Here - Agents Lab - University of Nottingham

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– Dealing with conflicts based on plans priorities and deadlines including dynamicprioritization and preemption.– Supporting different policies to deal with preempted and failed plans suchas stopping, suspending or aborting a preempted plan or a failed plan.– Recovering from a plan failure and performing wind-down activities aftersuspension and before resuming a plan.Furthermore, in addition to mechanisms needed for representing, reasoningon and resolving conflicts in plans execution, similar mechanisms are also necessarywhen processing goals and events to generate plans. For example processinggoals and events should be ordered based on their priorities and a lower prioritygoal conflicting with a higher priority goal might needed to be considered asfailed or to be kept to be processed later. These mechanisms are in particularneeded in a distributed control paradigm in which a control component mightreceives conflicting goals from other components and should provide feedback tothose components if it can handle those goals or not.7 ConclusionThe paper presents various requirements for agent programming languages in orderto implement cognitive robot control systems. These requirements are drawnpartially based on an analysis of the problem at hand and partially based on astudy of current autonomous robot control systems. We do not claim that the setof requirements presented in this paper is complete. However by analyzing mainaspects of programming cognitive robots and providing an overview of capabilitiesof current high-level robot programming tools, we have made a contributionto the systematic analysis and presentation of agent programming languages requirementsfor programming cognitive robots. In this paper we limit ourselvesto single robot applications. Multi-robot scenarios impose other requirements onagent programming languages for coordination, cooperation and communicationbetween robots which have been left for further research.The requirements presented in this paper show a big gap between capabilitiesof current agent programming languages and demands of programmingautonomous robot control systems. Our research aim is to bridge this gap byextending the 2APL agent programming language to meet these requirements.One of such requirements is integration with existing robotic frameworks to facilitatecontrolling and communication with functional modules developed inthese frameworks. This can encourage the use of agent programming languagesby robotic community and facilitate their use for rapid prototyping and developmentof autonomous systems. To address this requirement, we have developedan environment interface for 2APL facilitating its integration with ROS usingROS communication mechanisms. We have used ROS to provide basic roboticcapabilities such as face recognition, voice recognition and a number of high-levelactions such as sit-down(), stand-up(), turn-neck(O) and walk-to(X,Y) for ourNAO robots. Using 2APL and ROS, we have developed a demo application in51
which different NAO’s movement can be controlled by voice. Also NAO can becommanded to remember the face of a user and whenever a user greets NAO, ifNAO recognizes the user’s face, it greets the user by his/her name.A part of the future work is to extend 2APL to provide support for developmentof a sensory component for processing and management of heterogeneoussensory information. This component should enable unified representation ofsensory data and domain knowledge and reasoning on high-level events (i.e. situations).The sensory information managed and processed by the sensory componentshould be accessible by a BDI based control component in a symbolicform and through both querying and receiving as events.Another part of the future work is extending plan representation and executioncapabilities of 2APL. An extensive list of such required capabilities ispresented in section 6. These capabilities include providing support for governingthe execution of plans by sequential, temporal and priority orderings, andbased on different internal conditions and external events, representing and handlingconflicts in parallel execution of plans, and monitoring and handling plansexecution failures.Finally, we aim to provide support for development of distributed real-timeBDI-based control systems. This requires a specific version of 2APL dedicated todevelopment of real-time control components. The semantic and implementationof such version should guarantee safe and bounded-time computations to enableanalysis and guaranteeing required real-time properties of a control component.Also a dedicated architecture and runtime environment is required to supportthe real-time coordination and communication of different control components ofa robot. We envision a ROS architecture consisting of a distributed set of controlcomponent with different functionalities (e.g. deliberative, reactive, plan failurehandling) which can share beliefs and goals and other ROS components (i.e.functional layer modules) including sensory components described above. Thesecomponents can have different real-time requirements. Some of them should berun in real-time and guarantee bounded reaction and response time to events.Our initial proposal for development of the real-time distributed architecture isto use the Orocos Real-Time Toolkit which has been seamlessly integrated withROS.References1. Anand S. Rao. (1996) AgentSpeak(L): BDI agents speak out in a logica computablelanguage. In W. Vander Velde and J. W. Perram, editors, Agents Breaking Away(LNAI), volume 1038 of LNAI, pages 4255. Springer-Verlag.2. Bernard, D. et.al. (2000) Remote Agent Experiment: Final Report, NASA TechnicalReport, Feb. 20003. Bordini, R. Braubach, L. Dastani, M. Fallah A.E. Gomez-Sanz, J. Leite, J, OHare,G. Pokahr, A. and Ricci, A. (2006) A survey of programming languages and platformsfor multi-agent systems. In Informatica 30, pages 33444. Bordini,R.H. hbner, J.F. and Wooldridge, M. (2007) Programming Multi-agent Systemsin AgentSpeak Using Jason. Wiley Series in Agent Technology52
Page 2 and 3: Proceedings of the Tenth Internatio
Page 4 and 5: OrganisationOrganising CommitteeMeh
Page 6: Table of ContentseJason: an impleme
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Page 85 and 86: ACKNOWLEDGMENTThis work has been su
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Page 89 and 90: a conversation is following. Additi
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Page 93 and 94: Table 1. Core Agent ProtocolsAgent
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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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