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  • 51. El-Wazer, M.
    et al.
    Zaslavsky, Arkady
    Multi-protocol gateway approach to packet-switched wireless data networks interconnection1997Inngår i: Proceedings: APCC' 97, the Third Asia-Pacific Conference on Communications : Sydney, Australia, 7 - 10 December 1997, Sydney Convention and Exhibition Centre, Darling Harbour, 1997Konferansepaper (Fagfellevurdert)
  • 52. Faiz, M.
    et al.
    Zaslavsky, Arkady
    Database replica management strategies in multidatabase systems with mobile hosts1996Inngår i: Database Reengineering and Interoperability, Berlin: Encyclopedia of Global Archaeology/Springer Verlag, 1996, s. 323-336Kapittel i bok, del av antologi (Annet vitenskapelig)
  • 53. Faiz, M.
    et al.
    Zaslavsky, Arkady
    Srinivasan, B.
    Revising replication strategies for mobile computing environments1995Inngår i: ECOOP '95: object-oriented programming : 9th European Conference Åarhus [i.e. Aarhus], Denmark, August 7-11, 1995 : proceedings / [ed] Walter Olthoff, Encyclopedia of Global Archaeology/Springer Verlag, 1995, s. 1-4Konferansepaper (Fagfellevurdert)
  • 54.
    Finkel, Raphael A.
    et al.
    University of Kentucky.
    Zaslavsky, Arkady
    Monostori, Krisztián
    Monash University, Melbourne, VIC.
    Schmidt, Heinz
    Monash University, Melbourne, VIC.
    Signature extraction for overlap detection in documents2002Inngår i: Australian Computer Science Communications, ISSN 0157-3055, Vol. 24, nr 1, s. 59-64Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Easy access to the Web has led to increased potential for students cheating on assignments by plagiarising others' work. By the same token, Web-based tools offer the potential for instructors to check submitted assignments for signs of plagiarism. Overlap-detection tools are easy to use and accurate in plagiarism detection, so they can be an excellent deterrent to plagiarism. Documents can overlap for other reasons, too: Old documents are superseded, and authors summarize previous work identically in several papers. Overlap-detection tools can pinpoint interconnections in a corpus of documents and could be used in search engines.We describe a web-accessible text registry based on signature extraction. We extract a small but diagnostic signature from each registered text for permanent storage and comparison against other stored signatures. This comparison allows us to estimate the amount of overlap between pairs of documents, although the total time required is linear in the total size of the documents. We compare our algorithm with several alternatives and present both efficiency and accuracy results.

  • 55.
    Gaber, M. M.
    et al.
    Monash University, Melbourne, VIC.
    Krishnaswamy, S.
    Monash University, Melbourne, VIC.
    Zaslavsky, Arkady
    On-board mining of data streams in sensor networks2005Inngår i: Advanced methods for knowledge discovery from complex data, New York: Encyclopedia of Global Archaeology/Springer Verlag, 2005, s. 307-336Kapittel i bok, del av antologi (Annet vitenskapelig)
  • 56. Gaber, Mohamed
    et al.
    Gillick, Brett
    Krishnaswamy, Shonali
    Zaslavsky, Arkady
    Visualisation of cluster dynamics and change detection in ubiquitous data stream mining2006Inngår i: Proceedings of the 3rd International Workshop on Knowledge Discovery from Data Streams / [ed] Jao Gama, Berlin, 2006Konferansepaper (Fagfellevurdert)
  • 57. Gaber, Mohamed
    et al.
    Gillick, Brett
    Krishnaswamy, Shonali
    Zaslavsky, Arkady
    Visualisation of fuzzy classification of data elements in ubiquitous data stream mining2006Inngår i: Ubiquitous computing: proceedings of the 3rd International Workshop on Ubiquitous Computing, IWUC 2006 ; in conjunction with ICEIS 2006 ; Paphos, Cyprus, May 2006 / [ed] Soraya Kouadri Mostéfaoui, Setúbal: INSTICC Press, 2006Konferansepaper (Fagfellevurdert)
  • 58.
    Gaber, Mohamed M
    et al.
    Monash University, Melbourne, VIC.
    Krishnaswamy, Shonali
    Monash University, Melbourne, VIC.
    Zaslavsky, Arkady
    Ubiquitous data stream mining2004Inngår i: Current research and future directions workshop. Proceedings: held in conjunction with The Eighth Pacific-Asia Conference on Knowledge Discovery and Data Mining, Sydney, Australia May 26 2004, 2004Konferansepaper (Fagfellevurdert)
    Abstract [en]

    The dissemination of data stream systems, wireless networks and mobile devices motivates the need for an efficient data analysis tool capable of gaining insights about these continuous data streams. Ubiquitous data mining (UDM) is concerned with this problem. UDM is the time-critical process of pattern discovery in data streams in a wireless environment. In this paper, the state of the art of mining data streams is given and our approach in tackling the problem is presented. The paper also highlights the addressed and open issues in the field.

  • 59.
    Gaber, Mohamed Medhat
    et al.
    School of Computing, University of Portsmouth.
    Krishnaswamy, Shonali
    Centre for Distributed Systems and Software Engineering, Monash University.
    Gillick, Britt
    Centre for Distributed Systems and Software Engineering, Monash University.
    Nicoloudis, Nicholas
    Centre for Distributed Systems and Software Engineering, Monash University.
    Liono, Jonathan
    Centre for Distributed Systems and Software Engineering, Monash University.
    Taiar, Hasnain Al
    Centre for Distributed Systems and Software Engineering, Monash University.
    Zaslavsky, Arkady
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Datavetenskap.
    Adaptive clutter-aware visualization for mobile data stream mining2010Inngår i: 22nd International Conference on Tools with Artificial Intelligence: proceedings : 27-29 October 2010, Arras, France, Los Alamitos, Calif: IEEE Communications Society, 2010, s. 304-311Konferansepaper (Fagfellevurdert)
    Abstract [en]

    There is an emerging focus on real-time data stream analysis on mobile devices. A wide range of data stream processing applications are targeted to run on mobile handheld devices with limited computational capabilities such as patient monitoring, driver monitoring, providing real-time analysis and visualization for emergency and disaster management, real-time optimization for courier pick-up and delivery etc. There are many challenges in visualization of the analysis/data stream mining results on a mobile device. These include coping with the small screen real-estate and effective presentation of highly dynamic and real-time analysis. This paper proposes a generic theory for visualization on small screens that we term Adaptive Clutter Reduction ACR. Based on ACR, we have developed and experimentally validated a novel data stream clustering result visualization technique that we term Clutter-Aware Clustering Visualizer (CACV). Experimental results on both synthetic and real datasets using the Google Andriod platform are presented proving the effectiveness of the proposed techniques.

  • 60.
    Gaber, Mohamed Medhat
    et al.
    Monash University, Melbourne, VIC.
    Krishnaswamy, Shonali
    Monash University, Melbourne, VIC.
    Zaslavsky, Arkady
    A cost-efficient model for ubiquitous data stream mining2004Inngår i: Proceedings of the International Conference on information Processing and Management of Uncertainty in Knowledge-Based Systems, Perugia: Casa Editrice Universita' La Sapienza , 2004Konferansepaper (Fagfellevurdert)
  • 61.
    Gaber, Mohamed Medhat
    et al.
    Monash University, Melbourne, VIC.
    Krishnaswamy, Shonali
    Monash University, Melbourne, VIC.
    Zaslavsky, Arkady
    A wireless data stream mining model2004Inngår i: Proceedings of the 3rd International Workshop on Wireless Information Systems, INSTICC Press, 2004Konferansepaper (Fagfellevurdert)
    Abstract [en]

    The sensor networks, web click stream and astronomical applications generate a continuous flow of data streams. Most likely data streams are generated in a wireless environment. These data streams challenge our ability to store and process them in real-time with limited computing capabilities of the wireless environment. Querying and mining data streams have attracted attention in the past two years. The main idea behind the proposed techniques in mining data streams in to develop efficient approximate algorithms with an acceptable accuracy. Recently, we have proposed algorithm output granularity as an approach in mining data streams. This approach has the advantage of being resource-aware in addition to its generality. In this paper, a model for mining data streams in a wireless environment has been proposed. The model contains two novel contributions; a ubiquitous data mining system architecture and algorithm output granularity approach in mining data streams.

  • 62.
    Gaber, Mohamed Medhat
    et al.
    Monash University, Melbourne, VIC.
    Krishnaswamy, Shonali
    Monash University, Melbourne, VIC.
    Zaslavsky, Arkady
    Adaptive mining techniques for data streams using algorithm output granularity2003Inngår i: Proceedings of the 2nd Australasian Data Mining Workshop, The University of Technology , 2003Konferansepaper (Fagfellevurdert)
    Abstract [en]

    Mining data streams is an emerging area of research given the potentially large number of business and scientific applications. A significant challenge in analyzing/ mining data streams is the high data rate of the stream. In this paper, we propose a novel approach to cope with the high data rate of incoming data streams. We termed our approach "algorithm output granularity". It is a resource-aware approach that is adaptable to available memory, time constraints, and data stream rate. The approach is generic and applicable to clustering, classification and counting frequent items mining techniques. We have developed a data stream clustering algorithm based on the algorithm output granularity approach. We present this algorithm and discuss its implementation and empirical evaluation. The experiments show  acceptable accuracy accompanied with run-time efficiency. They show that the proposed algorithm outperforms the K-means in terms of running time while preserving the accuracy that our algorithm can achieve.

  • 63.
    Gaber, Mohamed Medhat
    et al.
    Monash University, Melbourne, VIC.
    Krishnaswamy, Shonali
    Monash University, Melbourne, VIC.
    Zaslavsky, Arkady
    Cost-efficient mining techniques for data streams2004Inngår i: Proceedings of the Second Australasian Information Security Workshop (AISW2004), the Australasian Workshop on Data Mining and Web Intelligence (DMWI2004), and the Australasian Workshop on Software Internationalisation (AWSI2004), Australian Computer Society, 2004, Vol. 32, s. 109-114Konferansepaper (Fagfellevurdert)
    Abstract [en]

    A data stream is a continuous and high-speed flow of data items. High speed refers to the phenomenon that the data rate is high relative to the computational power. The increasing focus of applications that generate and receive data streams stimulates the need for online data stream analysis tools. Mining data streams is a real time process of extracting interesting patterns from high-speed data streams. Mining data streams raises new problems for the data mining community in terms of how to mine continuous high-speed data items that you can only have one look at. In this paper, we propose algorithm output granularity as a solution for mining data streams. Algorithm output granularity is the amount of mining results that fits in main memory before any incremental integration. We show the application of the proposed strategy to build efficient clustering, frequent items and classification techniques. The empirical results for our clustering algorithm are presented and discussed which demonstrate acceptable accuracy coupled with efficiency in running time.

  • 64.
    Gaber, Mohamed Medhat
    et al.
    Monash University, Melbourne, VIC.
    Krishnaswamy, Shonali
    Monash University, Melbourne, VIC.
    Zaslavsky, Arkady
    Resource-aware knowledge discovery in data streams2004Inngår i: Proceedings of the First International Workshop on Knowledge Discovery in Data Streams / [ed] J. Gama; J.S. Aguilar-Ruiz, ECML/PKDD 2004 conference , 2004Konferansepaper (Fagfellevurdert)
    Abstract [en]

    Mining data streams has raised a number of research challenges for the data mining community. These challenges include the limitations of computational resources, especially because mining streams of data most likely be done on a mobile device with limited resources. Also due to the continuality of data streams, the algorithm should have only one pass or less over the incoming data elements. In this paper, our Algorithm Output Granularity (AOG) approach in mining data streams is discussed. AOG is a novel adaptable approach that can cope with the challenging inherent features of data streams. We also show the results for AOG based clustering in a resource constrained environment.

  • 65.
    Gaber, Mohamed Medhat
    et al.
    Monash University, Melbourne, VIC.
    Krishnaswamy, Shonali
    Monash University, Melbourne, VIC.
    Zaslavsky, Arkady
    Resource-aware mining of data streams2005Inngår i: Journal of universal computer science (Online), ISSN 0948-695X, E-ISSN 0948-6968, Vol. 11, nr 8Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Mining data streams has raised a number of research challenges for the data mining community. These challenges include the limitations of computational resources, especially because mining streams of data most likely be done on a mobile device with limited resources. Also due to the continuality of data streams, the algorithm should have only one pass or less over the incoming data records. In this article, our Algorithm Output Granularity (AOG) approach in mining data streams is discussed. AOG is a novel adaptable approach that can cope with the challenging inherent features of data streams. We also show the results for AOG based clustering in a resource constrained environment.

  • 66.
    Gaber, Mohamed Medhat
    et al.
    Caulfield School of Information Technology, Monash University.
    Zaslavsky, Arkady
    Krishnaswamy, Shonali
    Caulfield School of Information Technology, Monash University.
    A survey of classification methods in data streams2007Inngår i: Data Streams: Models and Algorithms, Berlin: Encyclopedia of Global Archaeology/Springer Verlag, 2007, s. 39-59Kapittel i bok, del av antologi (Annet vitenskapelig)
    Abstract [en]

    With the advance in both hardware and software technologies, automated data generation and storage has become faster than ever. Such data is referred to as data streams. Streaming data is ubiquitous today and it is often a challenging task to store, analyze and visualize such rapid large volumes of data. Most conventional data mining techniques have to be adapted to run in a streaming environment, because of the underlying resource constraints in terms of memory and running time. Furthermore, the data stream may often show concept drift, because of which adaptation of conventional algorithms becomes more challenging. One such important conventional data mining problem is that of classification. In the classification problem, we attempt to model the class variable on the basis of one or more feature variables. While this problem has been extensively studied from a conventional mining perspective, it is a much more challenging problem in the data stream domain. In this chapter, we will re-visit the problem of classification from the data stream perspective. The techniques for this problem need to be thoroughly re-designed to address the issue of resource constraints and concept drift. This chapter reviews the state-of-the-art techniques in the literature along with their corresponding advantages and disadvantages.

  • 67.
    Gaber, Mohamed Medhat
    et al.
    Centre for Distributed Systems and Software Engineering, Monash University.
    Zaslavsky, Arkady
    Krishnaswamy, Shonali
    Centre for Distributed Systems and Software Engineering, Monash University.
    Mining data streams: a review2005Inngår i: SIGMOD record, ISSN 0163-5808, E-ISSN 1943-5835, Vol. 34, nr 2, s. 18-26Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    The recent advances in hardware and software have enabled the capture of different measurements of data in a wide range of fields. These measurements are generated continuously and in a very high fluctuating data rates. Examples include sensor networks, web logs, and computer network traffic. The storage, querying and mining of such data sets are highly computationally challenging tasks. Mining data streams is concerned with extracting knowledge structures represented in models and patterns in non stopping streams of information. The research in data stream mining has gained a high attraction due to the importance of its applications and the increasing generation of streaming information. Applications of data stream analysis can vary from critical scientific and astronomical applications to important business and financial ones. Algorithms, systems and frameworks that address streaming challenges have been developed over the past three years. In this review paper, we present the state-of-the-art in this growing vital field.

  • 68.
    Gaber, Mohamed Medhat
    et al.
    School of Computer Science & Software Engineering, Monash University Melbourne.
    Zaslavsky, Arkady
    Krishnaswamy, Shonali
    School of Computer Science & Software Engineering, Monash University Melbourne.
    Towards an adaptive approach for mining data streams in resource constrained environments2004Inngår i: Data Warehousing and Knowledge Discovery: 6th International Conference, DaWaK 2004, Zaragoza, Spain, September 1-3, 2004. Proceedings / [ed] Yahiko Kambayashi; Mukesh Mohania; Wolfram Wöß, Berlin: Encyclopedia of Global Archaeology/Springer Verlag, 2004, s. 189-198Konferansepaper (Fagfellevurdert)
    Abstract [en]

    Mining data streams in resource constrained environments has emerged as a challenging research issue for the data mining community in the past two years. Several approaches have been proposed to tackle the challenges of limited capabilities for small devices that generate or receive data streams. These approaches try to approximate the mining results with acceptable accuracy and efficiency in space and time complexity. However these approaches are not resource-aware. In this paper, a thorough discussion about the state of the art of mining data streams is presented followed by a formalization of our Algorithm Output Granularity (AOG) approach in mining data streams. The incorporation of AOG within a generic ubiquitous data mining system architecture is shown and discussed. The industrial applications of AOG-based mining techniques are given and discussed.

  • 69. Gan, Choon Hean
    et al.
    Giles, Stephen Roy
    Zaslavsky, Arkady
    CAWAnalyser: enhancing wireless intrusion detection with runtime context-awareness2004Inngår i: Proceedings of the Australian Telecommunication Networks and Applications Conference / [ed] F. Safaei, 2004Konferansepaper (Fagfellevurdert)
  • 70.
    Gan, Choon Hean
    et al.
    School of Computer Science & Software Engineering, Monash University Melbourne.
    Zaslavsky, Arkady
    Giles, Stephen
    School of Computer Science & Software Engineering, Monash University Melbourne.
    CAWAnalyser: enhancing wireless intrusion response with runtime context-awareness2005Inngår i: Networking - ICN 2005: 4th International Conference on Networking, Reunion Island, France, April 2005. Proceedings / [ed] Pascal Lorenz, Berlin: Encyclopedia of Global Archaeology/Springer Verlag, 2005, s. 239-246Konferansepaper (Fagfellevurdert)
    Abstract [en]

    Most existing wireless IDSs do not provide timely active responses to wireless intrusions as the execution of the responses is done manually by the administrator. Some wireless IDSs address this issue by providing automated responses. On one hand, they reduce the chances of successful wireless attacks by responding immediately to intrusions. On the other hand, they execute responses without considering environmental factors and hence, results in execution of unsuitable responses causing negative effects to legitimate systems. This paper addresses this issue by proposing a wireless IDS with adaptive automated response mechanism named Context Aware Wireless Analyser (CAWAnalyser). CAWAnalyser selects an appropriate response based on a number of contextual factors, and invokes the selected response if the total impact of such response is lower than the total impact of the corresponding attack.

  • 71.
    Gillick, Brett
    et al.
    Centre for Distributed Systems and Software Engineering, Monash University.
    Taiar, Hasnain Al
    Centre for Distributed Systems and Software Engineering, Monash University.
    Krishnaswamy, Shonali
    Centre for Distributed Systems and Software Engineering, Monash University.
    Liono, Jonathan
    Centre for Distributed Systems and Software Engineering, Monash University.
    Nicoloudis, Nicholas
    Centre for Distributed Systems and Software Engineering, Monash University.
    Sinah, Abhijat
    Centre for Distributed Systems and Software Engineering, Monash University.
    Zaslavsky, Arkady
    Gaber, Mohamed Medhat
    School of Computing, University of Portsmouth.
    Clutter-adaptive visualization for mobile data mining2011Inngår i: 2010 IEEE International Conference on Data Mining Workshops: (ICDMW), Sidney 13 Dec 2010, Piacataway, NJ: IEEE Communications Society, 2011, s. 1381-1384Konferansepaper (Fagfellevurdert)
  • 72.
    Gunasekera, Kutila
    et al.
    Monash University, Melbourne, VIC.
    Krishnaswamy, Shonali
    Monash University, Melbourne, VIC.
    Loke, Seng Wai
    La Trobe University.
    Zaslavsky, Arkady
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Datavetenskap.
    Runtime efficiency of adaptive mobile software agents in pervasive computing environments2009Inngår i: AUPC '09: ICPS 2009 & co-located workshops compilation proceedings, July 13-16, 2009, Imperial College, London, UK, New York: ACM Digital Library, 2009, s. 123-131Konferansepaper (Fagfellevurdert)
    Abstract [en]

    Developing software that can support heterogeneous hardware platforms, adapt to dynamic highly variable environments and meet rapidly changing requirements is a challenge of pervasive computing. Dynamic adaptive software agents are a promising approach to develop software for such environments. We previously proposed an agent architecture which brings together features such as runtime adaptivity, agent mobility, platform-independence, context-awareness and lightweightness which are desirable in pervasive environments. In this paper, we propose and analyse a cost model of network load for our runtime adaptive mobile software agents. We compare our adaptive agents against standard non-adaptive mobile agents using this cost model. Subsequent experimental results demonstrate that our agents outperform non-adaptive mobile agents. We also propose and discuss further features which make our adaptive agent based software solution more suitable for pervasive computing environments.

  • 73.
    Gunasekera, Kutila
    et al.
    Monash University, Melbourne, VIC.
    Krishnaswamy, Shonali
    Monash University, Melbourne, VIC.
    Loke, Seng
    La Trobe University.
    Zaslavsky, Arkady
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Datavetenskap.
    Adaptation support for agent based pervasive systems2012Inngår i: Mobile and Ubiquitous Systems: Computing, Networking, and Services: 7th International ICST Conference, MobiQuitous 2010, Sydeny, Australia, December 6-9, 2010, Revised Selected Papers, Heidelberg: Encyclopedia of Global Archaeology/Springer Verlag, 2012, s. 90-101Konferansepaper (Fagfellevurdert)
    Abstract [en]

    Pervasive computing systems execute in dynamic highly variable environments and need software that are context-aware and can adapt at runtime. Mobile agents are viewed as an enabling technology for building software for such environments due to their flexibility, migratory nature and scalability. This paper presents a novel approach which aims to further enhance this advantage by building compositionally adaptive mobile software agents that are also context-driven, component-based and have the ability to exchange their components with peer agents. We present the formal underpinnings of our approach and a decision making model which assists agent adaptation. We also describe our current implementation and experimental results to evaluate the benefits of the proposed approach.

  • 74.
    Gunasekera, Kutila
    et al.
    Faculty of Information Technology, Monash University.
    Loke, Seng Wai
    Department of Computer Science & Computer Engineering, La Trobe University.
    Zaslavsky, Arkady
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Datavetenskap.
    Krisnaswamy, Shonali
    Faculty of Information Technology, Monash University.
    Runtime adaptation of multiagent systems for ubiquitous environments2009Inngår i: IWI 2009: 2009 IEEE/WIC/ACM International Conference on Web Intelligence : proceedings, 15-18 September, 2009 : Università degli Studi di Milano Bicocca, Milano, Italy, Los Alamitos, Calif: IEEE Communications Society, 2009, Vol. 2, s. 486-490Konferansepaper (Fagfellevurdert)
    Abstract [en]

    There is a growing need to develop adaptive mobile agent systems to meet the increasing complexity and dynamic environments of distributed applications. This paper proposes and argues that using compositional adaptation of individual and teamed software agents allows multiagent systems to radically change their behaviors and structure at runtime. We describe and analyze an application scenario which motivates a compositionally adaptive agent based approach. The challenges and different solutions are described. The paper then proposes the VERSAG approach which is used to combine the benefits of the various approaches in a novel manner. This paper also presents experimental evaluations which compare these adaptive mobile agents with standard non-adaptive mobile agents.

  • 75.
    Gunasekera, Kutila
    et al.
    Faculty of Information Technology, Monash University.
    Zaslavsky, Arkady
    Krishnaswamy, Shonali
    Faculty of Information Technology, Monash University.
    Loke, Seng Wai
    Department of Computer Science & Computer Engineering, La Trobe University.
    Component based approach for composing adaptive mobile agents2009Inngår i: Agent and Multi-Agent Systems: Technologies and Applications: Third KES International Symposium, KES-AMSTA 2009, Uppsala, Sweden, June 3-5, 2009. Proceedings / [ed] Anne Hakansson; Ngoc Thanh Nguyen; Ronald Hartung; Robert J. Howlett; Lakhmi C. Jain, Berlin: Encyclopedia of Global Archaeology/Springer Verlag, 2009, s. 90-99Konferansepaper (Fagfellevurdert)
    Abstract [en]

    Mobile software agents are increasingly identified as suitable for developing pervasive applications. This paper presents a novel approach to compose adaptive mobile agents. The proposed agents are context-driven, adaptive, component-based and are able to exchange their capabilities with peer agents. The approach requires a flexible, lightweight and standardized component model. We describe the proposed agent system, issues related to selection of a component model, implementation and future research issues.

  • 76.
    Gunasekera, Kutila
    et al.
    Faculty of Information Technology, Monash University.
    Zaslavsky, Arkady
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Datavetenskap.
    Krishnaswamy, Shonali
    Faculty of Information Technology, Monash University.
    Loke, Seng Wai
    Department of Computer Science & Computer Engineering, La Trobe University.
    Service oriented context-aware software agents for greater efficiency2010Inngår i: Agent and multi-agent systems: technologies and applications: 4th KES international symposium, KES-AMSTA 2010, Gdynia, Poland, June 23-25, 2010 ; proceedings / [ed] Piotr Jedrzejowicz; Ngoc Thanh Nguyen; Robert J. Howlet; Lakhmi C. Jain, Berlin: Encyclopedia of Global Archaeology/Springer Verlag, 2010, s. 62-71Konferansepaper (Fagfellevurdert)
    Abstract [en]

    Convergence of web service technology with intelligent and mobile agent technology, enhanced with context awareness, can produce a new software paradigm that will enable more efficient and effective mobile services. Traditionally, an agent has been a passive service requestor with the service processing happening at the provider's site. We propose that in some situations it would be beneficial if the agent could acquire the ability to execute the functionality provided by the service and run it locally instead of simply using the service. To this end, we build an analytical model which compares time consumption of traditional service access with that of component use. Such a model would allow an agent to dynamically select the more efficient approach. We describe experiments which compare the two approaches and discuss the results and lessons learnt

  • 77.
    Gunasekera, Kutila
    et al.
    Faculty of Information Technology, Monash University.
    Zaslavsky, Arkady
    Krishnaswamy, Shonali
    Faculty of Information Technology, Monash University.
    Loke, Seng Wai
    Department of Computer Science and Computer Engineering, La Trobe University.
    VERSAG: context-aware adaptive mobile agents for the semantic web2008Inngår i: 32nd Annual IEEE International Computer Software and Applications Conference: COMPSAC 2008 ; Turku, Finland, 28 July - 1 August 2008, Piscataway, NJ: IEEE Communications Society, 2008, s. 521-522Konferansepaper (Fagfellevurdert)
    Abstract [en]

    Software agents roaming around and accessing services is an important part of the vision of the Semantic Web. The need to engage in diverse activities in rapidly changing environments makes it essential that these agents are able to adapt to varying situations. We propose a novel approach to engineer adaptive software agents for such scenarios. Our agents have the ability to exchange their capabilities with peers, support multiple forms of adaptation, enable software reuse through a component-based infrastructure and provide fine-grained and efficient agent mobility. We describe our solution, the first implementation and identify further research issues.

  • 78.
    Gunasekera, Kutila
    et al.
    Faculty of Information Technology, Monash University.
    Zaslavsky, Arkady
    Loke, Seng Wai
    Department of Computer Science and Computer Engineering, La Trobe University.
    Krishnaswamy, Shonali
    Faculty of Information Technology, Monash University.
    Context driven compositional adaptation of mobile agents2008Inngår i: 2008 Ninth International Conference on Mobile Data Management workshops: MDMW ; Beijing, China, 27 - 30 April 2008, Piscataway, NJ: IEEE Communications Society, 2008, s. 201-208Konferansepaper (Fagfellevurdert)
    Abstract [en]

    Agent technology is increasingly seen as an attractive approach to develop applications for pervasive environments. However, it is a challenge to develop agent systems for such complex and dynamic environments. We propose a novel approach to develop software for pervasive environments by using context-aware adaptive agents. Our agents have the ability to exchange their capabilities with peers, are self-adaptive and enable software reuse through a component-based infrastructure. The solution also enables reduced network traffic and has the potential to allow agents to execute on heterogeneous agent platforms. We also describe the first implementation of the solution and discuss experiments carried out with it and possible further enhancements.

  • 79.
    Haghighi, Pari Delir
    et al.
    Centre for Organisational and Social Informatics, Monash University.
    Burstein, Frada
    Centre for Organisational and Social Informatics, Monash University.
    Zaslavsky, Arkady
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Datavetenskap.
    Arbon, Paul
    Flinders University.
    The role of domain ontology for medical emergency management in mass gatherings2010Inngår i: Bridging the Socio-technical Gap in Decision Support Systems: Challenges for the Next Decade, Amsterdam: IOS Press, 2010, s. 520-531Kapittel i bok, del av antologi (Fagfellevurdert)
    Abstract [en]

    Mass gatherings are common events that typically attract large crowds of people. If such events are not properly planned, coordinated and managed with regard to health and safety issues, they can become hazardous and lead to injuries, illnesses and deaths. Conducting a safe and successful mass gathering event requires effective planning and management including the provision of timely medical care and response. To achieve these goals, there is a need for a unifying and formal model/framework of mass gatherings that can be applied across all the emergency agencies and events, and used in support of time-critical decision making for medical emergency management in this context. However, the absence of a common knowledge structure and conceptual model in Medical Emergency Management in Mass Gathering (MEMMG), acknowledged in the literature, limits our understanding of such events and impedes the effectiveness of decision support systems in these environments. In this paper, we propose domain ontology for MEMMG that represents main concepts of mass gatherings and their characteristics and relationships in a standard and formal manner. The proposed domain ontology is an instantiation and extension of DOEM (Domain Ontology for Emergency Management) that represents major generic concepts in the emergency management and can be used as information structure for the development of various emergency management decision support systems. We illustrate application of such an ontology to the classical Intelligence-Design-Choice-Implementation decision support model

  • 80.
    Haghighi, Pari Delir
    et al.
    Centre for Distributed Systems and Software Engineering, Monash University.
    Gaber, Mohamed
    Centre for Distributed Systems and Software Engineering, Monash University.
    Krishnaswamy, Shonali
    Centre for Distributed Systems and Software Engineering, Monash University.
    Zaslavsky, Arkady
    Situation-aware adaptive processing (SAAP) of data streams: Chapter 142009Inngår i: Pervasive Computing: Innovations in Intelligent Multimedia and Applications, Computer Communications and Networks, London: Encyclopedia of Global Archaeology/Springer Verlag, 2009, s. 313-338Kapittel i bok, del av antologi (Annet vitenskapelig)
    Abstract [en]

    The growth and proliferation of technologies in the field of sensor networking and mobile computing have led to the emergence of diverse applications that process and analyze sensory data on mobile devices such as a smart phone. However, the real power to make a significant impact on the area of developing these applications rests not merely on deploying the technologies, but on the ability to perform real-time, intelligent analysis of the data streams that are generated by the various sensors. In this chapter, we present a novel approach for Situation-Aware Adaptive Processing (SAAP) of data streams for pervasive computing environments. This approach uses fuzzy logic principles for modelling and reasoning about uncertain situations, and performs gradual adaptation of parameters of data stream mining algorithms in real-time according to availability of resources and the occurring situations.

  • 81.
    Haghighi, Pari Delir
    et al.
    Centre for Distributed Systems and Software Engineering, Monash University.
    Gillick, Brett
    Centre for Distributed Systems and Software Engineering, Monash University.
    Krishnaswamy, Shonali
    Centre for Distributed Systems and Software Engineering, Monash University.
    Gaber, Mohamed Medhat
    Centre for Distributed Systems and Software Engineering, Monash University.
    Zaslavsky, Arkady
    Situation-aware adaptive visualization for sensory data stream mining2010Inngår i: Knowledge discovery from sensor data: second international workshop, Sensor-KDD 2008, Las Vegas, NV, USA, August 24-27, 2008 ; revised selected papers / [ed] Mohamed Medhat Gaber; Ranga Raju Vatsavai; Olufemi A. Omitaomu; João Gama; Nitesh V. Chawla; Auroop R. Ganguly, Berlin: Encyclopedia of Global Archaeology/Springer Verlag, 2010, s. 43-58Konferansepaper (Fagfellevurdert)
    Abstract [en]

    With the emergence of ubiquitous data mining and recent advances in mobile communications, there is a need for visualization techniques to enhance the user-interactions, real-time decision making and comprehension of the results of mining algorithms. In this paper we propose a novel architecture for situation-aware adaptive visualization that applies intelligent visualization techniques to data stream mining of sensory data. The proposed architecture incorporates fuzzy logic principles for modeling and reasoning about context/situations and performs gradual adaptation of data mining and visualization parameters according to the occurring situations. A prototype of the architecture is implemented and described in the paper through a real-world scenario in the area of healthcare monitoring

  • 82.
    Haghighi, Pari Delir
    et al.
    Centre for Distributed Systems and Software Engineering, Monash University.
    Krishnaswamy, Shonali
    Centre for Distributed Systems and Software Engineering, Monash University.
    Zaslavsky, Arkady
    Gaber, Mohamed Medhat
    Centre for Distributed Systems and Software Engineering, Monash University.
    Reasoning about Context in Uncertain Pervasive Computing Environments2008Inngår i: Smart sensing and context: third European conference, EuroSSC 2008, Zurich, Switzerland, October 29-31, 2008 ; proceedings / [ed] Daniel Roggen; Clemens Lombriser; Gerhard Tröster; Gerd Kortuem; Paul Havinga, Encyclopedia of Global Archaeology/Springer Verlag, 2008, s. 112-125Konferansepaper (Fagfellevurdert)
    Abstract [en]

    Context-awareness is a key to enabling intelligent adaptation in pervasive computing applications that need to cope with dynamic and uncertain environments. Addressing uncertainty is one of the major issues in context-based situation modeling and reasoning approaches. Uncertainty can be caused by inaccuracy, ambiguity or incompleteness of sensed context. However, there is another aspect of uncertainty that is associated with human concepts and real-world situations. In this paper we propose and validate a Fuzzy Situation Inference (FSI) technique that is able to represent uncertain situations and reflect delta changes of context in the situation inference results. The FSI model integrates fuzzy logic principles into the Context Spaces (CS) model, a formal and general context reasoning and modeling technique for pervasive computing environments. The strengths of fuzzy logic for modeling and reasoning of imperfect context and vague situations are combined with the CS model's underlying theoretical basis for supporting context-aware pervasive computing scenarios. An implementation and evaluation of the FSI model are presented to highlight the benefits of the FSI technique for context reasoning under uncertainty

  • 83.
    Haghighi, Pari Delir
    et al.
    Monash University, Melbourne, VIC.
    Zaslavsky, Arkady
    Krishnaswamy, Shonali
    Monash University, Melbourne, VIC.
    An evaluation of query languages for context-aware computing2006Inngår i: Proceedings: Seventeenth International Conference on Database and Expert Systems Applications (DEXA 2006) : 4 - 8 September 2006, Krakow, Poland ; [workshop papers], Los Alamitos, Calif: IEEE Communications Society, 2006, s. 455-462Konferansepaper (Fagfellevurdert)
    Abstract [en]

    Context-awareness highly depends on easy and prompt access to the right information and this needs overcoming challenges of query expression and processing of context information. One of the main considerations in querying context is selecting and applying an appropriate and expressive query language for defining user's context needs. This paper presents an overview of current languages used for querying context and investigates their strengths and limitations in expressing different aspects of context.

  • 84.
    Haghighi, Pari Delir
    et al.
    Centre for Distributed Systems and Software Engineering, Monash University.
    Zaslavsky, Arkady
    Krishnaswamy, Shonali
    Centre for Distributed Systems and Software Engineering, Monash University.
    Gaber, Mohamed
    Centre for Distributed Systems and Software Engineering, Monash University.
    Mobile data mining for intelligent healthcare support2009Inngår i: 2009 42nd Hawaii International Conference on System Sciences: HICSS ; Waikoloa, Hawaii, 5 - 8 January 2009, IEEE Communications Society, 2009, s. 1-10Konferansepaper (Fagfellevurdert)
    Abstract [en]

    The growth in numbers and capacity of mobile devices such as mobile phones coupled with widespread availability of inexpensive range of biosensors presents an unprecedented opportunity for mobile healthcare applications. In this paper we propose a novel approach for Situation-Aware Adaptive Processing (SAAP) of data streams for smart and real-time analysis of data. The implementation and evaluation of the framework for a health monitoring application is described.

  • 85.
    Haghighi, Pari Delir
    et al.
    Centre for Distributed Systems and Software Engineering, Monash University.
    Zaslavsky, Arkady
    Krishnaswamy, Shonali
    Centre for Distributed Systems and Software Engineering, Monash University.
    Gaber, Mohamed
    Centre for Distributed Systems and Software Engineering, Monash University.
    Loke, Seng Wai
    Department of Computer Science and Computer Engineering, La Trobe University.
    Context-aware adaptive data stream mining2009Inngår i: Intelligent Data Analysis, ISSN 1088-467X, E-ISSN 1571-4128, Vol. 13, nr 3, s. 423-434Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    In resource-constrained devices, adaptation of data stream processing to variations of data rates and availability of resources is crucial for consistency and continuity of running applications. However, to enhance and maximize the benefits of adaptation, there is a need to go beyond mere computational and device capabilities to encompass the full spectrum of context-awareness. This paper presents a general approach for context-aware adaptive mining of data streams that aims to dynamically and autonomously adjust data stream mining parameters according to changes in context and situations. We perform intelligent and real-time analysis of data streams generated from sensors that is under-pinned using context-aware adaptation. A prototype of the proposed architecture is implemented and evaluated in the paper through a real-world scenario in the area of healthcare monitoring.

  • 86.
    Hii, Paul
    et al.
    Monash University, Melbourne, VIC.
    Jayaraman, Prem
    Monash University, Melbourne, VIC.
    Zaslavsky, Arkady
    Message-on-demand service in a decentralized unified messaging system2006Inngår i: Proceedings of the Third Annual Conference on Wireless On Demand Network Systems and Services, L'INRIA - Institut national de recherche en informatique et en automatique, 2006Konferansepaper (Fagfellevurdert)
    Abstract [en]

    Variety of service integration concepts have emerged during the last few years. Most of these aim at the concept of integrating telecommunication and the data communication technologies. One form of such a system is Unified Messaging. Unified Messaging enables users to manage their messages independent of location, communication device or communication medium. Most of the existing systems provide centralized message-store based access to messages but lack services like user personalization, message notification and are non-pervasive. In this paper, we define UMS as a user-centric system that provides messaging services based on user demands and preferences. We have proposed a decentralized Unified Messaging System (DUMS) that is pervasive and context-aware. Based on our proposed architecture, we successfully implemented and demonstrated a messaging system called i- UMS that ensures a user receives almost instantly all messages such as emails, instant messages and so on in an intelligent and most appropriate manner.

  • 87.
    Indrawan, Maria
    et al.
    Monash University, Melbourne, VIC.
    Page, John Premjeet
    Monash University, Melbourne, VIC.
    Zaslavsky, Arkady
    Extending the buddy model to secure variable sized multi agent communities2005Inngår i: Proceedings of the Second International Workshop on Safety and Security in Multiagent Systems, 2005, s. 59-75Konferansepaper (Fagfellevurdert)
    Abstract [en]

    This paper describes an extension of the Buddy model of agent security. The Buddy model provides a security layer that encapsulates all agents within the multi - agent community and therefore, extends a security cover to all agents of the community. While the current model is able to service several scenarios involving fixed sized agent communities, it does not consider variable sized agent communities. This paper describes an extension to the existing model that covers this aspect. The proposed extension increases the application coverage and flexibility of the Buddy model and makes it a potent security mechanism for multi-agent communities. Further, the paper establishes that agent migration does not lead to any schema operating overheads; on the contrary it contributes to maintaining an effective level of performance integrity within the schema.

  • 88.
    Indrawan, Maria
    et al.
    Monash University, Melbourne, VIC.
    Page, John
    Monash University, Melbourne, VIC.
    Zaslavsky, Arkady
    Agent communities, security vulnerabilities and the Buddy model of security: applicability and effectiveness2004Inngår i: Proceedings of the 12th International Conference on Advanced Computing and Communications: ADCOM 2004 / [ed] A.R. Dasgupta; S.S. Iyengar; H.S. Bhatt, Allied Publishers Ltd , 2004, s. 316-324Konferansepaper (Fagfellevurdert)
    Abstract [en]

    The paper examines the applicability and the effectiveness of the Buddy model of security proposed for countering security vulnerabilities experienced by mobile agent communities. The paper describes the security vulnerabilities that arise within mobile agent communities and demonstrates the Buddy model operation as an effective countermeasure for securing the agent community operation. The Buddy model involves all mobile agent community members in sharing the security function operation. This reduces the limitations and the dependence of the model on a particular agent and leads to the distribution of the security threat equally over all community members. A performance analysis of the schema is given to illustrate the advantages and the reliability of the Buddy model in a mobile agent community scenario.

  • 89. Indulska, J.
    et al.
    Loke, S.W.
    Rakotonirainy, A.
    Witana, V.
    Zaslavsky, Arkady
    An open architecture for pervasive systems2001Inngår i: New developments in distributed applications and interoperable systems: IFIP TC6 WG6.1 Third International Working Conference on Distributed Applications and Interoperable Systems (DAIS'2001), September 17 - 19, 2001, Kraków, Poland / [ed] Krzystof Zielinski; Kurt Geihs; Aleksander Laurentowski, Boston: Kluwer Academic Publishers, 2001Konferansepaper (Fagfellevurdert)
  • 90. Jayaputera, G.
    et al.
    Loke, S.W.
    Monash University, Melbourne, VIC.
    Zaslavsky, Arkady
    Adding value to marketplaces by assembling location-aware agents on-demand2003Inngår i: Proceedings of the Second International Conference on Mobile Business: m>Business 2003 ; [Die Konferenz "m>Business" fand vom 23. bis 24. Juni 2003 in Wien statt] / [ed] G.M. Giaglis; H. Werthner; V. Tschammer; K.A. Froeschl, Wien: Die Österreichische Computer Gesellschaft, 2003Konferansepaper (Fagfellevurdert)
    Abstract [en]

    With the penetration of ubiquitous computing into physical marketplaces, requirements for software agents have increased also. The need for software agents at anytime, anyplace and helping the consumers with their shopping experience is becoming more obvious. Request from the consumers are changing all the time as well as the physical marketplace they are in (hence also the merchants). These conditions mean that a system that has a predefined number of agents; with each of them possessing a predefined set of functionality is not suitable for use. We propose a system, named eHermes, which assembles agents on-demand based on the user request and preferences. Such architecture supports dynamism and flexibility at run-time that are suitable for the pervasive computing environment. The main concept, called mission, and its abstract representation, called Task Decomposition Diagram, along with the eHermes architecture are presented in this paper. eHermes is currently being implemented on the Grasshopper agent toolkit.

  • 91.
    Jayaputera, G. T.
    et al.
    Monash University, Melbourne, VIC.
    Zaslavsky, Arkady
    Loke, Seng Wai
    La Trobe University.
    Enabling run-time composition and support for heterogeneous pervasive multi-agent systems2007Inngår i: Journal of Systems and Software, ISSN 0164-1212, E-ISSN 1873-1228, Vol. 80, nr 12, s. 2039-2062Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    User needs-driven and computer-supported development of pervasive heterogeneous and dynamic multi-agent systems remains a great challenge for agent research community. This paper presents an innovative approach to composing, validating and supporting multi-agent systems at run-time. Multi-agent systems (MASs) can and should be assembled quasi-automatically and dynamically based on high-level user specifications which are transformed into a shared and common goal-mission. Dynamically generating agents could also be supported as a pervasive service. Heterogeneity of MASs refers to diverse functionality and constituency of the system which include mobile as well as host associated software agents. This paper proposes and demonstrates on-demand and just-in-time agent composition approach which is combined with run-time support for MASs. Run-time support is based on mission cost-efficiency and shared objectives which enable termination, generation, injection and replacement of software agents as the mission evolves at run-time. We present the formal underpinning of our approach and describe the prototype tool - called eHermes, which has been implemented using available agent platforms. Analysis and results of evaluating eHermes are presented and discussed.

  • 92.
    Jayaputera, Glenn
    et al.
    Monash University, Melbourne, VIC.
    Loke, Sang Wei
    Monash University, Melbourne, VIC.
    Zaslavsky, Arkady
    Mission impossible? Automatically assembling agents from high-level task descriptions2003Inngår i: Proceedings: IEEE/WIC International Conference on Intelligent Agent Technology (IAT 2003) : Halifax, Canada, October 13 - 17, 2003 / [ed] Jiming Liu, Los Alamitos, Calif: IEEE Communications Society, 2003, s. 161-167Konferansepaper (Fagfellevurdert)
    Abstract [en]

    In this paper, we present our notion of automatically assembling agents on-demand given high-level task descriptions. Our approach is based on the concept of the mission, which is represented by a hierarchical structure called the task decomposition diagram (TDD). Tasks in TDD are specified using a task specification language, which defines the functionality and possible implementation of those tasks. We discuss several strategies for executing the TDD, for assembling agents based on the TDD, and present the architecture of our prototype system called eHermes.

  • 93. Jayaputera, Glenn
    et al.
    Loke, Seng Wai
    Zaslavsky, Arkady
    An integrated approach to multiagent applications in mobile and ubiquitous environments2004Inngår i: Proceedings of the 3rd Asian International Mobile Computing Conference / [ed] A. Phonphoem, Kasetsart University , 2004Konferansepaper (Fagfellevurdert)
  • 94.
    Jayaputera, Glenn
    et al.
    School of Computer Science & Software Engineering, Monash University Melbourne.
    Loke, Seng Wai
    School of Computer Science & Software Engineering, Monash University Melbourne.
    Zaslavsky, Arkady
    Just in time mobile agent generation and management2005Konferansepaper (Annet vitenskapelig)
    Abstract [en]

    This paper presents a new and innovative approach called mission-based just in time agent generation. The approach allows agents to be constructed on the fly, at run-time and just when they are needed. This is a completely different approach to the traditional way of creating MAS in that agents are constructed at design time. We present the theoretical work behind the notion as well as an experimental result and future work.

  • 95. Jayaputera, Glenn
    et al.
    Loke, Seng Wai
    Zaslavsky, Arkady
    Mission-based service-oriented computing2004Inngår i: M-business 2004: uncovering the next waves - major opportunities and the essential lessons ; the Third International Mobile Business Conference (ICMB) ; July 12 - 13, 2004, Roosevelt Hotel, NYC / [ed] B. Rao; M. Parikh; M. Horwitch, New York: Institute for Technology and Enterprise, Polytechnic University , 2004Konferansepaper (Fagfellevurdert)
  • 96.
    Jayaputera, Glenn
    et al.
    CSSE, Monash University.
    Loke, Seng Wai
    CSSE, Monash University.
    Zaslavsky, Arkady
    Performance evaluation of dynamically assembled multiagent systems2005Inngår i: Proceedings: 2005 IEEE/WIC/ACM International Conference on Intelligent Agent Technology : September 19 - 22, 2005, Compiègne University of Technology, France / [ed] Andrzej Skowron, Los Alamitos, Calif: IEEE Computer Society Press , 2005, s. 451-454Konferansepaper (Fagfellevurdert)
    Abstract [en]

    Most methodologies for developing software agents focus at design phase. The proposed notion of dynamically generating multiagent systems (MASs) from user-defined specifications (called "missions" in our approach) is somewhat unorthodox and introduces a new paradigm in MASs development and usage, concentrating on run-time phase. In the dynamic MASs agents are created on-demand at run-time with different functionalities depending on the objectives of the mission. In this paper, we propose and demonstrate a way for evaluating MASs at run-time. Performance evaluation of eHermes - the dynamic MAS generator/constructor - is then presented and analyzed.

  • 97. Jayaputera, Glenn T.
    et al.
    Loke, Seng Wai
    Zaslavsky, Arkady
    A mission-based multiagent system for internet applications2003Inngår i: Proceedings of the Fifth International Conference on Enterprise Information Systems : Angers, France, April 23 - 26, 2003, Escola superior de Tecnologia do Instituto Politecnico de Setubal , 2003Konferansepaper (Fagfellevurdert)
  • 98.
    Jayaputera, Glenn T.
    et al.
    Monash University, Melbourne, VIC.
    Zaslavsky, Arkady
    Loke, Seng Wai
    Monash University, Melbourne, VIC.
    Run-time mission evolution in mobile multiagent systems2004Inngår i: Proceedings / IEEE/WIC/ACM International Conference on Intelligent Agent Technology, 2004, IAT 2004 : Sept. 20 - 24, 2004, [Beijing, China] / [ed] Ning Zhong, Los Alamitos, Calif: IEEE Computer Society Press , 2004, s. 79-85Konferansepaper (Fagfellevurdert)
    Abstract [en]

    We present our approach to developing a multiagent system based on the proposed complex concept of the mission. The mission is a high-level abstraction that combines the overall goal of a system, the means, and the process of achieving that goal, from which agents can be automatically generated. We propose a mechanism to generate agents on-demand and at run-time rather than at the design stage. Functionalities are given to the agents at run-time also. Programmatically, a mission is a live object where it maintains not only the various revisions of the plan but also the state and data for each revision. By doing so, our system is able to suspend and resume the mission. Furthermore, we envisage that the mission execution history is useful for the planner so that it can learn from it and hence produce a better plan in the future. We discuss the concept of the mission object, our programmatic model as well as our prototype system, which we call eHermes.

  • 99.
    Jayaputera, Glenn T.
    et al.
    School of Computer Science & Software Engineering, Monash University Melbourne.
    Zaslavsky, Arkady
    Loke, Seng Wai
    School of Computer Science & Software Engineering, Monash University Melbourne.
    Watson, Nigel
    School of Computer Science & Software Engineering, Monash University Melbourne.
    An assembly and execution shell for multiagent systems2005Inngår i: Proceedings of the 38th Annual Hawaii International Conference on System Sciences : 3 - 6 January 2005, Big Island, Hawaii, [Hilton Waikoloa] ; abstracts and CD-ROM of full papers / [ed] Ralph H. Sprague, Los Alamitos, Calif: IEEE Computer Society Press , 2005, s. 72-Konferansepaper (Fagfellevurdert)
    Abstract [en]

    This paper presents a novel approach to design and develop an assembly and execution shell for multiagent applications based on the concept of a mission. A mission is a live evolving object that contains data, current variables, goals and evolution history of the mission itself. By maintaining the states and data inside the mission, it is possible to suspend the multiagent system's mission execution temporarily and resume when permitted. Our design allows run-time and dynamic agent generation depending on the complexity of the mission itself. We discuss the design and internal structure of the mission, the strategy and efficiency of the system in executing the tasks in the mission, and present our prototype system called eHermes.

  • 100.
    Jayaputera, Glenn Tesla
    et al.
    Clayton School of Information Technology, CSIT, Monash University.
    Loke, Seng Wai
    Department of Computer Science and Computer Engineering, La Trobe University.
    Zaslavsky, Arkady
    Generating and supporting dynamic heterogeneous MAS2006Inngår i: Proceedings for ICPS 2006, International Conference on Pervasive Services: Lyon, France, June 26 - 29, 2006, Piscataway, NJ: IEEE Communications Society, 2006, s. 78-87Konferansepaper (Fagfellevurdert)
    Abstract [en]

    One of the grand challenges in agent technology research is quasi-automatic development of heterogeneous and dynamic multiagent systems using Agent Oriented Software Engineering (AOSE) tools. This paper presents an innovative approach to developing and supporting multiagent systems at run-time. Multiagent systems (MASs) can and should be generated dynamically based on high-level user specifications which are transformed into a mission. Dynamically generating agents could also be offered as a pervasive service. Heterogeneity of MASs refers to diverse functionality and constituency of the system which include mobile as well as host associated software agents. This paper proposes and demonstrates on-demand and just-in-time agent generation approach which is combined with run-time support for MASs. Run-time support is based on mission cost-efficiency and objectives which enable termination, generation, injection and replacement of software agents as the mission evolves. We present the formal underpinning of our approach and describe the prototype tool - called eHermes, which has been implemented using specific agent platforms. Lessons and results of testing eHermes are reported and analyzed

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