Implementation of 2D turn-based strategy game with AI

Práce se zabývá tvorbou strategické hry a umělé inteligence, která se ji naučíhrát. Je prozkoumán žánr strategických her, ale i důležité části pro umělouinteligenci. Dále práce analyzuje několik profesionálně vytvořených her a pro-gramů hrající tahové strategie. Závěrem hodnotí kvalitu jak implementovanéumělé inteligence, tak implementované hry a navrhuje možná zlepšení.The thesis deals with creating a strategy game and artificial intelligence learn-ing the game via self-play. It looks into a strategy game genre as well as im-portant components of artificial intelligence. It analyzes several examples ofreal-world games and programs playing strategy games. In the end, the thesisdiscusses the quality of implemented artificial intelligence but also the gameand suggests possible improvements

Optimization Methods for SIMLIB/C++ Simulation Library

Práce se věnuje metodám optimalizace parametrů simulačních modelů. Seznamuje se základy matematické optimalizace a jejím využitím v operačním výzkumu. Dále navrhuje rozšíření knihovny SIMLIB/C++ modulem pro optimalizační metody. Několik vybraných metod teoreticky popisuje, implementuje v jazyce C++, demonstruje jejich použití na několika příkladech a zhodnocuje jejich úspěšnost.This thesis addresses the topic of parametric optimization of simulation models. It introduces theoretical foundation of optimization and its uses in simulation analysis. Furthermore, it suggests the extension of SIMBLI/C++ library by module for optimization methods. Some of the chosen methods are then theoretically described, implemented in C++ language, demonstrates its uses and evaluates their success.

Detecting sets of linked key players in social networks

In der Analyse sozialer Netzwerke ist der Begriff der Gruppen-Betweenness eine Einheit, welche den Einfluss einer Gruppe innerhalb eines Netzwerks misst. Die Gruppen-Betweenness einer Teilmenge von Individuen in einem sozialen Netzwerk ist umso größer, je mehr kürzeste Pfade zwischen Paaren von anderen Personen im Netzwerk über Mitglieder der betrachteten Teilmenge verlaufen. Es gibt Algorithmen zur Bestimmung der Gruppen-Betweenness, und auch das Problem der Bestimmung einer Teilmenge gegebener Größe mit maximaler Gruppen-Betweenness wurde in der Literatur bereits behandelt. Das Ziel dieser Arbeit ist aber nicht nur eine Gruppe mit maximalen Gruppen-Betweenness Wert zu finden, sondern auch einen Algorithmus für die Suche nach Gruppen, in der jedes Mitglied mit jedem anderen verbunden ist (sogennante Cliquen), zu entwickeln. Da das Problem np-schwer ist, ist der entwickelte Algorithmus von metaheuristischer Natur. Zur Qualitätssicherung wurde nicht nur ein Algorithmus angewendet, sondern zwei unterschiedliche Techniken - Simulated Annealing (SA) und Genetischer Algorithmus (GA) - implementiert und verglichen. Im Zusammenhang mit dieser Problemstellung sind die besten Teilmengen eines Netzwerks nicht zulässig, das heißt sie stellen keine Clique dar. Daher wird ein geeigneter Ansatz benötigt um entweder unzulässige Lösungen wieder auszuscheiden (Penalty Methode) oder aber nur zulässige Lösungen zu generieren (Repair Methode). Die hier vorgestellten Algorithmen basieren auf zwei verschiedenen Penalty-Methoden. Die der Analyse zugrunde liegenden Daten sind sowohl von realweltlichen sozialen Netzwerken als auch von synthetisch erzeugten Daten abgeleitet.In Social Network Analysis (SNA) the concept of Group Betweenness Centrality (GBC) is a unit to measure the influence of a group within a network. It is defined as the more shortest paths of the network pass through a subset of individuals, the greater the betweenness of this subset of individuals is. There are algorithms for determining the Group Betweenness Centrality and also for determining a subset of given size with maximum GBC. However, the aim of this thesis is not only to find a group with maximum GBC, but also to develop an algorithm for finding a group in which every member is connected to every other member (also called clique). As the problem itself is np-hard the proposed algorithm is of a meta heuristic nature. For quality assessment not only one algorithm was implemented, instead the two techniques Simulated Annealing (SA) and Genetic Algorithm (GA) were applied and compared. Since most of the generated solutions are not feasible in the context of this problem statement, i.e. don't compose a clique, a suitable approach to either eliminate unacceptable solutions (penalty-function method) or only generate feasible solutions (repair function method), is required. The final algorithms work with two different penalty method approaches. The underlying data used in the analysis is derived from real-world data sets of social networks as well as from synthetically generated data

Solving the vehicle routing problem using hybrid cellular evolutionary algorithm

Problem usmjeravanja vozila (VRP) kompleksan je kombinatorički problem s kojim se svakodnevno susreću tvrtke koje obavljaju dostavu robe. Njegovim učinkovitim rješavanjem moguće je značajno smanjiti troškove dostave. Metaheurističkim metodama moguće je relativno brzo pronaći visoko kvalitetna rješenja. Stanični evolucijski algoritam metaheuristički je algoritam kod kojeg su jedinke iz populacije raspoređene unutar toroidalne mreže i mogu biti u interakciji samo sa obližnjim jedinkama. Podešavanjem selekcijskog pritiska moguće je postići odgovarajući omjer diverzifikacije i intenzifikacije koji je ključan za uspješnost algoritma. Hibridizacija postupkom pretraživanja velikog susjedstva ubrzava pronalazak visoko kvalitetnih rješenja. Razvijeni algoritam testiran je na nekoliko skupova ispitnih zadataka te na problemima dostave hrvatskih tvrtki. Rezultati ostvareni na ispitnim zadacima pokazuju da učinkovitost algoritma ne odstupa mnogo od najboljih poznatih algoritama za ovu vrstu problema, dok rezultati ostvareni na problemima hrvatskih tvrtki pokazuju da je primjenom algoritma moguće postići značajne uštede.Vehicle Routing Problem (VRP) is a complex combinatorial problem encountered daily by companies that are dealing with goods delivery. With its ecient solving it is possible to signicantly reduce the cost of delivery. Metaheuristic methods are capable of nding high-quality solutions in reasonable amount of time. The cellular evolutionary algorithm is a metaheuristic algorithm in which the individuals from the population are distributed within the toroidal grid and can interact only with nearby entities. By adjusting the selection pressure, it is possible to achieve the appropriate ratio of diversication and intensication that is crucial to the success of the algorithm. Hybridization by a large neighborhood search accelerates the nding of high quality solutions. The developed algorithm has been tested on several sets of benchmarks and on the delivery problems of Croatian companies. The results obtained on the benchmarks show that the eciency of the algorithm does not dier much from the best-known algorithms for this type of problem, while the results achieved on the problems of Croatian companies show that it is possible to achieve signicant savings by algorithm application

Solving the vehicle routing problem using hybrid cellular evolutionary algorithm

Problem usmjeravanja vozila (VRP) kompleksan je kombinatorički problem s kojim se svakodnevno susreću tvrtke koje obavljaju dostavu robe. Njegovim učinkovitim rješavanjem moguće je značajno smanjiti troškove dostave. Metaheurističkim metodama moguće je relativno brzo pronaći visoko kvalitetna rješenja. Stanični evolucijski algoritam metaheuristički je algoritam kod kojeg su jedinke iz populacije raspoređene unutar toroidalne mreže i mogu biti u interakciji samo sa obližnjim jedinkama. Podešavanjem selekcijskog pritiska moguće je postići odgovarajući omjer diverzifikacije i intenzifikacije koji je ključan za uspješnost algoritma. Hibridizacija postupkom pretraživanja velikog susjedstva ubrzava pronalazak visoko kvalitetnih rješenja. Razvijeni algoritam testiran je na nekoliko skupova ispitnih zadataka te na problemima dostave hrvatskih tvrtki. Rezultati ostvareni na ispitnim zadacima pokazuju da učinkovitost algoritma ne odstupa mnogo od najboljih poznatih algoritama za ovu vrstu problema, dok rezultati ostvareni na problemima hrvatskih tvrtki pokazuju da je primjenom algoritma moguće postići značajne uštede.Vehicle Routing Problem (VRP) is a complex combinatorial problem encountered daily by companies that are dealing with goods delivery. With its ecient solving it is possible to signicantly reduce the cost of delivery. Metaheuristic methods are capable of nding high-quality solutions in reasonable amount of time. The cellular evolutionary algorithm is a metaheuristic algorithm in which the individuals from the population are distributed within the toroidal grid and can interact only with nearby entities. By adjusting the selection pressure, it is possible to achieve the appropriate ratio of diversication and intensication that is crucial to the success of the algorithm. Hybridization by a large neighborhood search accelerates the nding of high quality solutions. The developed algorithm has been tested on several sets of benchmarks and on the delivery problems of Croatian companies. The results obtained on the benchmarks show that the eciency of the algorithm does not dier much from the best-known algorithms for this type of problem, while the results achieved on the problems of Croatian companies show that it is possible to achieve signicant savings by algorithm application

Solving the vehicle routing problem using hybrid cellular evolutionary algorithm

Problem usmjeravanja vozila (VRP) kompleksan je kombinatorički problem s kojim se svakodnevno susreću tvrtke koje obavljaju dostavu robe. Njegovim učinkovitim rješavanjem moguće je značajno smanjiti troškove dostave. Metaheurističkim metodama moguće je relativno brzo pronaći visoko kvalitetna rješenja. Stanični evolucijski algoritam metaheuristički je algoritam kod kojeg su jedinke iz populacije raspoređene unutar toroidalne mreže i mogu biti u interakciji samo sa obližnjim jedinkama. Podešavanjem selekcijskog pritiska moguće je postići odgovarajući omjer diverzifikacije i intenzifikacije koji je ključan za uspješnost algoritma. Hibridizacija postupkom pretraživanja velikog susjedstva ubrzava pronalazak visoko kvalitetnih rješenja. Razvijeni algoritam testiran je na nekoliko skupova ispitnih zadataka te na problemima dostave hrvatskih tvrtki. Rezultati ostvareni na ispitnim zadacima pokazuju da učinkovitost algoritma ne odstupa mnogo od najboljih poznatih algoritama za ovu vrstu problema, dok rezultati ostvareni na problemima hrvatskih tvrtki pokazuju da je primjenom algoritma moguće postići značajne uštede.Vehicle Routing Problem (VRP) is a complex combinatorial problem encountered daily by companies that are dealing with goods delivery. With its ecient solving it is possible to signicantly reduce the cost of delivery. Metaheuristic methods are capable of nding high-quality solutions in reasonable amount of time. The cellular evolutionary algorithm is a metaheuristic algorithm in which the individuals from the population are distributed within the toroidal grid and can interact only with nearby entities. By adjusting the selection pressure, it is possible to achieve the appropriate ratio of diversication and intensication that is crucial to the success of the algorithm. Hybridization by a large neighborhood search accelerates the nding of high quality solutions. The developed algorithm has been tested on several sets of benchmarks and on the delivery problems of Croatian companies. The results obtained on the benchmarks show that the eciency of the algorithm does not dier much from the best-known algorithms for this type of problem, while the results achieved on the problems of Croatian companies show that it is possible to achieve signicant savings by algorithm application