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Hagelbäck, Johan
Publications (7 of 7) Show all publications
Hagelbäck, J., Hilborn, O., Jercic, P., Johansson, S., Lindley, C., Svensson, J. & Wen, W. (2014). Psychophysiological Interaction and Empathic Cognition for Human-Robot Cooperative Work (PsyIntEC). In: Rohrbein, F.; Veiga, G.; Natale, C. (Ed.), Gearing Up and Accelerating Cross-Fertilization between Academic and Industrial Robotics Research in Europe: Technology Transfer Experiments from the ECHORD Project (pp. 283-299). Springer
Open this publication in new window or tab >>Psychophysiological Interaction and Empathic Cognition for Human-Robot Cooperative Work (PsyIntEC)
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2014 (English)In: 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.

Place, publisher, year, edition, pages
Springer, 2014
Keywords
human-robot interaction, psychophysiology, affective modeling, robotics
National Category
Human Aspects of ICT Computer Sciences
Identifiers
urn:nbn:se:bth-6590 (URN)10.1007/978-3-319-02934-4_14 (DOI)000329752400016 ()oai:bth.se:forskinfoB5643A721B059E9AC1257D6D0036DE33 (Local ID)978-3-319-02933-7 (ISBN)oai:bth.se:forskinfoB5643A721B059E9AC1257D6D0036DE33 (Archive number)oai:bth.se:forskinfoB5643A721B059E9AC1257D6D0036DE33 (OAI)
Available from: 2014-10-10 Created: 2014-10-10 Last updated: 2018-01-11Bibliographically approved
Julian, T., Preuss, M., Beume, N., Wessing, S., Hagelbäck, J., Georgios N., Y. & Corrado, G. (2013). Controllable procedural map generation via multiobjective evolution. Genetic Programming and Evolvable Machines, 14(2), 245-277
Open this publication in new window or tab >>Controllable procedural map generation via multiobjective evolution
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2013 (English)In: Genetic Programming and Evolvable Machines, ISSN 1389-2576, E-ISSN 1573-7632, Vol. 14, no 2, p. 245-277Article in journal (Refereed) Published
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.

Place, publisher, year, edition, pages
Springer, 2013
Keywords
Evolutionary computation, Multiobjective optimisation, Procedural content generation, Real-time strategy games, RTS, StarCraft
National Category
Software Engineering
Identifiers
urn:nbn:se:bth-6980 (URN)10.1007/s10710-012-9174-5 (DOI)000317007700005 ()oai:bth.se:forskinfo8C4ABDD4960A5F3AC1257B5F00422598 (Local ID)oai:bth.se:forskinfo8C4ABDD4960A5F3AC1257B5F00422598 (Archive number)oai:bth.se:forskinfo8C4ABDD4960A5F3AC1257B5F00422598 (OAI)
Available from: 2013-05-24 Created: 2013-05-02 Last updated: 2018-01-11Bibliographically approved
Sohaib, A. T., Qureshi, S., Hagelbäck, J., Hilborn, O. & Jerčić, P. (2013). Evaluating classifiers for emotion recognition using EEG. In: : . Paper presented at 7th International Conference on Foundations of Augmented Cognition. Las Vegas: Springer
Open this publication in new window or tab >>Evaluating classifiers for emotion recognition using EEG
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2013 (English)Conference paper, Published 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%.

Place, publisher, year, edition, pages
Las Vegas: Springer, 2013
Keywords
Emotion detection, Emotion recognition, Emotional state, Galvanic skin response, K nearest neighbor (KNN), Large datasets, Machine learning techniques, Picture system
National Category
Software Engineering
Identifiers
urn:nbn:se:bth-6852 (URN)10.1007/978-3-642-39454-6_53 (DOI)oai:bth.se:forskinfo4E725BBDBF0A9820C1257C2E003674CF (Local ID)9783642394539 (ISBN)oai:bth.se:forskinfo4E725BBDBF0A9820C1257C2E003674CF (Archive number)oai:bth.se:forskinfo4E725BBDBF0A9820C1257C2E003674CF (OAI)
Conference
7th International Conference on Foundations of Augmented Cognition
Note

Held as Part of 15th International Conference on Human-Computer Interaction, HCI International 2013

Available from: 2013-11-25 Created: 2013-11-25 Last updated: 2018-01-11Bibliographically approved
Preuss, M., Kozakowski, D., Hagelbäck, J. & Trautmann, H. (2013). Reactive strategy choice in StarCraft by means of Fuzzy Control. In: : . Paper presented at Conference on Computatonal Intelligence and Games (CIG). Niagara Falls: IEEE
Open this publication in new window or tab >>Reactive strategy choice in StarCraft by means of Fuzzy Control
2013 (English)Conference paper, Published 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.

Place, publisher, year, edition, pages
Niagara Falls: IEEE, 2013
Keywords
Best matching, Gameplay, Strategy choices
National Category
Computer Sciences
Identifiers
urn:nbn:se:bth-6732 (URN)10.1109/CIG.2013.6633627 (DOI)oai:bth.se:forskinfoC0D59DD2F3230131C1257CBA004BB17B (Local ID)9781467353113 (ISBN)oai:bth.se:forskinfoC0D59DD2F3230131C1257CBA004BB17B (Archive number)oai:bth.se:forskinfoC0D59DD2F3230131C1257CBA004BB17B (OAI)
Conference
Conference on Computatonal Intelligence and Games (CIG)
Available from: 2014-04-14 Created: 2014-04-14 Last updated: 2018-01-11Bibliographically approved
Hagelbäck, J. (2012). Multi-Agent Potential Field Based Architectures for Real-Time Strategy Game Bots. (Doctoral dissertation). Karlskrona: Blekinge Institute of Technology
Open this publication in new window or tab >>Multi-Agent Potential Field Based Architectures for Real-Time Strategy Game Bots
2012 (English)Doctoral 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.

Place, publisher, year, edition, pages
Karlskrona: Blekinge Institute of Technology, 2012. p. 178
Series
Blekinge Institute of Technology Doctoral Dissertation Series, ISSN 1653-2090 ; 2
Keywords
Games, Real-Time Strategy, Potential Fields, Multi-Agent Systems
National Category
Computer Sciences
Identifiers
urn:nbn:se:bth-00517 (URN)oai:bth.se:forskinfo0416D48C63481954C125794A00446E2A (Local ID)978-91-7295-223-2 (ISBN)oai:bth.se:forskinfo0416D48C63481954C125794A00446E2A (Archive number)oai:bth.se:forskinfo0416D48C63481954C125794A00446E2A (OAI)
Available from: 2012-09-18 Created: 2011-11-16 Last updated: 2018-01-11Bibliographically approved
Hagelbäck, J. (2012). Potential-Field Based navigation in in StarCraft. In: : . Paper presented at Computational Intelligence in Games (CIG). Granada
Open this publication in new window or tab >>Potential-Field Based navigation in in StarCraft
2012 (English)Conference paper, Published 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.

Place, publisher, year, edition, pages
Granada: , 2012
Keywords
potential fields, starcraft, pathfinding, agents, navigation
National Category
Computer Sciences
Identifiers
urn:nbn:se:bth-7111 (URN)oai:bth.se:forskinfo123430EDD41F2A41C1257ACB00388CE0 (Local ID)oai:bth.se:forskinfo123430EDD41F2A41C1257ACB00388CE0 (Archive number)oai:bth.se:forskinfo123430EDD41F2A41C1257ACB00388CE0 (OAI)
Conference
Computational Intelligence in Games (CIG)
Available from: 2012-12-10 Created: 2012-12-05 Last updated: 2018-01-11Bibliographically approved
Hagelbäck, J. & Johansson, S. J. (2009). A Multiagent Potential Field-Based Bot for Real-Time Strategy Games. International Journal of Computer Games Technology, 2009, Article ID 910819.
Open this publication in new window or tab >>A Multiagent Potential Field-Based Bot for Real-Time Strategy Games
2009 (English)In: International Journal of Computer Games Technology, ISSN 1687-7055, Vol. 2009, article id 910819Article in journal (Refereed) Published
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.

Place, publisher, year, edition, pages
Hindawi Publishing Corporation, 2009
Keywords
Agents, Swarm Intelligence, Emergent Behavior, Computer Games
National Category
Computer Sciences
Identifiers
urn:nbn:se:bth-8283 (URN)10.1155/2009/910819 (DOI)oai:bth.se:forskinfo9E0BB0AE1648AC12C12575220024E1AE (Local ID)oai:bth.se:forskinfo9E0BB0AE1648AC12C12575220024E1AE (Archive number)oai:bth.se:forskinfo9E0BB0AE1648AC12C12575220024E1AE (OAI)
Note

Open Access Journal Article ID 910819

Available from: 2012-09-18 Created: 2008-12-17 Last updated: 2018-01-11Bibliographically approved
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