Here - Agents Lab - University of Nottingham

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Although some success [13] [14] have been made in the literature for solvingthe joint action and configuration planning problems at a global level for heterogeneousrobotic ecologies or to enable their purely reactive configuration [15], wepropose to distribute certain aspects of these tasks over a number of agents withvarying responsibilities and functionalities in order to lead to better scalability,robustness and fault tolerance.Agent and Multi Agent Systems (MASs) are regarded as a general-purposeparadigm to facilitate the co-ordination of complex systems built in terms ofloosely-coupled, situated, autonomous and social components (the agents). Inparticular, the Belief Desire Intention (BDI) agent model, used in the Self -OSGiframework discussed in the following section, provides a simple but extensiblemodel of agency that explicitly addresses the production of rational and autonomousbehaviour by agents with limited computational resources.3 Self -OSGiSelf -OSGi [3] [4] is a BDI agent framework built on OSGi Java technology andpurposefully designed to support the type of collaboration envisaged within theubiquitous and embedded systems targeted by RUBICON.The component & service orientation used in the design of Self -OSGi is anhighly popular, mainstream approach used to build modular software systems.Component & service frameworks operate by posing clear boundaries (in termsof provided and required service interfaces) between software components andby guiding the developers in re-using and assembling these components intoapplications. Self -OSGi addresses the lack of common adaptation mechanismsin these frameworks by leveraging their previously unexploited similarities withthe BDI agent model.3.1 OSGiOSGi specification [5] is currently the most widely adopted technology for buildingmodular control systems for networked home applications, with many implementationstargeting computationally constrained platforms. Within the AALdomain, OSGi-based middleware have long been used to provide the technicalbasis for integrating network devices and services, e.g. in EU projects such asAmigo, OASIS, SOPRANO, and their recent consolidation in the UniversAALplatform.OSGi defines a standardised component model and a lightweight containerframework, built above the JVM, which is used as a shared platform for networkprovisionedservices and components specified through Java interfaces and Javaclasses. Each OSGi platform facilitates the dynamic installation and managementof units of deployment, called bundles, by acting as a host environmentwhereby various applications can be executed and managed in a secured andmodularised environment. An OSGi bundle is packaged in a Jar file and organisesthe frameworks internal state and manages its core functionalities. These73
include both container and life cycle operations to install, start, stop and removecomponents as well as checking dependencies.The separation between services and their actual implementations is the keyto enable system adaptation. With OSGi, in addition to syntactic matching ofservice interfaces, developers can also associate lists of name/value attributes todescribe the semantic of each service, and use the LDAP filter syntax to searchthe services that match given search criteria. Furthermore, Declarative Services(DS) for OSGi offers a declarative model for managing multiple componentswithin each bundle and also for automatically publishing, finding and bindingtheir required/provided services, based on XML component definitions. However,DS only matches pre-defined filters with pre-defined services attributes of alreadyactive components, but does not consider the automatic instantiation of newcomponents, the context-sensitive selection of their services, or the automaticrecovery from their failure - all necessary features for the construction of contextaware,adaptive systems.3.2 Component & Service based Agent ModelSelf -OSGi addresses the issues outlined in the previous section by translating theBDI agent model [16] into general component & service concepts. In particular,the separation between the services interface and the services implementation isthe basis for implementing both the declarative and the procedural componentsof BDI-like agents, and also for handling dynamic environments, by replicatingtheir ability to search for alternative applicable plans when a goal is first postedor when a previously attempted plan has failed.Belief Model: As in the Jadex agent language [17], Self -OSGi represents beliefsas simple Java objects. Compared to agent toolkits where beliefs are stored aslogic predicates, objects have the advantage of providing a strong typed definitionof agent’s beliefs. In addition, within Self -OSGi, a belief set is implemented asa Belief Set component with clearly defined interfaces, which are used to accessany data that may affect the value of its beliefs.Service Goal Model: Goals, describing the desires that the agent may possiblyintend, are represented in Self -OSGi by the (Java) interfaces of the services thatmay be used to achieve them, or service goals.Service goals may represent either: (i) performative sub-goals defining thedesired conditions to bring about in the world and/or in the systems state - forinstance, the method ”(void) beAt(X, Y)” in the goal service GoalNavigationmay be used by a robotic agent to represent the goal of being at a given location- and (ii) knowledge sub-goals subtending the exchange of information. Forinstance, the method ”Image getImage()” in the GoalImage service goal maybe used to express the goal of retrieving the last video frame captured by onecamera. In addition, service goals attributes may be used to further characteriseeach service goal, e.g. the characteristic of the information requested/granted,74
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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
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 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 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
Page 111 and 112: nents themselves. However, since th
Page 113 and 114: optimized for this format feature s
Page 115 and 116: Java serialization and Jadex Binary
Page 117 and 118: 10. P. Hoffman and F. Yergeau, “U
Page 119 and 120: Caching the results of previous que
Page 121 and 122: querying an agent’s beliefs and g
Page 123 and 124: 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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