| 1. |
- Hagelbäck, Johan, 1977-
(författare)
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Multi-Agent Potential Field based Architectures for Real-Time Strategy Game Bots
- 2012
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- 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.
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| 2. |
- Hagelbäck, Johan, 1977-, et al.
(författare)
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Psychophysiological Interaction and Empathic Cognition for Human-robot Cooperative Work (PsyIntEC)
- 2014
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Ingår i: Gearing Up and Accelerating Cross‐fertilization between Academic and Industrial Robotics Research in Europe. - Cham : Springer. - 9783319038377 - 9783319038384 ; , s. 283-299
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Bokkapitel (refereegranskat)abstract
- 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.
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| 3. |
- Kastrati, Zenun, 1984-, et al.
(författare)
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The Effect of a Flipped Classroom in a SPOC : Students' Perceptions and Attitudes
- 2019
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Ingår i: ICETC 2019: Proceedings of the 2019 11th International Conference on Education Technology and Computers. - New York, NY, USA : ACM Publications. - 9781450372541 ; , s. 246-249
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Konferensbidrag (refereegranskat)abstract
- The advent of Massive Open Online Courses (MOOCs) and Small Private Online Courses (SPOCs) has brought opportunities to higher education institutions. Despite this, one of the main drawbacks of MOOCs and SPOCs has been relatively low retention rate of the registered students. Having this in mind in this paper we report our research efforts with a SPOC on Applied Machine Learning specifically tailored for professional students. More concretely, we report our findings with regard to the effects of the flipped classroom approach on the students' perceptions and attitudes. The initial results show that flipping the class had direct effects on students' knowledge and skills compared to a fully online class setting. These findings have offered complementary explanations of the survey regression analysis which revealed that course structure/instructional approach followed by course content are the main drivers in accounting for the variance in students' overall perceptions of the course.
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| 4. |
- Davidsson, Paul, et al.
(författare)
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Comparing approaches to predict transmembrane domains in protein sequences
- 2005
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Ingår i: ProceedingSAC '05 Proceedings of the 2005 ACM symposium on Applied computing. - New York, NY, USA : ACM Press. - 1581139640 ; , s. 185-189
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Konferensbidrag (refereegranskat)abstract
- 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.
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| 5. |
- Golub, Koraljka, et al.
(författare)
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Automatic Classification of Swedish Metadata Using Dewey Decimal Classification : A Comparison of Approaches
- 2020
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Ingår i: Journal of Data and Information Science. - : Walter de Gruyter GmbH. - 2096-157X .- 2543-683X. ; 5:1, s. 18-38
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Tidskriftsartikel (refereegranskat)abstract
- With more and more digital collections of various information resources 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 six machine learning algorithms as well as a string-matching algorithm based on characteristics of DDC. State-of-the-art machine learning algorithms require at least 1,000 training examples per class. The complete data set at the time of research involved 143,838 records which had to be reduced to top three hierarchical levels of DDC in order to provide sufficient training data (totaling 802 classes in the training and testing sample, out of 14,413 classes at all levels). Evaluation shows that Support Vector Machine with linear kernel outperforms other machine learning algorithms as well as the string-matching algorithm on average; the string-matching algorithm outperforms machine learning for specific classes when characteristics of DDC are most suitable for the task. Word embeddings combined with different types of neural networks (simple linear network, standard neural network, 1D convolutional neural network, and recurrent neural network) produced worse results than Support Vector Machine, but reach close results, with the benefit of a smaller representation size. Impact of features in machine learning shows that using keywords or combining titles and keywords gives better results than using only titles as input. Stemming only marginally improves the results. Removed stop-words reduced accuracy in most cases, while removing less frequent words increased it marginally. The greatest impact is produced by the number of training examples: 81.90% accuracy on the training set is achieved when at least 1,000 records per class are available in the training set, and 66.13% when too few records (often less than 100 per class) on which to train are available-and these hold only for top 3 hierarchical levels (803 instead of 14,413 classes). Having to reduce the number of hierarchical levels to top three levels of DDC because of the lack of training data for all classes, skews the results so that they work in experimental conditions but barely for end users in operational retrieval systems. In conclusion, for operative information retrieval systems applying purely automatic DDC does not work, either using machine learning (because of the lack of training data for the large number of DDC classes) or using string-matching algorithm (because DDC characteristics perform well for automatic classification only in a small number of classes). Over time, more training examples may become available, and DDC may be enriched with synonyms in order to enhance accuracy of automatic classification which may also benefit information retrieval performance based on DDC. In order for quality information services to reach the objective of highest possible precision and recall, automatic classification should never be implemented on its own; instead, machine-aided indexing that combines the efficiency of automatic suggestions with quality of human decisions at the final stage should be the way for the future. The study explored machine learning on a large classification system of over 14,000 classes which is used in operational information retrieval systems. Due to lack of sufficient training data across the entire set of classes, an approach complementing machine learning, that of string matching, was applied. This combination should be explored further since it provides the potential for real-life applications with large target classification systems.
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| 6. |
- Golub, Koraljka, et al.
(författare)
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Automatic classification using DDC on the Swedish Union Catalogue
- 2018
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Ingår i: Proceedings of the 18th European Networked Knowledge Organization Systems (NKOS 2018) Workshop, Porto, Portugal, September 13, 2018. - : CEUR-WS.org. ; 2200, s. 4-16
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Konferensbidrag (refereegranskat)abstract
- 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.
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| 7. |
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| 8. |
- Golub, Koraljka, et al.
(författare)
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Automatic subject classification of Swedish DDC : Impact of tuning and training data set
- 2019
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Ingår i: 19th European NKOS Workshop, 23rd TPDL. - : Networked Knowledge Organization Systems/Services/Structures, NKOS.
-
Konferensbidrag (refereegranskat)abstract
- The presentation builds on the NKOS 2018 presentation of automatically produced Dewey Decimal Classification (DDC) classes for Swedish union catalogue (LIBRIS). Based on a dataset of 143,838 records, Support Vector Machine with linear kernel outperforms Multinomial Naïve Bayes algorithm. Impact of features shows that using keywords or combining titles and keywords gives better results than using only titles as input. Stemming only marginally improves the results. Removed stop-words reduced accuracy in most cases, while removing less frequent words increased it marginally. Word embeddings combined with different types of neural networks (Simple linear network, Standard neural network, 1D convolutional neural network, Recurrent neural network) produced worse results than Naïve Bayes /Support Vector Machine, but reach close results. The greatest impact is produced by the number of training examples: 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.
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| 9. |
- Hagelbäck, Johan, 1977-
(författare)
-
A Multi-Agent Potential Field Based Approach for Real-Time Strategy Game Bots
- 2009
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Licentiatavhandling (övrigt vetenskapligt/konstnärligt)abstract
- 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.
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| 10. |
- Hagelbäck, Johan, 1977-, et al.
(författare)
-
A Multi-agent Potential Field based bot for a Full RTS Game Scenario
- 2009
-
Konferensbidrag (refereegranskat)abstract
- 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.
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