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Jørgen Vinne Iversen

Bio: Jørgen Vinne Iversen is an academic researcher. The author has contributed to research in topics: Usability & Personalization. The author has an hindex of 1, co-authored 1 publications receiving 36 citations.

Papers
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01 Jan 2003
TL;DR: A conceptual software agent architecture for supporting users of mobile commerce services will be presented, including a peer-to-peer based collaborative filtering extension to support product and service recommendations and the proposed incremental classifier is shown to an order of magnitude faster than the other classifiers.
Abstract: Cyberspace plays an increasingly important role in people’s life due to its plentiful offering of services and information, e.g. the Word Wide Web, the Mobile Web and Online Games. However, the usability of cyberspace services is frequently reduced by its lack of customization according to individual needs and preferences.In this thesis we address the cyberspace customization issue by focusing on methods for user representation and prediction. Examples of cyberspace customization include delegation of user data and tasks to software agents, automatic pre-fetching, or pre-processing of service content based on predictions. The cyberspace service types primarily investigated are Mobile Commerce (e.g. news, finance and games) and Massively Multiplayer Online Games (MMOGs).First a conceptual software agent architecture for supporting users of mobile commerce services will be presented, including a peer-to-peer based collaborative filtering extension to support product and service recommendations.In order to examine the scalability of the proposed conceptual software agent architecture a simulator for MMOGs is developed. Due to their size and complexity, MMOGs can provide an estimated “upper bound” for the performance requirements of other cyberspace services using similar agent architectures.Prediction of cyberspace user behaviour is considered to be a classification problem, and because of the large and continuously changing nature of cyberspace services there is a need for scalable classifiers. This is handled by proposed classifiers that are incrementally trainable, support a large number of classes, and supports efficient decremental untraining of outdated classification knowledge, and are efficiently parallelized in order to scale well.Finally the incremental classifier is empirically compared with existing classifiers on: 1) general classification data sets, 2) user clickstreams from an actual web usage log, and 3) a synthetic game usage log from the developed MMOG simulator. The proposed incremental classifier is shown to an order of magnitude faster than the other classifiers, significantly more accurate than the naive bayes classifier on the selected data sets, and with insignificantly different accuracy from the other classifiers.The papers leading to this thesis have combined been cited more than 50 times in book, journal, magazine, conference, workshop, thesis, whitepaper and technical report publications at research events and universities in 20 countries. 2 of the papers have been applied in educational settings for university courses in Canada, Finland, France, Germany, Norway, Sweden and USA.

36 citations


Cited by
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Journal ArticleDOI
TL;DR: It is shown, that literature concerning the highest development stage, the DT, is scarce, whilst there is more literature about DM and DS.

1,250 citations

Journal ArticleDOI
20 Jan 2008
TL;DR: In this paper, a visualization approach based on classical multidimensional scaling (CMDS) and key graph is proposed for analyzing players' action behaviors in an online game where three player types according to Bartle's taxonomy are found, that is, achievers, explorers and socializers.
Abstract: We propose a visualization approach for analyzing players' action behaviors. The proposed approach consists of two visualization techniques: classical multidimensional scaling (CMDS) and Key Graph. CMDS is for discovering clusters of players who behave similarly. Key Graph is for interpreting action behaviors of players in a cluster of interest. In order to reduce the dimension of matrices used in computation of the CMDS input, we exploit a time-series reduction technique recently proposed by us. Our visualization approach is evaluated using log of an online game where three-player types according to Bartle's taxonomy are found, that is, achievers, explorers, and socializers.

50 citations

Book ChapterDOI
01 Sep 2004
TL;DR: The experimental results given in this paper show that Hidden Markov Models have higher recognition performance than the previous approach, especially for classification of players of different types but having similar action frequencies.
Abstract: In this paper, we describe our work on classification of players in Massively Multiplayer Online Games using Hidden Markov Models based on player action sequences. In our previous work, we have discussed a classification approach using a variant of Memory Based Reasoning based on player action frequencies. That approach, however, does not exploit time structures hidden in action sequences of the players. The experimental results given in this paper show that Hidden Markov Models have higher recognition performance than our previous approach, especially for classification of players of different types but having similar action frequencies.

28 citations

Proceedings ArticleDOI
21 Oct 2008
TL;DR: This paper compares a variety of strategies to persist changes of the game world and shows that a distance-based solution offers the scalability and efficiency required for large-scale games as well as offering error bounds and eliminating unnecessary updates associated with localized movement.
Abstract: The most important asset of a Massively Multiplayer Online Game is its world state, as it represents the combined efforts and progress of all its participants. Thus, it is extremely important that this state is not lost in case of server failures. Survival of the world state is typically achieved by making it persistent, e.g., by storing it in a relational database. The main challenge of this approach is to track the large volume of modifications applied to the world in real time. This paper compares a variety of strategies to persist changes of the game world. While critical events must be written synchronously to the persistent storage, a set of approximation strategies are discussed and compared that are suitable for events with low consistency requirements, such as player movements. An analysis to better understand the possible limitations and bottlenecks of these strategies is presented using experimental data from an MMOG research framework. Our analysis shows that a distance-based solution offers the scalability and efficiency required for large-scale games as well as offering error bounds and eliminating unnecessary updates associated with localized movement.

25 citations

Journal ArticleDOI
TL;DR: This conjecture is that in cases where an agent-based simulation is affected by the temporal communication locality between agents, and there is complex agent–environment interaction, the two services will considerably improve the overall performance of a simulation.

25 citations