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  • 1.
    Lindberg, Markus
    et al.
    Cognitive Science, Lund University, Lund, Sweden.
    Månsson, Kristian
    Cognitive Science, Lund University, Lund, Sweden.
    Johansson, Birger
    Cognitive Science, Lund University, Lund, Sweden.
    Balkenius, Christian
    Cognitive Science, Lund University, Lund, Sweden.
    Gulz, Agneta
    Cognitive Science, Lund University, Lund, Sweden.
    Does a Robot Tutee Increase Children’s Engagement in a Learning-by-Teaching Situation?2017In: Intelligent Virtual Agents 17th International Conference, IVA 2017, Stockholm, Sweden, August 27-30, 2017, Proceedings / [ed] Jonas Beskow, Christopher Peters, Ginevra Castellano, Carol O'Sullivan, Iolanda Leite and Stefan Kopp, Cham: Springer, 2017, Vol. 10498, p. 243-246Conference paper (Refereed)
    Abstract [en]

    This paper presents initial attempts to combine a humanoid robot with the teachable agent approach. Several design choices are discussed, including the decision to use a robot instead of a virtual agent and which behaviours to implement in the robot. A pilot study explored how the interaction with a robot seemed to influence children’s engagement as well as their attribution of mental states to a robot and to a virtual agent. Eight children participated and the interaction was measured via an observational protocol and a conversational interview. A main outcome was large individual differences between the children’s interaction with the robot compared to the virtual agent.

  • 2.
    Ljung, Dennis
    et al.
    Linköping University, Department of Computer and Information Science, Database and information techniques. Linköping University, Faculty of Science & Engineering.
    Carlsson, Niklas
    Linköping University, Department of Computer and Information Science, Database and information techniques. Linköping University, Faculty of Science & Engineering.
    Lambrix, Patrick
    Linköping University, Department of Computer and Information Science, Database and information techniques. Linköping University, Faculty of Science & Engineering.
    Player Pairs Valuation in Ice Hockey2018In: Proceedings of the 5th Workshop on Machine Learning and Data Mining for Sports Analytics: co-located with 2018 European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD 2018) / [ed] Ulf Brefeld, Jesse Davis, Jan Van Haaren, Albrecht Zimmermann, Cham: Springer, 2018, Vol. 11330, p. 82-92Conference paper (Refereed)
    Abstract [en]

    To overcome the shortcomings of simple metrics for evaluating player performance, recent works have introduced more advanced metrics that take into account the context of the players’ actions and perform look-ahead. However, as ice hockey is a team sport, knowing about individual ratings is not enough and coaches want to identify players that play particularly well together. In this paper we therefore extend earlier work for evaluating the performance of players to the related problem of evaluating the performance of player pairs. We experiment with data from seven NHL seasons, discuss the top pairs, and present analyses and insights based on both the absolute and relative ice time together.

  • 3.
    Präntare, Fredrik
    et al.
    Linköping University, Department of Computer and Information Science, Artificial Intelligence and Integrated Computer Systems. Linköping University, Faculty of Science & Engineering.
    Heintz, Fredrik
    Linköping University, Department of Computer and Information Science, Artificial Intelligence and Integrated Computer Systems. Linköping University, Faculty of Science & Engineering.
    An Anytime Algorithm for Simultaneous Coalition Structure Generation and Assignment2018In: PRIMA 2018: Principles and Practice of Multi-Agent Systems: 21st International Conference, Tokyo, Japan, October 29-November 2, 2018, Proceedings / [ed] Tim Miller, Nir Oren, Yuko Sakurai, Itsuki Noda, Bastin Tony Roy Savarimuthu and Tran Cao Son, Cham, 2018, Vol. 11224, p. 158-174Conference paper (Refereed)
    Abstract [en]

    A fundamental problem in artificial intelligence is how to organize and coordinate agents to improve their performance and skills. In this paper, we consider simultaneously generating coalitions of agents and assigning the coalitions to independent tasks, and present an anytime algorithm for the simultaneous coalition structure generation and assignment problem. This optimization problem has many real-world applications, including forming goal-oriented teams of agents. To evaluate the algorithm’s performance, we extend established methods for synthetic problem set generation, and benchmark the algorithm against CPLEX using randomized data sets of varying distribution and complexity. We also apply the algorithm to solve the problem of assigning agents to regions in a major commercial strategy game, and show that the algorithm can be utilized in game-playing to coordinate smaller sets of agents in real-time.

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