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A in the example) can evaluate all bids, including its own, before sending a message(to platform D in the example) to accept the best bid. Upon reception ofthe accept message, the chosen platform will instantiate the selected componentplan and automatically register with R-OSGi the service goal it provides. Theinitiating platform will then retrieve the reference to the service goal from theremote platform, create a proxy, and export it to the local OSGi registry.Fig. 2. Automatic distribution of Self -OSGi SystemsWithin the RUBICON Control Architecture, the distributed extension ifSelf -OSGi has the following roles:– provide the backbone upon which AI techniques, such as planning, cognitivereasoning and learning, can be integrated into a single system.– provide a semantic vocabulary for expressing the capabilities and the requirementsof all available devices and software components in the ecology,in order to support self-organization capabilities and the modular specificationof the behaviour of the robotic ecology.– enhance system’s scalability by framing it as a multi-agent system in orderto leverage agent communication languages (ACLs) and multi-agent system(MAS) coordination & negotiation protocols.– reduce the gap between the mainstream software solutions traditionally usedin AmI/AAL domains and the state of the art techniques used in agentorientedsoftware engineering and in the control of robotic ecologies.The final element to allow the use of the same mechanisms for the coordinationof robotic ecologies is the interface between Self -OSGi and the PEISmiddleware discussed in Section 2.79
Specifically, the PEIS tuplespace is used to:1. serve as platform discovery mechanism, by leveraging its peer-to-peer communicationfunctionalities to re-create on each platform the directory of allthe platforms available over the network. Self -OSGi uses these directories tofind out the url of remote instances of Self -OSGi, before using R-OSGi tocontact them by sending service goal requests and conducting auction-basednegotiation protocols.2. communicate with the PEIS-Init components installed on each platform, inorder to manage PEIS components running outside the JVM, such as roboticbehaviour components implemented in native C/C++ languages. In thesecases, component plans in Self -OSGi acts as proxies of the underlying PEIScomponent they manage. A basic implementation - PEIS Component Monitor- provides generic functionalities to start/stop/configure peis componentsby setting the value of special tuples used to propagate these instructionsvia the tuplespace.5 Testing Tools and ExampleFor the purpose of testing and demonstrating the overall implementation of themulti-agent framework presented in this paper, we have developed a set of PEIStools capable of simulating the sensing and actuation aspects of components,while running the full framework for specification, introspection, deployment,communication and configuration. These tools are intended to simplify the developmentand debugging of the agent based tools and their interaction with therobotic ecology, and facilitate their adoption in real world situations.The test illustrated in this section shows the ability of our framework toautomatically generate a sequence of configurations to perform a given task in thecurrent context (state). The test has been performed in a low-fidelity simulatoremulating the behaviour of real devices distributed over three platforms:1. a robot (robot-1 ) equipped with a laser ranging sensor, an odometry sensors,and a navigation component plan.2. a robot (robot-2 ) equipped with a 3D ranging camera, an odometry sensors,a navigation and a tracking component plan.3. a server (server) with an installation of a simultaneous localization andmapping (SLAM) component plan.Scenario: In order to drive the robots, both navigation component plansmust rely on odometry and localization information (their service goal requirement).However, none of the robot platforms have enough computational powerto run a localization component plan locally. Fortunately, an instance of theSLAM component plan - running on the server - can provide location updates,as long as it receives data from both one ranging sensor and from the odometryof the robot. In addition, robot-2 can use its 3D ranging camera to observe the80
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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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Page 41: deliberative behavior in BDI archit
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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
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Page 60 and 61: EnvironmentHardwareLocal SoftwareC+
Page 62 and 63: a cognitive layer can connect as a
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Page 66 and 67: 4 ExperimentTo evaluate the feasibi
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Page 70 and 71: A Programming Framework for Multi-A
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Page 85 and 86: ACKNOWLEDGMENTThis work has been su
Page 87 and 88: The code was analysed both objectiv
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Page 93 and 94: Table 1. Core Agent ProtocolsAgent
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Page 99 and 100: principal reasons. Firstly, it is a
Page 101 and 102: 2. Muldoon, C., O’Hare, G.M.P., C
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Page 105 and 106: DevelopmentProductionHuman Readabil
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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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