6,919 research outputs found
Using a Cognitive Architecture for Opponent Target Prediction
One of the most important aspects of a compelling game AI is that it anticipates the playerâs actions and responds to them in a convincing manner. The first step towards doing this is to understand what the player is doing and predict their possible future actions. In this paper we show an approach where the AI system focusses on testing hypotheses made about the playerâs actions using an implementation of a cognitive architecture inspired by the simulation theory of mind. The application used in this paper is to predict the target that the player is heading towards, in an RTS-style game. We improve the prediction accuracy and reduce the number of hypotheses needed by using path planning and path clustering
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 sĂŁo 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 Ă© encontrar o caminho mais curto
entre dois nós. Um nó é um ponto no espaço onde um agente inteligente consegue navegar.
Agentes mĂłveis em mundos fĂsicos e virtuais sĂŁo uma componente chave para a simulação de
comportamento inteligente. Se um agente nĂŁo for capaz de navegar no ambiente que o rodeia
sem colidir com obstĂĄculos, nĂŁo aparenta ser inteligente. Consequentemente, pathfinding faz
parte das tarefas fundamentais de inteligencia artificial em vĂdeo jogos.
Algoritmos de pathfinding funcionam bem com agentes Ășnicos 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 dinĂąmicos. Pelo contrĂĄrio, os influence maps nĂŁo sĂŁo usados
no pathfinding. Influence maps sĂŁo uma tĂ©cnica de raciocĂnio espacial que ajudam agentes
inteligentes e jogadores a tomar decisÔes 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 decisÔes estratégicas ou tåticas. As decisÔes
estratégicas são baseadas em atingir uma meta global, por exemplo, a captura de uma zona
do inimigo e ganhar o jogo. DecisÔes tåticas são baseadas em acçÔes 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 nĂvel de tempo de pesquisa e
consumo de memĂłria, assim como obter caminhos visualmente mais suaves
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
A cloud-based path-finding framework: Improving the performance of real-time navigation in games
This paper reviews current research in Cloud utilisation within games and finds that there is little beyond Cloud gaming and Cloud MMOs. To this end, a proof-of-concept Cloud-based Path-finding framework is introduced. This was developed to determine the practicality of relocating the computation for navigation problems from consumer-grade clients to powerful business-grade servers, with the aim of improving performance. The results gathered suggest that the solution might be impractical. However, because of the poor quality of the data, the results are largely inconclusive. Thus recommendations and questions for future research are posed.N/
Symbolic Reasoning for Hearthstone
Trading-Card-Games are an interesting problem domain for Game AI, as they feature some challenges, such as highly variable game mechanics, that are not encountered in this intensity in many other genres. We present an expert system forming a player-level AI for the digital Trading-Card-Game Hearthstone. The bot uses a symbolic approach with a semantic structure, acting as an ontology, to represent both static descriptions of the game mechanics and dynamic game-state memories. Methods are introduced to reduce the amount of expert knowledge, such as popular moves or strategies, represented in the ontology, as the bot should derive such decisions in a symbolic way from its knowledge base. We narrow down the problem domain, selecting the relevant aspects for a play-to-win bot approach and comparing an ontology-driven approach to other approaches such as machine learning and case-based reasoning. Upon this basis, we describe how the semantic structure is linked with the game-state and how different aspects, such as memories, are encoded. An example will illustrate how the bot, at runtime, uses rules and queries on the semantic structure combined with a simple utility system to do reasoning and strategic planning. Finally, an evaluation is presented that was conducted by fielding the bot against the stock âExpertâ AI that Hearthstone is shipped with, as well as Human opponents of various skill levels in order to assess how well the bot plays. Evaluating how believable the bot reasons is assessed through a Pseudo-Turing test
Influence-based motion planning algorithms for games
In games, motion planning has to do with the motion of non-player characters (NPCs)
from one place to another in the game world. In todayâs video games there are two
major approaches for motion planning, namely, path-finding and influence fields.
Path-finding algorithms deal with the problem of finding a path in a weighted search
graph, whose nodes represent locations of a game world, and in which the connections
among nodes (edges) have an associated cost/weight. In video games, the most employed
pathfinders are A* and its variants, namely, Dijkstraâs algorithm and best-first
search. As further will be addressed in detail, the former pathfinders cannot simulate
or mimic the natural movement of humans, which is usually without discontinuities,
i.e., smooth, even when there are sudden changes in direction.
Additionally, there is another problem with the former pathfinders, namely, their lack
of adaptivity when changes to the environment occur. Therefore, such pathfinders
are not adaptive, i.e., they cannot handle with search graph modifications during path
search as a consequence of an event that happened in the game (e.g., when a bridge
connecting two graph nodes is destroyed by a missile).
On the other hand, influence fields are a motion planning technique that does not suffer
from the two problems above, i.e., they can provide smooth human-like movement and
are adaptive. As seen further ahead, we will resort to a differentiable real function to
represent the influence field associated with a game map as a summation of functions
equally differentiable, each associated to a repeller or an attractor. The differentiability
ensures that there are no abrupt changes in the influence field, consequently, the
movement of any NPC will be smooth, regardless if the NPC walks in the game world in
the growing sense of the function or not. Thus, it is enough to have a spline curve that
interpolates the path nodes to mimic the smooth human-like movement.
Moreover, given the nature of the differentiable real functions that represent an influence
field, the removal or addition of a repeller/attractor (as the result of the destruction
or the construction of a bridge) does not alter the differentiability of the global
function associated with the map of a game. That is to say that, an influence field is
adaptive, in that it adapts to changes in the virtual world during the gameplay.
In spite of being able to solve the two problems of pathfinders, an influence field may
still have local extrema, which, if reached, will prevent an NPC from fleeing from that
location. The local extremum problem never occurs in pathfinders because the goal
node is the sole global minimum of the cost function. Therefore, by conjugating the
cost function with the influence function, the NPC will never be detained at any local
extremum of the influence function, because the minimization of the cost function
ensures that it will always walk in the direction of the goal node. That is, the conjugation
between pathfinders and influence fields results in movement planning algorithms which, simultaneously, solve the problems of pathfinders and influence fields.
As will be demonstrated throughout this thesis, it is possible to combine influence fields
and A*, Dijkstraâs, and best-first search algorithms, so that we get hybrid algorithms
that are adaptive. Besides, these algorithms can generate smooth paths that resemble
the ones traveled by human beings, though path smoothness is not the main focus of
this thesis. Nevertheless, it is not always possible to perform this conjugation between
influence fields and pathfinders; an example of such a pathfinder is the fringe search
algorithm, as well as the new pathfinder which is proposed in this thesis, designated as
best neighbor first search.Em jogos de vĂdeo, o planeamento de movimento tem que ver com o movimento de
NPCs (âNon-Player Charactersâ, do inglĂȘs) de um lugar para outro do mundo virtual
de um jogo. Existem duas abordagens principais para o planeamento de movimento,
nomeadamente descoberta de caminhos e campos de influĂȘncia.
Os algoritmos de descoberta de caminhos lidam com o problema de encontrar um caminho
num grafo de pesquisa pesado, cujos nós representam localizaçÔes de um mapa
de um jogo, e cujas ligaçÔes (arestas) entre nĂłs tĂȘm um custo/peso associado. Os
algoritmos de descoberta de caminhos mais utilizados em jogos sĂŁo o A* e as suas variantes,
nomeadamente, o algoritmo de Dijkstra e o algoritmo de pesquisa do melhor
primeiro (âbest-first searchâ, do inglĂȘs). Como se verĂĄ mais adiante, os algoritmos de
descoberta de caminhos referidos nĂŁo permitem simular ou imitar o movimento natural
dos seres humanos, que geralmente nĂŁo possui descontinuidades, i.e., o movimento Ă©
suave mesmo quando hå mudanças repentinas de direcção.
A juntar a este problema, existe um outro que afeta os algoritmos de descoberta de
caminhos acima referidos, que tem que ver com a falta de adaptatividade destes algoritmos
face a alteraçÔes ao mapa de um jogo. Ou seja, estes algoritmos não são
adaptativos, pelo que não permitem lidar com alteraçÔes ao grafo durante a pesquisa
de um caminho em resultado de algum evento ocorrido no jogo (e.g., uma ponte que
ligava dois nĂłs de um grafo foi destruĂda por um mĂssil).
Por outro lado, os campos de influĂȘncia sĂŁo uma tĂ©cnica de planeamento de movimento
que nĂŁo padece dos dois problemas acima referidos, i.e., os campos possibilitam um
movimento suave semelhante ao realizado pelo ser humano e sĂŁo adaptativos. Como
se verå mais adiante, iremos recorrer a uma função real diferenciåvel para representar
o campo de influĂȘncia associado a um mapa de um jogo como um somatĂłrio de
funçÔes igualmente diferenciåveis, em que cada função estå associada a um repulsor
ou a um atractor. A diferenciabilidade garante que não existem alteraçÔes abruptas
ao campo de influĂȘncia; consequentemente, o movimento de qualquer NPC serĂĄ suave,
independentemente de o NPC caminhar no mapa de um jogo no sentido crescente ou
no sentido decrescente da função. Assim, basta ter uma curva spline que interpola os
nĂłs do caminho de forma a simular o movimento suave de um ser humano.
Além disso, dada a natureza das funçÔes reais diferenciåveis que representam um campo
de influĂȘncia, a remoção ou adição de um repulsor/atractor (como resultado da destruição
ou construção de uma ponte) não altera a diferenciabilidade da função global associada
ao mapa de um jogo. Ou seja, um campo de influĂȘncia Ă© adaptativo, na medida
em que se adapta a alteraçÔes que ocorram num mundo virtual durante uma sessão de
jogo.
Apesar de ser capaz de resolver os dois problemas dos algoritmos de descoberta de caminhos, um campo de influĂȘncia ainda pode ter extremos locais, que, se alcançados,
impedirĂŁo um NPC de fugir desse local. O problema do extremo local nunca ocorre
nos algoritmos de descoberta de caminhos porque o nĂł de destino Ă© o Ășnico mĂnimo
global da função de custo. Portanto, ao conjugar a função de custo com a função de
influĂȘncia, o NPC nunca serĂĄ retido num qualquer extremo local da função de influĂȘncia,
porque a minimização da função de custo garante que ele caminhe sempre na direção
do nó de destino. Ou seja, a conjugação entre algoritmos de descoberta de caminhos
e campos de influĂȘncia tem como resultado algoritmos de planeamento de movimento
que resolvem em simultĂąneo os problemas dos algoritmos de descoberta de caminhos e
de campos de influĂȘncia.
Como serĂĄ demonstrado ao longo desta tese, Ă© possĂvel combinar campos de influĂȘncia
e o algoritmo A*, o algoritmo de Dijkstra, e o algoritmo da pesquisa pelo melhor
primeiro, de modo a obter algoritmos hĂbridos que sĂŁo adaptativos. AlĂ©m disso, esses
algoritmos podem gerar caminhos suaves que se assemelham aos que sĂŁo efetuados por
seres humanos, embora a suavidade de caminhos nĂŁo seja o foco principal desta tese.
No entanto, nem sempre Ă© possĂvel realizar essa conjugação entre os campos de influĂȘncia
e os algoritmos de descoberta de caminhos; um exemplo Ă© o algoritmo de pesquisa
na franja (âfringe searchâ, do inglĂȘs), bem como o novo algoritmo de pesquisa proposto
nesta tese, que se designa por algoritmo de pesquisa pelo melhor vizinho primeiro (âbest
neighbor first searchâ, do inglĂȘs)
Enhancing automated red teaming with Monte Carlo Tree Search
This study has investigated novel Automated Red Teaming methods that support replanning. Traditional Automated Red Teaming (ART) approaches usually use evolutionary computing methods for evolving plans using simulations. A drawback of this method is the inability to change a teamâs strategy part way through a simulation. This study focussed on a Monte-Carlo Tree Search (MCTS) method in an ART environment that supports re-planning to lead to better strategy decisions and a higher average scor
Evolving Effective Micro Behaviors for Real-Time Strategy Games
Real-Time Strategy games have become a new frontier of artificial intelligence research. Advances in real-time strategy game AI, like with chess and checkers before, will significantly advance the state of the art in AI research. This thesis aims to investigate using heuristic search algorithms to generate effective micro behaviors in combat scenarios for real-time strategy games. Macro and micro management are two key aspects of real-time strategy games. While good macro helps a player collect more resources and build more units, good micro helps a player win skirmishes against equal numbers of opponent units or win even when outnumbered. In this research, we use influence maps and potential fields as a basis representation to evolve micro behaviors. We first compare genetic algorithms against two types of hill climbers for generating competitive unit micro management. Second, we investigated the use of case-injected genetic algorithms to quickly and reliably generate high quality micro behaviors. Then we compactly encoded micro behaviors including influence maps, potential fields, and reactive control into fourteen parameters and used genetic algorithms to search for a complete micro bot, ECSLBot. We compare the performance of our ECSLBot with two state of the art bots, UAlbertaBot and Nova, on several skirmish scenarios in a popular real-time strategy game StarCraft. The results show that the ECSLBot tuned by genetic algorithms outperforms UAlbertaBot and Nova in kiting efficiency, target selection, and fleeing. In addition, the same approach works to create competitive micro behaviors in another game SeaCraft. Using parallelized genetic algorithms to evolve parameters in SeaCraft we are able to speed up the evolutionary process from twenty one hours to nine minutes. We believe this work provides evidence that genetic algorithms and our representation may be a viable approach to creating effective micro behaviors for winning skirmishes in real-time strategy games
- âŠ