28 research outputs found
Rail accessibility in Germany: Changing regional disparities between 1990 and 2020
Transport accessibility is an important location factor for households and firms. In the last few decades, technological and social developments have contributed to a reinvigorated role of passenger transport. However, rail accessibility is unevenly distributed in space. The introduction of high-speed rail has furthermore promoted a polarisation of accessibility between metropolises and peripheral areas in some European countries. In this article we analyse the development of rail accessibility at the regional level in Germany between 1990 and 2020 for 266 functional city-regions. Our results show two different facets: The number of regions that are directly connected to one another has decreased, but at the same time the spatial disparities of accessibility have decreased, albeit to a small extent. This development was strongest in East Germany after German reunification and thus largely a consequence of the renovation of the conventional rail infrastructure, not high-speed rail. Nevertheless, it can be concluded that the introduction of high-speed traffic in Germany did not lead to an increase in accessibility disparities. Instead, the accessibility effects of high-speed rail in Germany seem to break the traditional dichotomy between core and periphery.Verkehrliche Erreichbarkeit stellt einen wichtigen Standortfaktor für Haushalte und Unternehmen dar. In den letzten Jahrzehnten haben technologische und soziale Entwicklungen zu einer neuen Attraktivität des Schienenpersonenverkehrs beigetragen. Die Erreichbarkeit über den Schienenverkehr fällt jedoch räumlich sehr unterschiedlich aus. Die Einführung des Hochgeschwindigkeitsverkehrs hat zudem in einigen europäischen Ländern eine Polarisierung der Erreichbarkeit zwischen Metropolen und peripheren Räumen befördert. In diesem Beitrag analysieren wir die Entwicklung der Bahnerreichbarkeit auf regionaler Ebene in Deutschland zwischen 1990 und 2020 für 266 funktionale Stadtregionen. Unsere Ergebnisse zeigen zwei unterschiedliche Facetten: Die Zahl der direkt miteinander verbundenen Regionen hat sich verringert, aber zugleich zeigt sich für die Erreichbarkeit der Bevölkerung eine Abschwächung der räumlichen Disparitäten, wenn auch in geringem Maße. Diese Entwicklung war in Ostdeutschland nach der deutschen Wiedervereinigung am stärksten und damit weitgehend eine Folge der Sanierung der konventionellen Schieneninfrastruktur, nicht des Hochgeschwindigkeitsverkehrs. Dennoch kann der Schluss gezogen werden, dass seine Einführung in Deutschland nicht zur Erhöhung von Erreichbarkeitsdisparitäten geführt hat. Stattdessen scheinen die Erreichbarkeitswirkungen des Hochgeschwindigkeitsverkehrs in Deutschland die traditionelle Dichotomie zwischen Kern und Peripherie zu durchbrechen
A Multi-Agent Potential Field based approach for Real-Time Strategy Game bots
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
Multi-Agent Potential Field Based Architectures for Real-Time Strategy Game Bots
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
Potential-Field Based navigation in in StarCraft
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
Measuring player experience on runtime dynamic difficulty scaling in an RTS game
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
Locating transmembrane domains in protein sequences
We have developed a new approach for locating transmembrane domains in protein sequences based on hydrophobicity analysis and backpropagation neural network or k-nearest-neighbor as classifiers. Our system was able to locate over 98% of the transmembrane domains and the total accuracy including overpredictions was above 95%
Locating transmembrane domains in protein sequences
We have developed a new approach for locating transmembrane domains in protein sequences based on hydrophobicity analysis and backpropagation neural network or k-nearest-neighbor as classifiers. Our system was able to locate over 98% of the transmembrane domains and the total accuracy including overpredictions was above 95%
Dealing with Fog of War in a Real Time Strategy Game Environment
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
The Rise of Potential Fields in Real Time Strategy Bots
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