Here - Agents Lab - University of Nottingham

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It has been argued that building robot systems for environments in whichrobots need to co-exist and cooperate with humans requires taking a cognitivestance [12] . According to [12], translating the key issues that such robot systemshave to deal with requires a cognitive robot control architecture. Takinga cognitive stance towards the design and implementation of a robot systemmeans that such a system needs to be designed to perform a range of cognitivefunctions. Various cognitive architectures, such as ACT-R [13] and SOAR [14],have been used to control robots. These architectures were not primarily aimedat addressing the robot control problem and in this sense are similar to agentprogramming languages, the technology that we advocate here for controllingrobots. SOAR has been used to control the hexapod HexCrawler and a wheeledrobot called the SuperDroid [3]. ADAPT (Adaptive Dynamics and Active Perceptionfor Thought) is a cognitive architecture based on SOAR that is specificallydesigned for robotics [15]. The SS-RICS (Symbolic and Sub-symbolic Robotic IntelligentControl System) architecture for controlling robots is based on ACT-R;SS-RICS is intended to be a theory of robotic cognition based on human cognition[16, 2]. Unlike [17], we are not mainly concerned here with the long-termgoal of developing robotic systems that have the full range of cognitive abilitiesof humans based on a cognitive architecture such as SOAR. Our work is orientedtowards providing a more pragmatic solution for the robot control problem asdiscussed above. Although our work may contribute to the larger goal that [17]sets (as there are quite a few similarities between BDI-based agents and cognitivearchitectures such as ACT-R and SOAR), we do not address this question.3 Cognitive Robot Control ArchitectureThis section introduces our cognitive robot control architecture. We discuss severalissues including the processing and mapping of sensory data to a symbolicrepresentation, the translation of high-level decisions into low-level motor controlcommands, and the interaction of the various architecture components.3.1 Overall Design of the ArchitectureFigure 1 shows the main components of our architecture, including a symbolic,cognitive layer (Goal), a middle layer for controlling robot behavior (written inC++), and a hardware control layer (using URBI 1 , a robotic programming language).The Environment Interface Standard (EIS) layer provides the technologywe have used to manage the interaction between the symbolic and sub-symboliclayers. EIS provides a tool to deal with the issue that sub-symbolic sensory datais typically noisy, incomplete, quantitative measurements, whereas the symboliclayers need the symbolic representation that supports logical reasoning.The main functions for controlling a robot are placed in the behavioural controllayer. In addition to the functions such as (object) recognition, navigation,1 http://www.urbiforge.org/57
localization, path planning and other common functions, this layer is also responsiblefor communicating with the higher-level symbolic components, includingthe interpretation of symbolic messages that represent actions and making therobot perform these actions in its physical environment.Cognitive Control (GOAL)KnowledgeInformationEnvironment Interface (Java)TCP/IPRobotic Behavioural Control (C++)Symbolic LayersSub-symbolic LayersSensory DataTCP/IPUrbi Server ( Urbiscript )Robot PlatformEnvironmentFig. 1. The overall design of the architectureThe EIS layer acts as a bridge between the behavioural and cognitive controllayers. Because these layers use different languages for representing sub-symbolicand symbolic information, respectively, we need an interface to translate betweenthese representations. The cognitive control layer acts as a task manager for therobot and provides support for managing the robot’s mental state which allowsthe robot to keep track of what is happening while executing a task.3.2 System Architecture and ComponentsA robot control architecture provides an organizational structure for softwarecomponents that control a robotic system [18]; such architectures are specializedbecause of the unique requirements that embedded systems such as robots imposeon software. Here we discuss the detailed architecture presented in Figure 2and also discuss some of the implementation details.Robot Platform The architecture has been implemented on the humanoid Naorobot platform from Aldebaran Robotics 2 . We use the URBI [19] middlewarethat provides the urbiscript language for interfacing with the robot’s hardware.We have chosen URBI instead of the Robot Operating System (ROS 3 ) platform2 http://www.aldebaran-robotics.com/3 http://www.ros.org/58
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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
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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 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 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
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Page 105 and 106: DevelopmentProductionHuman Readabil
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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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