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I Web Intelligence and Intelligent Agents Web Intelligence and Intelligent Agents Edited by Zeeshan-ul-hassan Usmani, Ph.D. In-Tech intechweb.org Published by In-Teh In-Teh Olajnica 19/2, 32000 Vukovar, Croatia Abstracting and non-prot use of the material is permitted with credit to the source. Statements and opinions expressed in the chapters are these of the individual contributors and not necessarily those of the editors or publisher. No responsibility is accepted for the accuracy of information contained in the published articles. Publisher assumes no responsibility liability for any damage or injury to persons or property arising out of the use of any materials, instructions, methods or ideas contained inside. After this work has been published by the In-Teh, authors have the right to republish it, in whole or part, in any publication of which they are an author or editor, and the make other personal use of the work. © 2010 In-teh www.intechweb.org Additional copies can be obtained from: publication@intechweb.org First published March 2010 Printed in India Technical Editor: Maja Jakobovic Cover designed by Dino Smrekar Web Intelligence and Intelligent Agents, Edited by Zeeshan-ul-hassan Usmani, Ph.D. p. cm. ISBN 978-953-7619-85-5 V Preface Entitites or computer programs that learn from their environment and can act based on what they have learned can be dened as intelligent agents. These agents can be as simple as triggering an alarm in case of a re or as complex as human beings. Intelligent agents and their applications to solve real-world problems are getting smarter and diversied day by day. Whether it is an autonomous intelligent agent working for ambient intelligence, or a rational agent mining the trends of a stock market, a bot to negotiate an online bid, or a virtual customer to buy books for you, one can see the applications and use of intelligent agents everywhere. This age of information overload and ever-growing contents creation on world-wide-web with millions of pages per day presents some unique problems such as real-time recommendations, data mining, abstracting useful information, and search optimization based on ones’ unique prole etc. Intelligent agents with their ability to work with humongous amount of data - usually fed by social networks and services like twitter and blogs -, scalability, robustness, and capability to learn from the environment makes them a promising candidate to solve these problems. This book presents a unique and diversied collection of research work ranging from controlling the activities in virtual world to optimization of productivity in games, from collaborative recommendations to populate an open computational environment with autonomous hypothetical reasoning, and from dynamic health portal to measuring information quality, correctness, and readability from the web. There are several interesting chapters that discuss bio-inspired nano-agents architecture, the role of intelligent agents in intuitive search, , activity recognition, communications of humanoids, negotiation, sense of humor, object-oriented semantics, data clustering and compression, trust management, and brain informatics to name a few. Readers will also nd some novel applications such as using intelligent agents to control disruption in airline operations control and to save lives by modeling real-life suicide bombing events in advance to predict the carnage. VI We hope that you will enjoy reading this diverse collection of research and the book will attract an interest of researchers from various disciplines to harness the power of intelligent agents to solve the contemporary problems intelligent web has to offer. We welcome your suggestions to improve our work! Zeeshan-ul-hassan Usmani, Ph.D. Assistant Professor, Faculty of Computer Science and Engineering Ghulam Ishaq Khan Institute (GIKI), Topi - 23640, Pakistan zusmani@giki.edu.pk VII Contents Preface V 1. ABio-inspiredNano-AgentArchitectureforIntelligentAgents 001 Jean-ClaudeHeudin 2. ControllingandAssistingActivitiesinSocialVirtualWorlds 013 I.Rodriguez,A.PuigandM.Esteva 3. ADynamicHealthcarePortalDesignandEnhancements 027 Yung-ChingWeng,Sheau-LingHsieh,Kai-PingHsuandChi-HuangChen, 4. Universics:CommonFormalizationFrameworkforBrainI nformaticsandSemanticWeb 055 IoachimDrugus 5. Autonomoushypotheticalreasoning:thecaseforopen-mindedagents 079 AspassiaDaskalopuluandGeorgiosK.Giannikis 6. DisruptionManagementinAirlineOperationsControl– AnIntelligentAgent-BasedApproach 107 AntónioJ.M.CastroandEugénioOliveira 7. DocumentCompressionImprovementsBasedonDataClustering 133 JiříDvorský,JanMartinovič,JanPlatošandVáclavSnášel 8. EmbodimentofanAgentusingAnthropomorphizationofanObject 155 HirotakaOsawaandMichitaImai 9. TowardsSocializedMachines:EmotionsandSenseofHumour inConversationalAgents 173 MichalPtaszynski,PawelDybala,ShinsukeHiguhi,WenhanShi, RafalRzepkaandKenjiAraki 10. TrustandReputationManagementinWeb-basedSocialNetwork 207 TouhidBhuiyanandAudunJøsang 11. Similarity-basedTechniquesforTrustManagement 233 MozhganTavakolifard VIII 12. AnInformationFilterforIntuitiveandSimpleSearch 251 SayakaAkioka,HideoFukumoriandYoichiMuraoka 13. Skipping-BasedCollaborativeRecommendationsinspired fromStatisticalLanguageModeling 263 GeoffrayBonnin,ArmelleBrunandAnneBoyer 14. HumanComputationGamesandOptimizationofTheirProductivity 289 Kuan-TaChen,Chien-WeiLin,Ling-JyhChenandIrwinKing 15. WebIntelligencefortheAssessmentofInformationQuality: Credibility,Correctness,andReadability 305 JohanF.HoornandTeunisD.vanWijngaarden 16. OverviewoftheRelationalAnalysisapproachinData-Mining andMulti-criteriaDecisionMaking 325 JulienAh-PineandJean-FrançoisMarcotorchino 17. Representative-basedProtocolforMultipleInterdependent IssueNegotiationProblems 347 KatsuhideFujita,TakayukiItoandMarkKlein 18. Object-orientedSemanticandSensoryKnowledgeExtraction fromtheWeb 365 ShunHattori 19. PinpointClusteringofWebPagesandMiningImplicitCrossoverConcepts 391 MakotoHARAGUCHIandYoshiakiOKUBO 20. IntelligentAgentsforAutomaticServiceComposition inAmbientIntelligence 411 MariaJ.Santomia,FranciscoMoya,FelixJ.Villanueva, DavidVillaandJuanC.Lopez 21. Exploringthebeehivemetaphorasamodelforproblemsolving: search,optimisation,andmore 429 PavolNavrat,AnnaBouEzzeddine,LuciaJastrzembskaandTomasJelinek 22. Combiningpervasivecomputingwithactivityrecognitionandlearning 447 PatriceC.Roy,BrunoBouchard,AbdenourBouzouaneandSylvainGiroux 23. IntelligentAgentsinExtremeConditions–Modeling andSimulationofSuicideBombingforRiskAssessment 463 Zeeshan-ul-hassanUsmani,FawziAlghamdiandDanielKirk ABio-inspiredNano-AgentArchitectureforIntelligentAgents 1 ABio-inspiredNano-AgentArchitectureforIntelligentAgents Jean-ClaudeHeudin X A Bio-inspired Nano-Agent Architecture for Intelligent Agents Jean-Claude Heudin Interactive Media Lab. – IIM Léonard de Vinci France 1. Introduction Intelligent artificial creatures cover a large range of applications in various domains. Recent advances in intelligent agent technologies make now possible to develop a growing number of real-world applications. However, these applications require a new generation of open software architectures that combines such technologies with lightweight design and portability. This chapter describes a new nano-agent architecture designed for intelligent artificial creatures. This software environment takes advantages of our past experiences in distributed artificial intelligence with the Knowledge-based Operating System (Heudin et al., 1986), real-time multi-expert applications such as the Electronic Copilot project for combat aircrafts (Gilles et al., 1991), and the more recent Evolutionary Virtual Agent (EVA) applications (Heudin, 2004). In section 2 and 3 of this chapter, we introduce the nano-agent bio-inspired architecture and its programming language called nanoScheme. Section 4 describes an application example developed using this software environment: an online self-animated character that interacts using natural language and emotional expressions. This virtual character is based on a “schizophrenic” model in which the character has multiple distinct personalities, each with its own pattern of perceiving and interacting with the user. In section 5, the qualitative efficiency of this prototype is then compared with the ALICE conversational engine (Wallace, 2002). The chapter concludes by outlining future developments and possible applications. 2. The EVA approach Since the first conversational agent Eliza (Weizenbaum, 1966), there have been a large number of studies for designing intelligent agents that could dialog in a very natural way with human users. A major part of this research focused on dedicated aspects of the problem such as natural language interaction, non-verbal communication, emotional expressions, self-animated characters, etc., but very few projects integrates all requirements (Franklin & Graesser, 1997). The ideal intelligent agent must be an autonomous character that responds to human interaction in real-time with appropriate behaviors, not predetermined, broad in content, highly contextual, communicative, and behaviorally subtle 1 WebIntelligenceandIntelligentAgents2 (Badler, 2002). The character must also appear to think, make decision, and act of its own volition (Thomas & Johnston, 1981). Simulating these sophisticated properties of the human brain is a challenging goal. We argue that they are global properties which emerge from the very large number of non- linear interactions that occur within the brain architecture. The problem of simulating these emergent behaviors cannot be solved by using a classical reductionist approach. Therefore, in order to create a believable intelligent agent, we propose to use an approach that has given some successes for the study of complex systems (Heudin, 2007). The first phase of this approach is a top-down analysis that defines complexity levels and their related components. The second phase is a bottom-up multi-agent simulation that attempts to capture the behavioral essence of the complex phenomena. The idea is that the complex properties that cannot be simulated using a classical model will be likely to emerge from the interactions between the agents. If defined and organized correctly, the resulting system should exhibit the appropriate dynamical behaviors. The ideal tool for this approach is a multi-agent system which enables to implement as many agents as needed with the following constraints (Langton, 1989): 1. The complex system is modeled as a dynamical network of agents. 2. Each agent details the way in which it reacts to local situation and interactions with other agents. 3. There is no agent that directs all the other agents. 4. Any behavior or global pattern is therefore emergent. Such a multi-agent system must also take advantage of a distributed environment, exploiting hierarchy and concurrency to perform large-scale simulations. All these features were the initial requirements for designing the new Evolutionary Virtual Agent (EVA) architecture. 3. EVA Architecture Overview 3.1 Nano-Agent Architecture In order to meet these requirements, we have designed a bio-inspired multi-agent architecture that does not try to simulate a specific organism but rather integrates several artificial life features in order to implement machine life and intelligence. A typical application consists one or more “nano-agents”, and possibly up to a large number if necessary as in natural swarms. We call them “nano” because of their small size and resource requirement compared to most existing software environments. An application can be composed of several “execution environments” running on a computer or on a network of computers. Each of these environments includes a set of nano-agents and a nano-server which diffuses messages locally. In other words, when a nano-agent diffuses a message, all nano-agents in the local execution environment receive the message. In addition, any nano- agent can send a message to another distant execution environment. Fig. 1. The nano-agent architecture principle. In the current implementation, the core technology is implemented in Java and its weight is less than 25 Kilo-bytes. Most applications require a small set of knowledge-base and behavioral scripts text files, thus resulting in lightweight applications that are also well- suited for web-based, mobile phone, robots and embedded environments. 3.2 The Nanocheme Language The behavior of each nano-agent is programmed using a user-friendly language, called nanoScheme, based on the Scheme programming language. It includes a reduced set of primitives which is a subset of the R4RS specification (Clinger & Rees 1991). This subset includes the following functions: Basic calculus: + - * / = < > <= >= Mathematics: cos sin acos asin log expt round Predicates: number? integer? even? string? symbol? string=? eqv? pair? null? procedure? Strings and symbols: string->number number->string string->symbol symbol->string substring string-length string-append List processing: cons car cdr set-car! set-cdr! Control and evaluation: quote eval apply load define lambda set! begin if Most of the missing features of the Scheme specification could be added by programming them directly in nanoScheme. This provides the application developer a high-level interactive language which is embedded in each nano-agent. Here is an example of the implementation of the R4RS function that returns the length of a list: ( define ( length x ) ( if ( null? x ) 0 ( + 1 ( length ( cdr x ) ) ) ) ) [...]... Natura Language Communication between Man and Machine, Communication ACM, No 9, 36-45 12 Web Intelligence and Intelligent Agents Controlling and Assisting Activities in Social Virtual Worlds 13 2 X Controlling and Assisting Activities in Social Virtual Worlds I Rodriguez, A Puig and M Esteva Applied Mathematics Department University of Barcelona, Spain Artificial Intelligence Research Institute Barcelona,... establishes the valid interactions participants may have and the consequences of those interactions Our main objectives are: Establish participants' roles, activities and norms by means of a multiagent system named electronic institution Participants can be both software agents and humans 14 Web Intelligence and Intelligent Agents Use of intelligent virtual objects with an external module named... Kallmann M and Thalmman D (1998) Modeling objects for interaction tasks In Proc Eurographics Workshop on Animation and Simulation, pages 73–86 26 Web Intelligence and Intelligent Agents Kallmann M., Monzani J., Caicedo A., and Thalmann D (2000), A common environment for simulating virtual human agents in real time, in Proc.Workshop on Achieving HumanLike Behavior in Interactive Animated Agents Kallmann... with the user while searching on the web Another problem is that the interaction case reported here is too short and simple to let all the personalities express themselves in the flow of conversation 10 Web Intelligence and Intelligent Agents 6 Conclusion and Future Works EVA is a long term open project for designing artificial creatures There are many possible and promising research directions for... of an iObjectDoor in Wonderland On the left side, Client 2 sees glazed red door because he has permission to see the next room but not to pass through it On the right side, Client 3 has both permission to see and to pass through it 24 Web Intelligence and Intelligent Agents Fig 6 Snapshots showing multi-view scheme Client 2 and client 3 views of the iObjectdoor on left and right pictures, respectively... are sorted according to their score ; (c) and the highest one is selected Format: (a) all sentences in the selected URL file are scored according to the keywords and the structure of the phrase ; (b) the highest scored sentence is formatted and used as the output 8 Web Intelligence and Intelligent Agents 4.5 Graphical Interface The prototype included also three agents for implementing the graphical interface... context and use this information to decide what actions to do Figure 2 distinguishes between iObjects at scene/institution level and participant level The first one correspond to the iObjects belonging to the scene infrastructure (e.g noticeboard) or institution infrastructure (i.e door) Figure 3 shows a notice board iObject showing 20 Web Intelligence and Intelligent Agents information about good and. .. world (from Sun Microsystems) and presents some simulation results 22 Web Intelligence and Intelligent Agents Fig 4 Generic approach to enforce norms in a SVW As can be appreciated in Figure 4, an interaction with an iObject is captured in the virtual world client and it is sent, using a socket message, to the iObjects manager The message indicates client identifier, object and event used to interact... Mapping of the main moods in the PAD space 4.4 Memory and Web Mining They are two additional groups of nano -agents for implementing memory and web mining functionalities The memory group is responsible for storing and retrieving information when needed It stores all interactions with the user in “log files” and an indexer periodically parses these files and extracts keywords A dedicated plugin package adds... virtual worlds and multiagent systems combined with virtual environments Section 3 describes how our system models activities taking place in normative virtual worlds and uses intelligent objects to guide and control the user during the activities Section 4 presents the developed intelligent objects framework and finally section 5 presents conclusions and future work 2 Related work 2.1 Norms in web based . I Web Intelligence and Intelligent Agents Web Intelligence and Intelligent Agents Edited by Zeeshan-ul-hassan Usmani, Ph.D. In-Tech intechweb.org Published by In-Teh In-Teh Olajnica. contextual, communicative, and behaviorally subtle 1 Web Intelligence and Intelligent Agents2 (Badler, 2002). The character must also appear to think, make decision, and act of its own volition. according to the keywords and the structure of the phrase ; (b) the highest scored sentence is formatted and used as the output. Web Intelligence and Intelligent Agents8 4.5 Graphical Interface
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