43 research outputs found
Game AI revisited
More than a decade after the early research efforts on the
use of artificial intelligence (AI) in computer games and the
establishment of a new AI domain the term “game AI” needs
to be redefined. Traditionally, the tasks associated with
game AI revolved around non player character (NPC) behavior at different levels of control, varying from navigation
and pathfinding to decision making. Commercial-standard
games developed over the last 15 years and current game
productions, however, suggest that the traditional challenges
of game AI have been well addressed via the use of sophisticated AI approaches, not necessarily following or inspired
by advances in academic practices. The marginal penetration of traditional academic game AI methods in industrial
productions has been mainly due to the lack of constructive communication between academia and industry in the
early days of academic game AI, and the inability of academic game AI to propose methods that would significantly
advance existing development processes or provide scalable
solutions to real world problems. Recently, however, there
has been a shift of research focus as the current plethora
of AI uses in games is breaking the non-player character AI
tradition. A number of those alternative AI uses have already shown a significant potential for the design of better
games.
This paper presents four key game AI research areas that
are currently reshaping the research roadmap in the game
AI field and evidently put the game AI term under a new
perspective. These game AI flagship research areas include
the computational modeling of player experience, the procedural generation of content, the mining of player data on
massive-scale and the alternative AI research foci for enhancing NPC capabilities.peer-reviewe
Player Modeling
Player modeling is the study of computational models of players in games. This includes the detection, modeling, prediction and expression of human player characteristics which are manifested
through cognitive, affective and behavioral patterns. This chapter introduces a holistic view of player modeling and provides a high level taxonomy and discussion of the key components of a player\u27s model. The discussion focuses on a taxonomy of approaches for constructing a player model, the available types of data for the model\u27s input and a proposed classification for the model\u27s output. The chapter provides also a brief overview of some promising applications and a discussion of the key challenges player modeling is currently facing which are linked to the input, the output and the computational model
Experience-driven procedural content generation (extended abstract)
Procedural content generation is an increasingly
important area of technology within modern human-computer
interaction with direct applications in digital games, the semantic
web, and interface, media and software design. The personalization
of experience via the modeling of the user, coupled with the
appropriate adjustment of the content according to user needs
and preferences are important steps towards effective and meaningful
content generation. This paper introduces a framework for
procedural content generation driven by computational models of
user experience we name Experience-Driven Procedural Content
Generation. While the framework is generic and applicable to
various subareas of human computer interaction, we employ
games as an indicative example of content-intensive software that
enables rich forms of interaction.The research was supported, in part, by the FP7 ICT projects
C2Learn (318480) and iLearnRW (318803).peer-reviewe
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Bayesian opponent modeling in adversarial game environments.
This thesis investigates the use of Bayesian analysis upon an opponent¿s behaviour in order to determine the desired goals or strategy used by a given adversary. A terrain analysis approach utilising the A* algorithm is investigated, where a probability distribution between discrete behaviours of an opponent relative to a set of possible goals is generated. The Bayesian analysis of agent behaviour accurately determines the intended goal of an opponent agent, even when the opponent¿s actions are altered randomly. The environment of Poker is introduced and abstracted for ease of analysis. Bayes¿ theorem is used to generate an effective opponent model, categorizing behaviour according to its similarity with known styles of opponent. The accuracy of Bayes¿ rule yields a notable improvement in the performance of an agent once an opponent¿s style is understood. A hybrid of the Bayesian style predictor and a neuroevolutionary approach is shown to lead to effective dynamic play, in comparison to agents that do not use an opponent model. The use of recurrence in evolved networks is also shown to improve the performance and generalizability of an agent in a multiplayer environment. These strategies are then employed in the full-scale environment of Texas Hold¿em, where a betting round-based approach proves useful in determining and counteracting an opponent¿s play. It is shown that the use of opponent models, with the adaptive benefits of neuroevolution aid the performance of an agent, even when the behaviour of an opponent does not necessarily fit within the strict definitions of opponent ¿style¿.Engineering and Physical Sciences Research Council (EPSRC
Languages of games and play: A systematic mapping study
Digital games are a powerful means for creating enticing, beautiful, educational, and often highly addictive interactive experiences that impact the lives of billions of players worldwide. We explore what informs the design and construction of good games to learn how to speed-up game development. In particular, we study to what extent languages, notations, patterns, and tools, can offer experts theoretical foundations, systematic techniques, and practical solutions they need to raise their productivity and improve the quality of games and play. Despite the growing number of publications on this topic there is currently no overview describing the state-of-the-art that relates research areas, goals, and applications. As a result, efforts and successes are often one-off, lessons learned go overlooked, language reuse remains minimal, and opportunities for collaboration and synergy are lost. We present a systematic map that identifies relevant publications and gives an overview of research areas and publication venues. In addition, we categorize research perspectives along common objectives, techniques, and approaches, illustrated by summaries of selected languages. Finally, we distill challenges and opportunities for future research and development
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 today’s 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, Dijkstra’s 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*, Dijkstra’s, 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 vídeo, o planeamento de movimento tem que ver com o movimento de
NPCs (“Non-Player Characters”, do inglês) 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 influência.
Os algoritmos de descoberta de caminhos lidam com o problema de encontrar um caminho
num grafo de pesquisa pesado, cujos nós representam localizações de um mapa
de um jogo, e cujas ligações (arestas) entre nós têm um custo/peso associado. Os
algoritmos de descoberta de caminhos mais utilizados em jogos são o A* e as suas variantes,
nomeadamente, o algoritmo de Dijkstra e o algoritmo de pesquisa do melhor
primeiro (“best-first search”, do inglês). Como se verá mais adiante, os algoritmos de
descoberta de caminhos referidos não permitem simular ou imitar o movimento natural
dos seres humanos, que geralmente não possui descontinuidades, i.e., o movimento é
suave mesmo quando há 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 alterações ao mapa de um jogo. Ou seja, estes algoritmos não são
adaptativos, pelo que não permitem lidar com alterações ao grafo durante a pesquisa
de um caminho em resultado de algum evento ocorrido no jogo (e.g., uma ponte que
ligava dois nós de um grafo foi destruída por um míssil).
Por outro lado, os campos de influência são uma técnica de planeamento de movimento
que não padece dos dois problemas acima referidos, i.e., os campos possibilitam um
movimento suave semelhante ao realizado pelo ser humano e são adaptativos. Como
se verá mais adiante, iremos recorrer a uma função real diferenciável para representar
o campo de influência associado a um mapa de um jogo como um somatório de
funções igualmente diferenciáveis, em que cada função está associada a um repulsor
ou a um atractor. A diferenciabilidade garante que não existem alterações abruptas
ao campo de influência; consequentemente, o movimento de qualquer NPC será 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
nós do caminho de forma a simular o movimento suave de um ser humano.
Além disso, dada a natureza das funções reais diferenciáveis que representam um campo
de influência, 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 influência é adaptativo, na medida
em que se adapta a alterações 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 influência ainda pode ter extremos locais, que, se alcançados,
impedirão um NPC de fugir desse local. O problema do extremo local nunca ocorre
nos algoritmos de descoberta de caminhos porque o nó de destino é o único mínimo
global da função de custo. Portanto, ao conjugar a função de custo com a função de
influência, o NPC nunca será retido num qualquer extremo local da função de influência,
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 influência tem como resultado algoritmos de planeamento de movimento
que resolvem em simultâneo os problemas dos algoritmos de descoberta de caminhos e
de campos de influência.
Como será demonstrado ao longo desta tese, é possível combinar campos de influência
e o algoritmo A*, o algoritmo de Dijkstra, e o algoritmo da pesquisa pelo melhor
primeiro, de modo a obter algoritmos híbridos que são adaptativos. Além disso, esses
algoritmos podem gerar caminhos suaves que se assemelham aos que são efetuados por
seres humanos, embora a suavidade de caminhos não seja o foco principal desta tese.
No entanto, nem sempre é possível realizar essa conjugação entre os campos de influência
e os algoritmos de descoberta de caminhos; um exemplo é o algoritmo de pesquisa
na franja (“fringe search”, do inglês), bem como o novo algoritmo de pesquisa proposto
nesta tese, que se designa por algoritmo de pesquisa pelo melhor vizinho primeiro (“best
neighbor first search”, do inglês)
A Decentralized Partially Observable Markov Decision Model with Action Duration for Goal Recognition in Real Time Strategy Games
Multiagent goal recognition is a tough yet important problem in many real time strategy games or simulation systems. Traditional modeling methods either are in great demand of detailed agents’ domain knowledge and training dataset for policy estimation or lack clear definition of action duration. To solve the above problems, we propose a novel Dec-POMDM-T model, combining the classic Dec-POMDP, an observation model for recognizer, joint goal with its termination indicator, and time duration variables for actions with action termination variables. In this paper, a model-free algorithm named cooperative colearning based on Sarsa is used. Considering that Dec-POMDM-T usually encounters multiagent goal recognition problems with different sorts of noises, partially missing data, and unknown action durations, the paper exploits the SIS PF with resampling for inference under the dynamic Bayesian network structure of Dec-POMDM-T. In experiments, a modified predator-prey scenario is adopted to study multiagent joint goal recognition problem, which is the recognition of the joint target shared among cooperative predators. Experiment results show that (a) Dec-POMDM-T works effectively in multiagent goal recognition and adapts well to dynamic changing goals within agent group; (b) Dec-POMDM-T outperforms traditional Dec-MDP-based methods in terms of precision, recall, and F-measure