467 research outputs found
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)
Toward Open-Set Text-Independent Speaker Identification in Tactical Communications
Abstract-We present the design and implementation of an open-set textindependent speaker identification system using genetic Learning Classifier Systems (LCS). We examine the use of this system in a real-number problem domain, where there is strong interest in its application to tactical communications. We investigate different encoding methods for representing real-number knowledge and study the efficacy of each method for speaker identification. We also identify several difficulties in solving the speaker identification problems with LCS and introduce new approaches to resolve the difficulties. Experimental results show that our system successfully learns 200 voice features at accuracies of 90% to 100% and 15,000 features to more than 80% for the closed-set problem, which is considered a strong result in the speaker identification community. The open-set capability is also comparable to existing numeric-based methods
Non-determinism in the narrative structure of video games
PhD ThesisAt the present time, computer games represent a finite interactive system. Even in their more experimental forms, the number of possible interactions between player and NPCs (non-player characters) and among NPCs and the game world has a finite number and is led by a deterministic system in which events can therefore be predicted. This implies that the story itself, seen as the series of events that will unfold during gameplay, is a closed system that can be predicted a priori. This study looks beyond this limitation, and identifies the elements needed for the emergence of a non-finite, emergent narrative structure. Two major contributions are offered through this research. The first contribution comes in the form of a clear categorization of the narrative structures embracing all video game production since the inception of the medium. In order to look for ways to generate a non-deterministic narrative in games, it is necessary to first gain a clear understanding of the current narrative structures implemented and how their impact on usersâ experiencing of the story. While many studies have observed the storytelling aspect, no attempt has been made to systematically distinguish among the different ways designers decide how stories are told in games. The second contribution is guided by the following research question: Is it possible to incorporate non-determinism into the narrative structure of computer games? The hypothesis offered is that non-determinism can be incorporated by means of nonlinear dynamical systems in general and Cellular Automata in particular
Recommended from our members
Excellentia Eminentia Effectio
"In these pages you will learn about the fascinating research endeavors that each of our faculty members is undertaking. We have divided their research into the broad categories of health, sustainability, information, and systems. While we recognize the imperfect nature of categorizing research that, by its very nature may be interdisciplinary or transdisciplinary, we nonetheless believe it will be helpful as a way to see the depth and breadth of our research endeavors within each grouping. As you read the profiles on these pages, I know you will begin to appreciate that, taken as a whole, the research spectrum at Columbia Engineering is exceptional and that, as our professors go about their work, they are at the cusp of making breakthroughs that will have a major impact on the way we live our lives today and tomorrow.
Using MapReduce Streaming for Distributed Life Simulation on the Cloud
Distributed software simulations are indispensable in the study of large-scale life models but often require the use of technically complex lower-level distributed computing frameworks, such as MPI. We propose to overcome the complexity challenge by applying the emerging MapReduce (MR) model to distributed life simulations and by running such simulations on the cloud. Technically, we design optimized MR streaming algorithms for discrete and continuous versions of Conwayâs life according to a general MR streaming pattern. We chose life because it is simple enough as a testbed for MRâs applicability to a-life simulations and general enough to make our results applicable to various lattice-based a-life models. We implement and empirically evaluate our algorithmsâ performance on Amazonâs Elastic MR cloud. Our experiments demonstrate that a single MR optimization technique called strip partitioning can reduce the execution time of continuous life simulations by 64%. To the best of our knowledge, we are the first to propose and evaluate MR streaming algorithms for lattice-based simulations. Our algorithms can serve as prototypes in the development of novel MR simulation algorithms for large-scale lattice-based a-life models.https://digitalcommons.chapman.edu/scs_books/1014/thumbnail.jp
Reinforcement Learning
Brains rule the world, and brain-like computation is increasingly used in computers and electronic devices. Brain-like computation is about processing and interpreting data or directly putting forward and performing actions. Learning is a very important aspect. This book is on reinforcement learning which involves performing actions to achieve a goal. The first 11 chapters of this book describe and extend the scope of reinforcement learning. The remaining 11 chapters show that there is already wide usage in numerous fields. Reinforcement learning can tackle control tasks that are too complex for traditional, hand-designed, non-learning controllers. As learning computers can deal with technical complexities, the tasks of human operators remain to specify goals on increasingly higher levels. This book shows that reinforcement learning is a very dynamic area in terms of theory and applications and it shall stimulate and encourage new research in this field
Posters-at-the-Capitol 2017 Program Booklet
Posters-at-the-Capitol 2017 Program Booklet
Contents:
Welcoming Remarks
Posters-at-the-Capitol Organizing Committee
Welcome Letter from Mr. Robert King
Schedule of Activities
Mezzanine Map
Participant Listings
Eastern Kentucky University
Kentucky State University
Kentucky Community & Technical College System
Morehead State University
Murray State University
Northern Kentucky University
University of Louisville
University of Kentucky
Western Kentucky University
Programs of Distinction
Student Abstract
Air Force Institute of Technology Research Report 2007
This report summarizes the research activities of the Air Force Institute of Technologyâs Graduate School of Engineering and Management. It describes research interests and faculty expertise; lists student theses/dissertations; identifies research sponsors and contributions; and outlines the procedures for contacting the school. Included in the report are: faculty publications, conference presentations, consultations, and funded research projects. Research was conducted in the areas of Aeronautical and Astronautical Engineering, Electrical Engineering and Electro-Optics, Computer Engineering and Computer Science, Systems and Engineering Management, Operational Sciences, Mathematics, Statistics and Engineering Physics
Multi-Agent Systems
A multi-agent system (MAS) is a system composed of multiple interacting intelligent agents. Multi-agent systems can be used to solve problems which are difficult or impossible for an individual agent or monolithic system to solve. Agent systems are open and extensible systems that allow for the deployment of autonomous and proactive software components. Multi-agent systems have been brought up and used in several application domains
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