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Comparing objects and agents is a way of providing a better understanding of the concept ofan agent. Objects and agents present similarities: they both have behavioural capabilities,encapsulate an internal state and use message passing for communicating. Thus Shohamintroduced in [24] the notion of Agent-Oriented Programming as a specialisation of Object-Oriented Programming with specific constraints. From this perspective, Bradshaw [25]characterizes an agent as “an object with an attitude”. In other words an agent, as anabstraction mechanism, is an object with additional capabilities [26, 27].First, the state of an object is just a set of attributes, while many agents have a moresophisticated structure. This is because of the requirements of the behaviour of an agent. Forexample, Shoham describes the state of an agent, called its mental state, as a composition of“mental components” such as beliefs, capabilities, choices, and commitments. Also, asuccessful kind of agent is built on the Belief, Desire and Intention (BDI) model, whichseparates between the information, motivational and deliberative states [28].Then, unlike objects that receive indisputable orders through method invocation, agents onlyreceive requests. It means that agents can actually decide what to execute, which may implyto ignore some requests. This property of control over the behaviour is a consequence of whatOdell [27] calls unpredictable autonomy. It is summarized by the sentence “Agents can say“no”.” The behaviour of an agent may not be predictable externally since it depends on theencapsulated states and goals of the agent.Besides, an object is passive by default while an agent has to be active for pursuing its goals.This is what Odell calls dynamic autonomy. It is summarized by the sentence “Agents can say“go”.” It can be characterized in degrees, from simple reactivity to an entirely proactivebehaviour. A consequence is that an agent has its own thread of control. Thus the notion ofactive objects is probably the closest to agents in the object-oriented world. For example,Huhns and Stephens state in [29] that “Fundamentally, an agent is an active object with theability to reason, perceive and act.” Therefore, several works have illustrated ways ofimplementing agents from active objects [30, 31].Lastly, the relationships between agents are more complex than in the case of objects.Because of both sorts of autonomy, agent communication involves event notification and isgenerally asynchronous. This involves parallel processing. In addition, the effectivecollaboration of different agents requires a good organization and brings the need for a socialdimension within multi-agent systems.2.2 Multi-agent systems (MASs)2.2.1 OverviewCommunicative agents are able to cooperate in order to satisfy their respective goals. A set ofinteracting agents is called a multi-agent system or agent-based system (groups of agents arealso called communities or societies of agents). For the AI community, MASs are regarded asan interesting and still promising (although not new) approach for Distributed ArtificialIntelligence (DAI) [32].Sycara characterizes MASs as follows [33]:MSc EMOOSE Thesis – http://www.emn.fr/EMOOSE 13
(1) each agent has incomplete information or capabilities for solving the problem and,thus, has a limited viewpoint;(2) there is no system global control;(3) data are decentralized;(4) computation is asynchronous.Despite their autonomy (2)(4), the agents take part of the achievement of the overall goals ofthe system by working on the realization of their simple goals with their limited data andcapabilities (1)(3).Considering all the characteristics described above, it is apparent that MASs are a veryparticular type of systems that are more suitable to certain types of problems or environments[33, 34].MASs are particularly suited for conceiving complex systems. The abstraction and modularitythey provide leads to the conceptualization of a system as “a society of cooperatingautonomous problem solvers” [34]. Such a partition of the problem helps reduce complexity.Then, such a metaphor fits well to some kinds of problems involving naturally autonomousentities. Examples include air-traffic control, manufacturing systems or virtual characters incomputer games (see Applications).Because of their decentralized nature, MASs are also a good mean for solving problems thatinvolve distribution, in the general sense: either distribution of data, control, expertise orresources [34]. In particular, MASs can be used for modeling real-world entities with theirown expertise and resources that need interact between them. In the case of distributed data,agents are a mean for making computation at the data sources, thus reducing (possibly distant)communication to exchanges of already-computed high-level information.Furthermore, MASs are a good mean for implementing open systems. An open system is asystem whose components can change dynamically and be highly heterogeneous, for instancein terms of authors or implementation languages and techniques. A typical example of anopen system with heterogeneous components is the World Wide Web. Heterogeneity is not aproblem since agents present strong encapsulation. The dynamic alteration of the compositionof a MAS is made possible through certain types of MAS organizations like the Facilitator.As an example, the system described in [35] supports the dynamic addition and retraction ofservices by the mean of agents that register/unregister to the system.Finally, MASs present the advantage of allowing for the interoperation of legacy systems.Again, because agents naturally present a strong encapsulation, a legacy system can be turnedinto an agent. Strategies for such a transformation include the use of a wrapper or a transducer[36]. Modifying legacy systems can be very expensive. Integrating them into a MAS is a wayof making them able to collaborate with new systems without having to modify them.2.2.2 Agent communicationWhile partitioning a system into agents helps reduce its complexity, complexity may thenarise in the relationships between agents. Agent communication is at the heart of MASs,whether agents collaborate or compete, whether they communicate between them or with non-MSc EMOOSE Thesis – http://www.emn.fr/EMOOSE 14
Page 1: Vrije Universiteit Brussel - Belgiu
Page 4 and 5: ContentsAbstract 1Acknowledgements
Page 6 and 7: (4) Table UP_SUP (matches User Prof
Page 9 and 10: content providers’ interpretation
Page 11 and 12: - Active profiling consists in aski
Page 13: 2 Agents2.1 Overview2.1.1 Definitio
Page 17 and 18: MASs need an organization for speci
Page 19 and 20: However, the usefulness of personal
Page 21 and 22: they often provide implementation o
Page 23 and 24: 2.5 Design2.5.1 The DIMA designIn [
Page 25 and 26: 3 MetadataDuring the earlier invest
Page 27 and 28: This requires some agreement and st
Page 29 and 30: 4 PerspectivesRecommender systems m
Page 31 and 32: way Lego blocks should be assembled
Page 33 and 34: usiness intelligence is generated.
Page 35 and 36: important than memory space optimiz
Page 37 and 38: The graph is then formatted to be v
Page 39 and 40: Chapter 4: Implementation1 Implemen
Page 41 and 42: (1) A Name Server (NS) maps every a
Page 43 and 44: the agent terminates, the thread is
Page 45 and 46: 4 Application level4.1 OverviewThe
Page 47 and 48: 4.3 User Agent MakerThis agent has
Page 49 and 50: Thus the profile, node and link cla
Page 51 and 52: BIProviderR : Provide_Business_Inte
Page 53 and 54: to enable users to explicitly speci
Page 55 and 56: ConclusionA “minimalist” approa
Page 57 and 58: Appendix A: Addendum onIndustrial I
Page 59 and 60: Appendix B: SQL Tables Design1 Tabl
Page 61 and 62: Appendix C: Conference PaperA paper
Page 63 and 64: very large repositories of learning
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popularity of the link (the bigger
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Appendix D: Source Code1 Agent infr
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public static bool ProcessIsMainHos
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string path = MakePath(".", Middlew
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System.Messaging.MessageQueue queue
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public static NameServer GetNS(){if
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}}}table[agentId] = address;}}/// /
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****** NON-STATIC PART ******// Tab
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}}}return (AgentId) first;using Sys
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{}return Subject.GetHashCode() + Na
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name in order to allow for the retr
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}// Start agent's main Thread.Threa
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}DirectoryFacilitator.GetDF().Unreg
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{/// /// Allows for specific conver
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}Monitor.Exit(aLock);public overrid
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Allows for synchronized Message exc
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esource./// /// public string GetMa
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}}private void RunTerminationExamin
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}private Message RequestUACreation(
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}msgQueue.AddFilters(new MessageCat
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Message received: interrupt preterm
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19. Class Architecture.DBManagerAg
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}}connection.Close();/// /// For sa
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using System.Collections;namespace
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namespace Architecture.Profiles{usi
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private UP(DataRow row) : this(newU
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}DataRow[] rows = Row.GetChildRows(
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}adapter.Update(Data);//***********
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internal static readonly string LAS
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}}Row[OCCURRENCES] = Occurrences +
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}get{}return Data.Rows.Find(_name);
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private static SqlDataAdapter adapt
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}foreach (ILink link in links){if (
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}}}private DataRow Row{get{return D
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18. N. Good, et al. Combining colla
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49. D. Derbyshire, et al., Agent-Ba
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83. Council for Higher Education Ac
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