Here - Agents Lab - University of Nottingham

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EnvironmentHardwareLocal SoftwareC++ Application Java Application GOAL AgentWireless Connection[GOAL Agent ]Reasoning,Decision Making,Action SelectionPerceptionActionBelief MaintainingReasoningDecision MakingKnowledgeGoals[Java Environment Interface Layer]Bridge of Communication,Environment ManagementEnvironmentInterfaceEnvironmentManagementEnvironmentModePerceptionAction[C++ Robotic Behavior Layer ]Information Processing,Knowledge Processing,Action ExecutionEnvironmentConfiguratorEvents ConfigurationObjects ConfigurationCommunicationKnowledge ProcessingPerception DeliverInformation ProcessorObjects RecognitionFeatures ExtractionFeatures MarchingInformation FusionFeatures ConfigurationMemoryGlobal MemoryNavigationLocalization Path PlanningOdometry Grid MapAction ReceiverAction ExecutionBehaviorsMotionsDebug MonitorSensor Information GUICommunication GUIManual Control GUICamera Calibration GUIWorking MemoryGrid Map GUIMessages InterpreterSensing / Sensors Acting / ActuatorsRobot Hardware PlatformUrbi Server (Urbiscript Execution Cycle)Robot PlatformEnvironmentEnvironmentOther RobotsFig. 2. Overview of the agent-based cognitive robot architecturebecause it does not include the orchestration layers present in URBI that providesupport for parallel, tag-based, and event-driven programming of behaviourscripts. When the architecture is executed, an urbiscript program, which initializesall API parameters of sensors and motors (e.g., the resolution and framerate of camera images) is executed to configure the robot platform.Behavioural Control The behavioural control layer is written in C++, connectingthe robot hardware layer with higher layers via a TCP/IP connection. This layeris mainly responsible for information processing, knowledge processing, communicationwith the deliberative reasoning layer and external robots, and actionand behaviour executions. All of these components can operate concurrently.The main functional modules involve:– Sensing, for processing sensory data and receiving messages from otherrobots. Sensors that have been included are sonar, camera, microphone, aninertial sensor, and all sensors monitoring motors. Because the memory spacerequired for camera images is significantly bigger than that for other sensors,the transmission of images is realized via a separate communication channel.59
– Memory, for maintaining the global memory and working memory for thebehavioural layer. In global memory, a map of the environment and propertiesof objects or features extracted from sensor data are stored. We haveused a 2D grid map for representing the environment. This map also keepstrack which of the grid cells are occupied and which are available for pathplanning. The working memory stores temporary sensor data (e.g., images,sonar values) that are used for updating the global memory.– Information Processor, for image processing. This component providessupport for object recognition, feature extraction and matching, as well asfor information fusion. Information fusion is used to generate more reliableinformation from the data received from different sensors. We have usedthe OpenCV [20] library to implement algorithms and methods for processingimages captured by the camera. Algorithms such as Harris-SIFT [21],RANSAC [22], and SVM [23] for object recognition and scene classificationhave been integrated in this module.– Environment Configurator, for interpreting and classifying events thatoccur in the environment. For example, when the output of the left sonarexceeds a certain value, this module may send a corresponding event messagethat has been pre-configured. This is useful in case such readings have specialmeaning in a domain. In such cases, an event message may be used to indicatewhat is happening and which objects and features such as human faces andpictures have been detected.– Navigation includes support for Localization, Odometry, Path Planningand Mapping components, which aid robots in locating themselves and inplanning an optimal path from the robot’s current position to destinationplaces in a dynamic, real-time environment. Once a robot receives a navigationmessage from the cognitive layer with an instruction to walk towardsthe destination in the 2D grid-map space, the Path Planning component calculatesan optimal path. After each walking step, Odometry will try to keeptrack of the robot’s current position, providing the real-time coordinates ofthe robot for planning the next walking step. Due to the joint backlash andfoot slippage, it is impossible to accurately estimate the robot’s position justby means of odometry. For this reason, the robot has been equipped with thecapability to actively re-localize or correct its position by means of predefinedlandmarks. Additionally, navigation requires back-and-forth transformationbetween coordinate systems. The odometry sensors, for example, provide therobot’s coordinates in a World Coordinate System (WCS) that needs to bemapped to the robot’s position in 2D Grid-map Coordinate System (GCS)for path planning. However, when executing walking steps, the position inGCS has to be transformed into the Local Coordinate System (LCS) thatthe robot uses.– Communication, for delivering perception messages to the EIS componentand receiving action messages from the EIS. The communication componentis constructed based on a Server/Client structure and uses the TCP/IPprotocol. The behaviour layer in a robot starts a unique server to which60
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Proceedings of the Tenth Internatio
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OrganisationOrganising CommitteeMeh
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Table of ContentseJason: an impleme
Page 10 and 11: in Sect. 3 the translation of the J
Page 12 and 13: init_count(0).max_count(2000).(a)(b
Page 14 and 15: 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
Page 44 and 45: layer modules (i.e. nodes) can be d
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 62 and 63: a cognitive layer can connect as a
Page 64 and 65: can reliably be differentiated and
Page 66 and 67: 4 ExperimentTo evaluate the feasibi
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
Page 107 and 108: will then evaluate this new format
Page 109 and 110: 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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