6 research outputs found
A scouting strategy for real-time strategy games
© 2014 ACM. Real-time strategy (RTS) is a sub-genre of strategy video games. RTS games are more realistic with dynamic and time-constraint game playing, by abandoning the turn-based rule of its ancestors. Playing with and against computer-controlled players is a pervasive phenomenon in RTS games, due to the convenience and the preference of groups of players. Hence, better game-playing agents are able to enhance game-playing experience by acting as smart opponents or collaborators. One-way of improving game-playing agents' performance, in terms of their economic-expansion and tactical battlefield-arrangement aspects, is to understand the game environment. Traditional commercial RTS game-playing agents address this issue by directly accessing game maps and extracting strategic features. Since human players are unable to access the same information, this is a form of "cheating AI", which has been known to negatively affect player experiences. Thus, we develop a scouting mechanism for RTS game-playing agents, in order to enable game units to explore game environments automatically in a realistic fashion. Our research is grounded in prior robotic exploration work by which we present a hierarchical multi-criterion decision-making (MCDM) strategy to address the incomplete information problem in RTS settings
Influence map-based pathfinding algorithms in video games
Path search algorithms, i.e., pathfinding algorithms, are used to solve shortest path problems
by intelligent agents, ranging from computer games and applications to robotics. Pathfinding
is a particular kind of search, in which the objective is to find a path between two nodes. A
node is a point in space where an intelligent agent can travel. Moving agents in physical or
virtual worlds is a key part of the simulation of intelligent behavior. If a game agent is not able
to navigate through its surrounding environment without avoiding obstacles, it does not seem
intelligent. Hence the reason why pathfinding is among the core tasks of AI in computer games.
Pathfinding algorithms work well with single agents navigating through an environment. In realtime
strategy (RTS) games, potential fields (PF) are used for multi-agent navigation in large
and dynamic game environments. On the contrary, influence maps are not used in pathfinding.
Influence maps are a spatial reasoning technique that helps bots and players to take decisions
about the course of the game. Influence map represent game information, e.g., events and
faction power distribution, and is ultimately used to provide game agents knowledge to take
strategic or tactical decisions. Strategic decisions are based on achieving an overall goal, e.g.,
capture an enemy location and win the game. Tactical decisions are based on small and precise
actions, e.g., where to install a turret, where to hide from the enemy.
This dissertation work focuses on a novel path search method, that combines the state-of-theart
pathfinding algorithms with influence maps in order to achieve better time performance and
less memory space performance as well as more smooth paths in pathfinding.Algoritmos de pathfinding são usados por agentes inteligentes para resolver o problema do caminho
mais curto, desde a à rea jogos de computador até à robótica. Pathfinding é um tipo
particular de algoritmos de pesquisa, em que o objectivo é encontrar o caminho mais curto
entre dois nós. Um nó é um ponto no espaço onde um agente inteligente consegue navegar.
Agentes móveis em mundos fÃsicos e virtuais são uma componente chave para a simulação de
comportamento inteligente. Se um agente não for capaz de navegar no ambiente que o rodeia
sem colidir com obstáculos, não aparenta ser inteligente. Consequentemente, pathfinding faz
parte das tarefas fundamentais de inteligencia artificial em vÃdeo jogos.
Algoritmos de pathfinding funcionam bem com agentes únicos a navegar por um ambiente. Em
jogos de estratégia em tempo real (RTS), potential fields (PF) são utilizados para a navegação
multi-agente em ambientes amplos e dinâmicos. Pelo contrário, os influence maps não são usados
no pathfinding. Influence maps são uma técnica de raciocÃnio espacial que ajudam agentes
inteligentes e jogadores a tomar decisões sobre o decorrer do jogo. Influence maps representam
informação de jogo, por exemplo, eventos e distribuição de poder, que são usados para
fornecer conhecimento aos agentes na tomada de decisões estratégicas ou táticas. As decisões
estratégicas são baseadas em atingir uma meta global, por exemplo, a captura de uma zona
do inimigo e ganhar o jogo. Decisões táticas são baseadas em acções pequenas e precisas, por
exemplo, em que local instalar uma torre de defesa, ou onde se esconder do inimigo.
Esta dissertação foca-se numa nova técnica que consiste em combinar algoritmos de pathfinding
com influence maps, afim de alcançar melhores performances a nÃvel de tempo de pesquisa e
consumo de memória, assim como obter caminhos visualmente mais suaves
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