentSpeak(L) [2], JACK [3], Jason [4],Jadex [5], CANPLAN [6], 3APL [7], 2APL [8], and Goal [9], to name a few. Despiteits success, an important drawback of the BDI model is the lack of a learningability, in that once deployed, BDI agents have no capacity to adapt andimprove their behaviour over time. In this paper, we address this issue in thecontext of BDI-like rule-based agent programming languages. Particularly, weextend the Goal agent programming language [9] for practical systems [10, 11]with a new language primitive that supports adaptive modules, i.e., moduleswithin which action choices resulting from programmed rules are learnt overtime. While we have chosen Goal for this study, our approach applies generallyto other rule-based programming languages. We use an off-the-shelf reinforcementlearning [12] mechanism under the hood to implement this functionality.Our aim is to allow agent developers to easily program adaptive behavioursusing a programming model that they are already familiar with, and withouthaving to explicitly delve into machine learning technologies. Our idea is to leveragethe domain knowledge encoded into the agent program, by directly using themental state representation of the agent for learning purposes. This has the keybenefits that (i) the programmer need not worry about knowledge representationfor learning as a separate issue from programming; (ii) the programming model148
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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
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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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Page 99 and 100: principal reasons. Firstly, it is a
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Page 131 and 132: 6 ConclusionWe presented an abstrac
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Page 137 and 138: 3.1 simpAL OverviewThe main inspira
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Page 165: Author IndexAbdel-Naby, S., 69Alelc
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