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  • 1.
    Davidsson, Paul
    et al.
    Blekinge Institute of Technology, Sweden.
    Hagelbäck, Johan
    Travelstart Nordic, Sweden.
    Svensson, Kenny
    Ericsson, Sweden.
    Comparing approaches to predict transmembrane domains in protein sequences2005In: ProceedingSAC '05 Proceedings of the 2005 ACM symposium on Applied computing, ACM Press, 2005, p. 185-189Conference paper (Refereed)
    Abstract [en]

    There are today several systems for predicting transmembrane domains in membrane protein sequences. As they are based on different classifiers as well as different pre- and post-processing techniques, it is very difficult to evaluate the performance of the particular classifier used. We have developed a system called MemMiC for predicting transmembrane domains in protein se-quences with the possibility to choose between different ap-proaches to pre- and post-processing as well as different classifiers. Therefore it is possible to compare the performance of each classifier in a certain environment as well as the different approaches to pre- and post-processing. We have demonstrated the usefulness of MemMiC in a set of experiments, which shows, e.g., that the performance of a classifier is very dependent on which pre- and post-processing techniques are used.

  • 2.
    Golub, Koraljka
    et al.
    Linnaeus University, Faculty of Arts and Humanities, Department of Cultural Sciences.
    Hagelbäck, Johan
    Linnaeus University, Faculty of Technology, Department of computer science and media technology (CM).
    Ardö, Anders
    Lund University.
    Automatic classification using DDC on the Swedish Union Catalogue2018In: Proceedings of the 18th European Networked Knowledge Organization Systems (NKOS 2018) Workshop, Porto, Portugal, September 13, 2018 / [ed] Philipp Mayr, Douglas Tudhope, Joseph Busch, Koraljka Golub, Marjorie Hlava & Marcia Zeng, CEUR-WS.org , 2018, p. 4-16Conference paper (Refereed)
    Abstract [en]

    With more and more digital collections of various information re- sources becoming available, also increasing is the challenge of assigning subject index terms and classes from quality knowledge organization systems. While the ultimate purpose is to understand the value of automatically produced Dewey Decimal Classification (DDC) classes for Swedish digital collections, the paper aims to evaluate the performance of two machine learning algorithms for Swe- dish catalogue records from the Swedish union catalogue (LIBRIS). The algo- rithms are tested on the top three hierarchical levels of the DDC. Based on a data set of 143,838 records, evaluation shows that Support Vector Machine with linear kernel outperforms Multinomial Naïve Bayes algorithm. Also, using keywords or combining titles and keywords gives better results than using only titles as input. The class imbalance where many DDC classes only have few records greatly affects classification performance: 81.37% accuracy on the training set is achieved when at least 1,000 records per class are available, and 66.13% when few records on which to train are available. Proposed future research involves an exploration of the intellectual effort put into creating the DDC to further improve the algorithm performance as commonly applied in string matching, and to test the best approach on new digital collections that do not have DDC assigned.

  • 3.
    Golub, Koraljka
    et al.
    Linnaeus University, Faculty of Arts and Humanities, Department of Cultural Sciences.
    Hagelbäck, Johan
    Linnaeus University, Faculty of Technology, Department of computer science and media technology (CM).
    Ardö, Anders
    Automatic classification Using DDC on the Swedish Union Catalogue2019In: European DDC Users Group, EDUG, Annual Meeting 9-10 May 2019: National Library of Sweden, Stockholm, Sweden, 2019Conference paper (Other academic)
  • 4.
    Hagelbäck, Johan
    Blekinge Tekniska Högskola.
    A Multi-Agent Potential Field Based Approach for Real-Time Strategy Game Bots2009Licentiate thesis, monograph (Other academic)
    Abstract [en]

    Computer games in general and Real-Time Strategy (RTS) games in particular provide a rich challenge for both human- and computer controlled players, often denoted as bots. The player or bot controls a large number of units that have to navigate in partially unknown dynamic worlds to pursue a goal. Navigation in such worlds can be complex and require much computational resources. Typically it is solved by using some sort of path planning algorithm, and a lot of research has been conducted to improve the performance of such algorithms in dynamic worlds. The main goal of this thesis is to investigate an alternative approach for RTS bots based on Artificial Potential Fields, an area originating from robotics. In robotics the technique has successfully been used for navigation in dynamic environments, and we show that it is possible to use Artificial Potential Fields for navigation in an RTS game setting without any need of path planning.

    In the first three papers we define and demonstrate a methodology for creating multi-agent potential field based bots for an RTS game scenario where two tank armies battle each other. The fourth paper addresses incomplete information about the game world, referred to as the fog of war, and show how Potential Field based bots can handle such environments. The final paper shows how a Potential Field based bot can be evolved to handle a more complex full RTS scenario. It addresses resource gathering, construction of bases, technological development and construction of an army consisting of different types of units.

    We show that Artificial Potential Fields is a viable option for several RTS game scenarios and that the performance, both in terms of being able to win a game and computational resources used, can match and even surpass those of traditional approaches based on path planning.

  • 5.
    Hagelbäck, Johan
    Linnaeus University, Faculty of Technology, Department of Computer Science.
    Hybrid pathfinding in StarCraft2016In: IEEE Transactions on Computational Intelligence and AI in Games, ISSN 1943-068X, E-ISSN 1943-0698, Vol. 8, no 4, p. 319-324, article id 7063238Article in journal (Refereed)
    Abstract [en]

    Micro-management is a very important aspect of RTS games. It involves moving single units or groups of units effectively on the battle field, targeting the most threatening enemy units and use the unit's special abilities when they are the most harmful for the enemy or the most beneficial for the player. Designing good micro-management is a challenging task for AI bot developers. In this paper we address the micro-management sub-task of positioning units effectively in combat situations. Two different approaches are evaluated, one based on potential fields and the other based on flocking algorithms. The results show that both the potential fields version and the flocking version clearly increases the win percentage of the bot, but the difference in wins between the two is minimal. The results also show that the more flexible potential fields technique requires much more hardware resources than the more simple flocking technique.

  • 6.
    Hagelbäck, Johan
    Blekinge Tekniska Högskola.
    Multi-Agent Potential Field based Architectures for Real-Time Strategy Game Bots2012Doctoral thesis, monograph (Other academic)
    Abstract [en]

    Real-Time Strategy (RTS) is a sub-genre of strategy games which is running in real-time, typically in a war setting. The player uses workers to gather resources, which in turn are used for creating new buildings, training combat units, build upgrades and do research. The game is won when all buildings of the opponent(s) have been destroyed. The numerous tasks that need to be handled in real-time can be very demanding for a player. Computer players (bots) for RTS games face the same challenges, and also have to navigate units in highly dynamic game worlds and deal with other low-level tasks such as attacking enemy units within fire range.

    This thesis is a compilation grouped into three parts. The first part deals with navigation in dynamic game worlds which can be a complex and resource demanding task. Typically it is solved by using pathfinding algorithms. We investigate an alternative approach based on Artificial Potential Fields and show how an APF based navigation system can be used without any need of pathfinding algorithms.

    In RTS games players usually have a limited visibility of the game world, known as Fog of War. Bots on the other hand often have complete visibility to aid the AI in making better decisions. We show that a Multi-Agent PF based bot with limited visibility can match and even surpass bots with complete visibility in some RTS scenarios. We also show how the bot can be extended and used in a full RTS scenario with base building and unit construction.

    In the next section we propose a flexible and expandable RTS game architecture that can be modified at several levels of abstraction to test different techniques and ideas. The proposed architecture is implemented in the famous RTS game StarCraft, and we show how the high-level architecture goals of flexibility and expandability can be achieved.

    In the last section we present two studies related to gameplay experience in RTS games. In games players usually have to select a static difficulty level when playing against computer oppo- nents. In the first study we use a bot that during runtime can adapt the difficulty level depending on the skills of the opponent, and study how it affects the perceived enjoyment and variation in playing against the bot.

    To create bots that are interesting and challenging for human players a goal is often to create bots that play more human-like. In the second study we asked participants to watch replays of recorded RTS games between bots and human players. The participants were asked to guess and motivate if a player was controlled by a human or a bot. This information was then used to identify human-like and bot-like characteristics for RTS game players.

  • 7.
    Hagelbäck, Johan
    Blekinge Tekniska Högskola, Sweden.
    Potential-Field Based navigation in StarCraft2012In: 2012 IEEE Conference on Computational Intelligence and Games (CIG), IEEE conference proceedings, 2012, p. 388-393Conference paper (Refereed)
    Abstract [en]

    Real-Time Strategy (RTS) games are a sub-genre of strategy games typically taking place in a war setting. RTS games provide a rich challenge for both human- and computer players (bots). Each player has a number of workers for gathering resources to be able to construct new buildings, train additional workers, build combat units and do research to unlock more powerful units or abilities. The goal is to create a strong army and destroy the bases of the opponent(s). Armies usually consists of a large number of units which must be able to navigate around the game world. The highly dynamic and real-time aspects of RTS games make pathfinding a challenging task for bots. Typically it is handled using pathfinding algorithms such as A*, which without adaptions does not cope very well with dynamic worlds. In this paper we show how a bot for StarCraft uses a combination of A* and potential fields to better handle the dynamic aspects of the game.

  • 8.
    Hagelbäck, Johan
    et al.
    Blekinge Institute of Technology.
    Hilborn, Olle
    Blekinge Institute of Technology.
    Jercic, Petar
    Blekinge Institute of Technology.
    Johansson, Stefan J.
    Blekinge Institute of Technology.
    Lindley, Craig A.
    Intelligent Sensing Laboratory (CSIRO), Australia.
    Svensson, Johan
    Blekinge Institute of Technology.
    Wen, Wei
    Blekinge Institute of Technology.
    Psychophysiological Interaction and Empathic Cognition for Human-robot Cooperative Work (PsyIntEC)2014In: Gearing Up and Accelerating Cross‐fertilization between Academic and Industrial Robotics Research in Europe: Technology Transfer Experiments from the ECHORD Project / [ed] Florian Röhrbein, Germano Veiga, Ciro Natale, Springer, 2014, Vol. 94, p. 283-299Chapter in book (Refereed)
    Abstract [en]

    The aim of the PsyIntEC project is to explore affective and cognitive modeling of humans in human-robot interaction (HRI) as a basis for behavioral adaptation. To achieve this we have explored human affective perception of relevant modalities in human-human and human-robot interaction on a collaborative problem-solving task using psychophysiological measurements. The experiments conducted have given us valuable insight into the communicational and affective queues interplaying in such interactions from the human perspective. The results indicate that there is an increase in both positive and negative emotions when interacting with robots compared to interacting with another human or solving the task alone, but detailed analysis on shorter time segments is required for the results from all sensors to be conclusive and significant.

  • 9.
    Hagelbäck, Johan
    et al.
    Blekinge Institute of Technology, Sweden.
    Johansson, Stefan J.
    Blekinge Institute of Technology, Sweden.
    A Multi-agent Potential Field based bot for a Full RTS Game Scenario2009Conference paper (Refereed)
    Abstract [en]

    Computer games in general, and Real Time Strategy games in particular is a challenging task for both AI research and game AI programmers. The player, or AI bot, must use its workers to gather resources. They must be spent wisely on structures such as barracks or factories, mobile units such as soldiers, workers and tanks. The constructed units can be used to explore the game world, hunt down the enemy forces and destroy the opponent buildings. We propose a multi-agent architecture based on artificial potential fields for a full real time strategy scenario. We validate the solution by participating in a yearly open real time strategy game tournament and show that the bot, even though not using any form of path planning for navigation, is able to perform well and win the tournament.

  • 10.
    Hagelbäck, Johan
    et al.
    Blekinge Institute of Technology.
    Johansson, Stefan J.
    Blekinge Institute of Technology.
    A Multiagent Potential Field-Based Bot for Real-Time Strategy Games2009In: International Journal of Computer Games Technology, ISSN 1687-7047, E-ISSN 1687-7055, Vol. 2009, article id 910819Article in journal (Refereed)
    Abstract [en]

    Bots for real-time strategy (RTS) games may be very challenging to implement. A bot controls a number of units that will have to navigate in a partially unknown environment, while at the same time avoid each other, search for enemies, and coordinate attacks to fight them down. Potential fields are a technique originating from the area of robotics where it is used in controlling the navigation of robots in dynamic environments. Although attempts have been made to transfer the technology to the gaming sector, assumed problems with efficiency and high costs for implementation have made the industry reluctant to adopt it. We present a multiagent potential field-based bot architecture that is evaluated in two different real-time strategy game settings and compare them, both in terms of performance, and in terms of softer attributes such as configurability with other state-of-the-art solutions. We show that the solution is a highly configurable bot that can match the performance standards of traditional RTS bots. Furthermore, we show that our approach deals with Fog of War (imperfect information about the opponent units) surprisingly well. We also show that a multiagent potential field-based bot is highly competitive in a resource gathering scenario.

  • 11.
    Hagelbäck, Johan
    et al.
    Blekinge Tekniska Högskola, Sweden.
    Johansson, Stefan J.
    Blekinge Tekniska Högskola, Sweden.
    A Study on Human like Characteristics in Real Time Strategy Games2010In: 2010 IEEE Symposium on Computational Intelligence and Games (CIG), IEEE conference proceedings, 2010, p. 139-145Conference paper (Refereed)
    Abstract [en]

    Computer controlled characters (NPCs) are important in any video game to make the game world interesting, give more depth to a game and make the game playable. In almost any game the player has to cooperate with, fight against or interact with NPCs. This is especially true for single-player games but NPCs are also important in most multi-player games. When creating NPCs the developers often strive to create human like characters that behave reasonably intelligent in most cases. We have performed a study aiming to give an idea of the characteristics of human like NPCs in real-time strategy (RTS) games. In the study participants were asked to watch a recording of an RTS game and decide and motivate if the players in the game were controlled by a human player or a computer. We recorded matches were human players played against bots as well as bots playing against other bots. The results were categorized into different groups and they showed that some characteristics, for example simultaneous movement, are perceived as very bot-like and other things such as ability to try different tactics are perceived as humanlike.

  • 12.
    Hagelbäck, Johan
    et al.
    Blekinge Institute of Technology, Sweden.
    Johansson, Stefan J.
    Blekinge Institute of Technology, Sweden.
    Dealing with Fog of War in a Real Time Strategy Game Environment2008In: IEEE Symposium On Computational Intelligence and Games, 2008. CIG '08, IEEE Press, 2008, p. 55-62Conference paper (Refereed)
    Abstract [en]

    Bots for Real Time Strategy (RTS) games providea rich challenge to implement. A bot controls a number of units that may have to navigate in a partially unknown environment, while at the same time search for enemies and coordinate attacks to fight them down. It is often the case that RTS AIs cheat in the sense that they get perfect information about the game world to improve the performance of the tactics and planning behavior. We show how a multi-agent potential field based bot can be modified to play an RTSg ame without cheating, i.e. with incomplete information, and still be able to perform well without spending more resources than its cheating version in a tournament.

  • 13.
    Hagelbäck, Johan
    et al.
    Blekinge Institute of Technology, Sweden.
    Johansson, Stefan J.
    Blekinge Institute of Technology, Sweden.
    Demonstration of Multi-agent Potential Fields in Real-time Strategy Game2008In: Seventh International Conference on Autonomous Agents and Multi-agent Systems (AAMAS), May 12-16, 2008, Estoril, 2008, p. 1687-1688Conference paper (Other academic)
    Abstract [en]

    Bots for Real Time Strategy (RTS) games provide a rich challenge to implement. A bot controls a number of units that may have to navigate in a partially unknown environment, while at the same time search for enemies and coordinate attacks to fight them down. Potential fields is a technique originating from the area of robotics where it is used in controlling the navigation of robots in dynamic environments. Although attempts have been made to transfer the technology to the gaming sector, assumed problems with efficiency and high costs for implementation have made the industry reluctant to adopt it. Our demo shows the use of Multi-agent Potential Fields (MAPF) in an open source RTS game. We will demonstrate both the potential fields as such, and the coordination of the agents.

  • 14.
    Hagelbäck, Johan
    et al.
    Blekinge Institute of Technology.
    Johansson, Stefan J.
    Blekinge Institute of Technology.
    Measuring player experience on runtime dynamic difficulty scaling in an RTS game2009In: IEEE Symposium on Computational Intelligence and Games, 2009: CIG 2009, IEEE conference proceedings, 2009, p. 46-52Conference paper (Refereed)
    Abstract [en]

    Do players find it more enjoyable to win, than to play even matches? We have made a study of what a number of players expressed after playing against computer opponents of different kinds in an RTS game. There were two static computer opponents, one that was easily beaten, and one that was hard to beat, and three dynamic ones that adapted their strength to that of the player. One of these three latter ones intentionally drops its performance in the end of the game to make it easy for the player to win. Our results indicate that the players found it more enjoyable to play an even game against an opponent that adapts to the performance of the player, than playing against an opponent with static difficulty. The results also show that when the computer player that dropped its performance to let the player win was the least enjoyable opponent of them all.

  • 15.
    Hagelbäck, Johan
    et al.
    Blekinge Institute of Technology, Sweden.
    Johansson, Stefan J.
    Blekinge Institute of Technology, Sweden.
    The Rise of Potential Fields in Real Time Strategy bots2008In: Artificial Intelligence and Interactive Digital Entertainment (AIIDE), Stanford, Stanford University , 2008Conference paper (Refereed)
    Abstract [en]

    Bots for Real Time Strategy (RTS) games are challenging to implement. A bot controls a number of units that may have to navigate in a partially unknown environment, while at the same time search for enemies and coordinate attacks to fight them down. Potential fields is a technique originating from the area of robotics where it is used in controlling the navigation of robots in dynamic environments. We show that the use of potential fields for implementing a bot for a real time strategy game gives us a very competitive, configurable, and non-conventional solution.

  • 16.
    Hagelbäck, Johan
    et al.
    Blekinge Institute of Technology, Sweden.
    Johansson, Stefan J.
    Blekinge Institute of Technology, Sweden.
    Using Multi-Agent Potential Fields in Real-Time Strategy Games2008In: Seventh International Conference on Autonomous Agents and Multi-agent Systems (AAMAS), 12-16, 2008, Estoril, 2008, p. 631-638Conference paper (Refereed)
    Abstract [en]

    Bots for Real Time Strategy (RTS) games provide a rich challenge to implement. A bot controls a number of units that may have to navigate in a partially unknown environment, while at the same time search for enemies and coordinate attacks to fight them down. Potential fields is a technique originating from the area of robotics where it is used in controlling the navigation of robots in dynamic environments. Although attempts have been made to transfer thetechnology to the gaming sector, assumed problems with efficiency and high costs for implementation have made the industry reluctant to adopt it. We present a Multi-agent Potential Field based bot architecture that is evaluated in a real time strategy game setting and compare it, both in terms of performance, and in terms of softer attributes such as configurability with other state-of-the-art solutions. Although our solution did not reach the performance standards of traditional RTS bots in the test, we see great unexploited benefits inusing multi-agent potential field based solutions in RTS games.

  • 17.
    Hagelbäck, Johan
    et al.
    Linnaeus University, Faculty of Technology, Department of computer science and media technology (CM).
    Liapota, Pavlo
    Softwerk AB.
    Lincke, Alisa
    Linnaeus University, Faculty of Technology, Department of computer science and media technology (CM).
    Löwe, Welf
    Linnaeus University, Faculty of Technology, Department of computer science and media technology (CM).
    Variants of Dynamic Time Warping and their Performance in Human Movement Assessment2019In: 21st International Conference on Artificial Intelligence (ICAI'19: July 29 - August 1, 2019, las Vegas, USA), CSREA Press, 2019Conference paper (Refereed)
    Abstract [en]

    The advent of commodity 3D sensor technology enabled, amongst other things, the efficient and effective assessment of human movements. Statistical and machine learning approaches map recorded movement instances to expert scores to train models for the automated assessment of new movements. However, there are many variations in selecting the approaches and setting the parameters for achieving good performance, i.e., high scoring accuracy and low response time. The present paper researches the design space and the impact of sequence alignment on accuracy and response time. More specifically, we introduce variants of Dynamic Time Warping (DTW) for aligning the phases of slow and fast movement instances and assess their effect on the scoring accuracy and response time. Results show that an automated stripping of leading and trailing frames not belonging to the movement (using one DTW variant) followed by an alignment of selected frames in the movements (based on another DTW variant) outperforms the original DTW and other suggested variants thereof. Since these results are independent of the selected learning approach and do not rely on the movement specifics, the results can help improving the performance of automated human movement assessment, in general.

  • 18.
    Hagelbäck, Johan
    et al.
    Linnaeus University, Faculty of Technology, Department of computer science and media technology (CM).
    Lincke, Alisa
    Linnaeus University, Faculty of Technology, Department of computer science and media technology (CM).
    Löwe, Welf
    Linnaeus University, Faculty of Technology, Department of computer science and media technology (CM).
    Rall, Eduard
    AIMO AB.
    On the Agreement of Commodity 3D Cameras2019In: 23rd International Conference on Image Processing, Computer Vision, & Pattern Recognition (IPCV'19: July 29 - August 1, 2019, USA), CSREA Press, 2019Conference paper (Refereed)
    Abstract [en]

    The advent of commodity 3D sensor technol- ogy has, amongst other things, enabled the efficient and effective assessment of human movements. Machine learning approaches do not rely manual definitions of gold standards for each new movement. However, to train models for the automated assessments of a new movement they still need a lot of data that map recorded movements to expert judg- ments. As camera technology changes, this training needs to be repeated if a new camera does not agree with the old one. The present paper presents an inexpensive method to check the agreement of cameras, which, in turn, would allow for a safe reuse of trained models regardless of the cameras. We apply the method to the Kinect, Astra Mini, and Real Sense cameras. The results show that these cameras do not agree and that the models cannot be reused without an unacceptable decay in accuracy. However, the suggested method works independent of movements and cameras and could potentially save effort when integrating new cameras in an existing assessment environment.

  • 19.
    Jercic, Petar
    et al.
    Blekinge Tekniska Högskola.
    Wen, Wei
    Blekinge Tekniska Högskola.
    Hagelbäck, Johan
    Linnaeus University, Faculty of Technology, Department of Computer Science.
    Sundstedt, Veronica
    Blekinge Tekniska Högskola.
    The Effect of Emotions and Social Behavior on Performance in a Collaborative Serious Game Between Humans and Autonomous Robots2018In: International Journal of Social Robotics, ISSN 1875-4791, E-ISSN 1875-4805, Vol. 10, no 1, p. 115-129Article in journal (Refereed)
    Abstract [en]

    The aim of this paper is to investigate performance in a collaborative human–robot interaction on a shared serious game task. Furthermore, the effect of elicited emotions and perceived social behavior categories on players’ performance will be investigated. The participants collaboratively played a turn-taking version of the Tower of Hanoi serious game, together with the human and robot collaborators. The elicited emotions were analyzed in regards to the arousal and valence variables, computed from the Geneva Emotion Wheel questionnaire. Moreover, the perceived social behavior categories were obtained from analyzing and grouping replies to the Interactive Experiences and Trust and Respect questionnaires. It was found that the results did not show a statistically significant difference in participants’ performance between the human or robot collaborators. Moreover, all of the collaborators elicited similar emotions, where the human collaborator was perceived as more credible and socially present than the robot one. It is suggested that using robot collaborators might be as efficient as using human ones, in the context of serious game collaborative tasks.

  • 20.
    Jerčić, Petar
    et al.
    Blekinge Tekniska Högskola.
    Hagelbäck, Johan
    Linnaeus University, Faculty of Technology, Department of computer science and media technology (CM).
    Lindley, Craig
    CSIRO ICT Centre, Hobart, Australia.
    Physiological Affect and Performance in a Collaborative Serious Game Between Humans and an Autonomous Robot2018In: Entertainment Computing – ICEC 2018: 17th IFIP TC 14 International Conference, Held at the 24th IFIP World Computer Congress, WCC 2018, Poznan, Poland, September 17–20, 2018 / [ed] Clua E., Roque L., Lugmayr A., Tuomi P., Springer, 2018, p. 127-138Conference paper (Refereed)
    Abstract [en]

    This paper sets out to examine how elicited physiological affect influences the performance of human participants collaborating with the robot partners on a shared serious game task; furthermore, to investigate physiological affect underlying such human-robot proximate collaboration. The participants collaboratively played a turn-taking version of a serious game Tower of Hanoi, where physiological affect was investigated in a valence-arousal space. The arousal was inferred from the galvanic skin response data, while the valence was inferred from the electrocardiography data. It was found that the robot collaborators elicited a higher physiological affect in regard to both arousal and valence, in contrast to their human collaborator counterparts. Furthermore, a comparable performance between all collaborators was found on the serious game task.

  • 21.
    Preuss, Mike
    et al.
    TU Dortmund.
    Kozakowski, Daniel
    TU Dortmund.
    Hagelbäck, Johan
    Blekinge Tekniska Högskola.
    Trautmann, Heike
    Münster University.
    Reactive Strategy Choice in StarCraft by Means of Fuzzy Control2013In: 2013 IEEE Conference on Computational Intelligence in Games (CIG), IEEE conference proceedings, 2013, p. 1-8Conference paper (Refereed)
    Abstract [en]

    Current StarCraft bots are not very flexible in their strategy choice, most of them just follow a manually optimized one, usually a rush. We suggest a method of augmenting existing bots via Fuzzy Control in order to make them react on the current game situation. According to the available information, the best matching of a pool of strategies is chosen. While the method is very general and can be applied easily to many bots, we implement it for the existing BTHAI bot and show experimentally how the modifications affects its gameplay, and how it is improved compared to the original version.

  • 22.
    Qureshi, Shahnawaz
    et al.
    Prince of Songkla University, Thailand.
    Hagelbäck, Johan
    Linnaeus University, Faculty of Technology, Department of computer science and media technology (CM).
    Iqbal, Syed Muhammad Zeeshan
    BrightWare, Saudi Arabia.
    Javaid, Hamad
    Jinnah International Hospital, Pakistan.
    Lindley, Craig
    CSIRO ICT Centre, Australia.
    Evaluation of Classifiers for Emotion Detection while Performing Physical and Visual Tasks: Tower of Hanoi and IAPS2018In: Intelligent Systems and Applications. IntelliSys 2018: Proceedings of the 2018 Intelligent Systems Conference (IntelliSys) Volume 1 / [ed] Kohei Arai, Supriya Kapoor, Rahul Bhatia, Springer, 2018, p. 347-363Conference paper (Refereed)
    Abstract [en]

    With the advancement in robot technology, smart human-robot interaction is of increasing importance for allowing the more excellent use of robots integrated into human environments and activities. If a robot can identify emotions and intentions of a human interacting with it, interactions with humans can potentially become more natural and effective. However, mechanisms of perception and empathy used by humans to achieve this understanding may not be suitable or adequate for use within robots. Electroencephalography (EEG) can be used for recording signals revealing emotions and motivations from a human brain. This study aimed to evaluate different machine learning techniques to classify EEG data associated with specific affective/emotional states. For experimental purposes, we used visual (IAPS) and physical (Tower of Hanoi) tasks to record human emotional states in the form of EEG data. The obtained EEG data processed, formatted and evaluated using various machine learning techniques to find out which method can most accurately classify EEG data according to associated affective/emotional states. The experiment confirms the choice of a method for improving the accuracy of results. According to the results, Support Vector Machine was the first, and Regression Tree was the second best method for classifying EEG data associated with specific affective/emotional states with accuracies up to 70.00% and 60.00%, respectively. In both tasks, SVM was better in performance than RT. 

  • 23.
    Sohaib, Ahmad Tauseef
    et al.
    Blekinge Tekniska Högskola.
    Qureshi, Shahnawaz
    Blekinge Tekniska Högskola.
    Hagelbäck, Johan
    Blekinge Tekniska Högskola.
    Hilborn, Olle
    Blekinge Tekniska Högskola.
    Jercic, Petar
    Blekinge Tekniska Högskola.
    Evaluating classifiers for Emotion Recognition using EEG2013In: Foundations of Augmented Cognition: 7th International Conference, AC 2013, Held as Part of HCI International 2013, Las Vegas, NV, USA, July 21-26, 2013. Proceedings / [ed] Dylan D. Schmorrow & Cali M. Fidopiastis, Springer, 2013, Vol. Part IV, p. 492-501Conference paper (Refereed)
    Abstract [en]

    There are several ways of recording psychophysiology data from humans, for example Galvanic Skin Response (GSR), Electromyography (EMG), Electrocardiogram (ECG) and Electroencephalography (EEG). In this paper we focus on emotion detection using EEG. Various machine learning techniques can be used on the recorded EEG data to classify emotional states. K-Nearest Neighbor (KNN), Bayesian Network (BN), Artificial Neural Network (ANN) and Support Vector Machine (SVM) are some machine learning techniques that previously have been used to classify EEG data in various experiments. Five different machine learning techniques were evaluated in this paper, classifying EEG data associated with specific affective/emotional states. The emotions were elicited in the subjects using pictures from the International Affective Picture System (IAPS) database. The raw EEG data were processed to remove artifacts and a number of features were selected as input to the classifiers. The results showed that it is difficult to train a classifier to be accurate over large datasets (15 subjects) but KNN and SVM with the proposed features were reasonably accurate over smaller datasets (5 subjects) identifying the emotional states with an accuracy up to 77.78%.

  • 24.
    Togelius, Julian
    et al.
    IT University of Copenhagen.
    Preuss, Mike
    TU Dortmund.
    Beume, Nicola
    TU Dortmund.
    Wessing, Simon
    TU Dortmund.
    Hagelbäck, Johan
    Blekinge Tekniska Högskola, Sweden.
    Yannakakis, Georgios N.
    IT University Copenhagen.
    Multiobjective Exploration of the StarCraft Map Space2010In: 2010 IEEE Symposium on Computational Intelligence and Games (CIG), IEEE conference proceedings, 2010, p. 265-272Conference paper (Refereed)
    Abstract [en]

    This paper presents a search-based method for generating maps for the popular real-time strategy (RTS) game StarCraft. We devise a representation of StarCraft maps suitable for evolutionary search, along with a set of fitness functions based on predicted entertainment value of those maps, as derived from theories of player experience. A multiobjective evolutionary algorithm is then used to evolve complete Star- Craft maps based on the representation and selected fitness functions. The output of this algorithm is a Pareto front approximation visualizing the tradeoff between the several fitness functions used, and where each point on the front represents a viable map. We argue that this method is useful for both automatic and machine-assisted map generation, and in particular that the Pareto fronts are excellent design support tools for human map designers.

  • 25.
    Togelius, Julian
    et al.
    IT University of Copenhagen, Denmark.
    Preuss, Mike
    TU Dortmund, Germany.
    Beume, Nicola
    TU Dortmund, Germany.
    Wessing, Simon
    TU Dortmund, Germany.
    Hagelbäck, Johan
    Blekinge Tekniska Högskola, Sweden.
    Yannakakis, Georgios N.
    IT University Copenhagen, Denmark.
    Grappiolo, Corrado
    IT University of Copenhagen, Denmark.
    Controllable procedural map generation via multiobjective evolution2013In: Genetic Programming and Evolvable Machines, ISSN 1389-2576, E-ISSN 1573-7632, Vol. 14, no 2, p. 245-277Article in journal (Refereed)
    Abstract [en]

    This paper shows how multiobjective evolutionary algorithms can be used to procedurally generate complete and playable maps for real-time strategy (RTS) games. We devise heuristic objective functions that measure properties of maps that impact important aspects of gameplay experience. To show the generality of our approach, we design two different evolvable map representations, one for an imaginary generic strategy game based on heightmaps, and one for the classic RTS game StarCraft. The effect of combining tuples or triples of the objective functions are investigated in systematic experiments, in particular which of the objectives are partially conflicting. A selection of generated maps are visually evaluated by a population of skilled StarCraft players, confirming that most of our objectives correspond to perceived gameplay qualities. Our method could be used to completely automate in-game controlled map generation, enabling player-adaptive games, or as a design support tool for human designers.

1 - 25 of 25
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