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4 ExperimentTo evaluate the feasibility of the proposed architecture, we have carried out asimple navigation task using a humanoid robot Nao (See Figure 3) built byAldebaran Robotics. In this task, the Nao robot acts as a message delivererwhich is supposed to enter the destination room and deliver a message to thepeople in the room.Fig. 3. Humanoid robot NaoFig. 4. The main GUI of the robotic behavior layerThe experiment is carried out in a domestic corridor environment. A predefinedmap has been built for the robot to localize itself. The robot begins walkingfrom the starting place, and the goal of the robot is to enter the destination room,to show an arm-raising gesture, and eventually to deliver a coffee-break message.The behavioural layer processes the lower level functions; for example, Localizationis used to estimate the robot’s position, and real-time path planning is usedto steer the walking of the robot. Meanwhile, it will communicate with and provideperceptual knowledge to the high-level cognitive layer. Figure 4 shows themain GUI of the behavioural layer, and Figure 5 shows the GUI for the cognitivelayer implemented in Goal.The navigation task begins with a message navigation broadcasted fromthe behavioural layer to the cognitive layer, which indicates that both the lowlevelcontrol and high-level control are ready to start. Then, the first actioncommand goto(roomA) is generated. The Navigation module in the behaviourallayer takes charge and steers the walking from the starting place to the destinationroom. When the robot arrives at the door of the destination room, apercept at(roomA) is sent to the cognitive layer that will generate the nextaction enterRoom(roomA). After the robot enters the destination room, anotherpercept in(roomA) is sent. Thereafter, when the robot has the belief of being inthe destination room, it will perform the actions: showing gesture(raiseArm)65
Fig. 5. The deliberative reasoning agent in Goaland saying "Hi, Let’s take a coffee break!". In Figure 5, the action logshave been printed out to illustrate the decision making related to the task.Although this navigation task is simple, it shows that uncertain, quantitativesensory data is mapped into symbolic perceptual knowledge and allows the robotto select their actions while performing tasks in a real world environment.5 Conclusion and Future WorkThe navigation task that the robot has performed in the above section demonstratesthe feasibility of using a cognitive layer to control physical robots bymeans of agent-oriented programming. It also demonstrates that the clean separationof sub-symbolic and symbolic layers via the EIS component is feasible.Although our general architecture is similar to the layered approaches that havebeen used in many robot projects [24–27], we believe that the use of EIS providesa more principled approach to manage the interaction between sub-symbolic andsymbolic processors. Of course, it is important that the cognitive layer (agentprogram) needs adequate perceptions to make rational decisions given its specificenvironment. Our architecture provides sufficient support from various componentsdealing with Perception, Knowledge Processing, and Communication toensure this.Future work will concentrate on applying our proposed architecture for multirobotteamwork in the Block World for Teams environment [30], in which eachrobot can exchange their perceptions and share their mental states in the cognitivelayer to coordinate their actions. To this end, in the low-level layers, we willintegrate more image processing functions to endow robots with more perceptioninputs. Besides, we will also develop the adaptive ability so that robots can66
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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
Page 16 and 17: {plan, fun start_count_trigger/1,fu
Page 18 and 19: single parameter, an Erlang record
Page 20 and 21: 1. Belief annotations. Even though
Page 22 and 23: decisions taken during the design a
Page 24 and 25: Conceptual Integration of Agents wi
Page 26 and 27: Fig. 2. Active component structurep
Page 28 and 29: the service provider component. As
Page 30 and 31: Fig. 4. Web Service Invocationretri
Page 32 and 33: 01: public interface IBankingServic
Page 34 and 35: tate them in the same way as in the
Page 36 and 37: 01: public interface IChartService
Page 38 and 39: implementations being available for
Page 41: deliberative behavior in BDI archit
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Page 46 and 47: different methods to choose the cur
Page 48 and 49: also a single scheduler module, imp
Page 50 and 51: andom choice (OR), conditional choi
Page 52 and 53: - Dealing with conflicts based on p
Page 54 and 55: 5. Brooks, R. A. (1991) Intelligenc
Page 56 and 57: An Agent-Based Cognitive Robot Arch
Page 58 and 59: It has been argued that building ro
Page 60 and 61: EnvironmentHardwareLocal SoftwareC+
Page 62 and 63: a cognitive layer can connect as a
Page 64 and 65: can reliably be differentiated and
Page 68 and 69: learn or gain knowledge from experi
Page 70 and 71: A Programming Framework for Multi-A
Page 72 and 73: exchange and storage of tuples (key
Page 74 and 75: Although some success [13] [14] hav
Page 76 and 77: as well as important non-functional
Page 78 and 79: component plans have been instantia
Page 80 and 81: 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
Page 91 and 92: the context of a communication-heav
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
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Page 109 and 110: encoder, it is first checked if the
Page 111 and 112: nents themselves. However, since th
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Page 115 and 116: 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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