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  • 1.
    Davidsson, Paul
    et al.
    Blekinge Institute of Technology, School of Computing.
    Hagelbäck, Johan
    Travelstart Nordic, SWE.
    Svensson, Kenny
    Ericsson AB, SWE.
    Comparing Approaches to Predict Transmembrane Domains in Protein Sequences2005Conference 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 sequences with the possibility to choose between different approaches 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.
    Hagelbäck, Johan
    Blekinge Institute of Technology, School of Engineering, Department of Systems and Software Engineering.
    A Multi-Agent Potential Field based approach for Real-Time Strategy Game bots2009Licentiate thesis, comprehensive summary (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.

  • 3.
    Hagelbäck, Johan
    Blekinge Institute of Technology, School of Computing.
    Multi-Agent Potential Field Based Architectures for Real-Time Strategy Game Bots2012Doctoral thesis, comprehensive summary (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 use workers to gather resources, which in turn is used for creating new buildings, training combat units and build upgrades and research. The game is won when all buildings of the opponents 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 of nine papers. The first four papers deal with navigation in dynamic game worlds, which can be very complex and resource demanding. Typically it is solved by using pathfinding algorithms. We investigate an alternative approach based on Artificial Potential Fields and show how a PF 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. In a paper 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. In the sixth paper we show how the bot can be extended and used in a full RTS scenario with base building and unit construction. This is followed by a paper where 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. The last two papers 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 opponents. 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.

  • 4.
    Hagelbäck, Johan
    Blekinge Institute of Technology, School of Computing.
    Potential-Field Based navigation in in StarCraft2012Conference 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.

  • 5.
    Hagelbäck, Johan
    et al.
    Blekinge Institute of Technology, Faculty of Computing, Department of Creative Technologies.
    Hilborn, Olle
    Blekinge Institute of Technology, Faculty of Computing, Department of Creative Technologies.
    Jercic, Petar
    Blekinge Institute of Technology, Faculty of Computing, Department of Creative Technologies.
    Johansson, Stefan
    Blekinge Institute of Technology, Faculty of Computing, Department of Creative Technologies.
    Lindley, Craig
    Svensson, Johan
    Blekinge Institute of Technology, Faculty of Computing, Department of Creative Technologies.
    Wen, Wei
    Blekinge Institute of Technology, Faculty of Computing, Department of Creative Technologies.
    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] Rohrbein, F.; Veiga, G.; Natale, C., Springer , 2014, 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.

  • 6.
    Hagelbäck, Johan
    et al.
    Blekinge Institute of Technology, School of Computing.
    Johansson, Stefan J.
    Blekinge Institute of Technology, School of Computing.
    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.

  • 7.
    Hagelbäck, Johan
    et al.
    Blekinge Institute of Technology, School of Computing.
    Johansson, Stefan J.
    Blekinge Institute of Technology, School of Computing.
    A Multiagent Potential Field-Based Bot for Real-Time Strategy Games2009In: International Journal of Computer Games Technology, 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.

  • 8.
    Hagelbäck, Johan
    et al.
    Blekinge Institute of Technology, School of Engineering, Department of Systems and Software Engineering.
    Johansson, Stefan J.
    Blekinge Institute of Technology, School of Computing.
    A Study on Human like Characteristics in Real Time Strategy Games2010Conference 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 singleplayer 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.

  • 9.
    Hagelbäck, Johan
    et al.
    Blekinge Institute of Technology, School of Engineering, Department of Systems and Software Engineering.
    Johansson, Stefan J.
    Blekinge Institute of Technology, School of Engineering, Department of Systems and Software Engineering.
    Dealing with Fog of War in a Real Time Strategy Game Environment2008Conference 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. 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 RTS game 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.

  • 10.
    Hagelbäck, Johan
    et al.
    Blekinge Institute of Technology, School of Computing.
    Johansson, Stefan J.
    Blekinge Institute of Technology, School of Computing.
    Measuring player experience on runtime dynamic difficulty scaling in an RTS game2009Conference 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.

  • 11.
    Hagelbäck, Johan
    et al.
    Blekinge Institute of Technology, School of Engineering, Department of Systems and Software Engineering.
    Johansson, Stefan J.
    Blekinge Institute of Technology, School of Engineering, Department of Systems and Software Engineering.
    The Rise of Potential Fields in Real Time Strategy Bots2008Conference 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.

  • 12.
    Hagelbäck, Johan
    et al.
    Blekinge Institute of Technology, School of Engineering, Department of Systems and Software Engineering.
    Johansson, Stefan J.
    Blekinge Institute of Technology, School of Engineering, Department of Systems and Software Engineering.
    Using Multi-agent Potential Fields in Real-time Strategy2008Conference 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 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 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 in using multi-agent potential field based solutions in RTS games.

  • 13. Julian, Togelius
    et al.
    Preuss, Mike
    Beume, Nicola
    Wessing, Simon
    Hagelbäck, Johan
    Blekinge Institute of Technology, School of Computing.
    Georgios N., Yannakakis
    Corrado, Grappiolo
    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]

    his 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.

  • 14. Preuss, Mike
    et al.
    Kozakowski, Daniel
    Hagelbäck, Johan
    Blekinge Institute of Technology, School of Computing.
    Trautmann, Heike
    Reactive strategy choice in StarCraft by means of Fuzzy Control2013Conference 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.

  • 15.
    Sohaib, Ahmad Tauseef
    et al.
    Blekinge Institute of Technology, School of Computing.
    Qureshi, Shahnawaz
    Blekinge Institute of Technology, School of Computing.
    Hagelbäck, Johan
    Blekinge Institute of Technology, School of Computing.
    Hilborn, Olle
    Blekinge Institute of Technology, School of Computing.
    Jerčić, Petar
    Blekinge Institute of Technology, School of Computing.
    Evaluating classifiers for emotion recognition using EEG2013Conference 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%.

  • 16. Togelius, Julian
    et al.
    Preuss, Mike
    Beume, Nicola
    Wessing, Simon
    Hagelbäck, Johan
    Blekinge Institute of Technology, School of Computing.
    Yannakakis, Georgios N.
    Multiobjective Exploration of the StarCraft Map Space2010Conference 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 StarCraft 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

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