11 research outputs found
Ms Pac-Man versus Ghost Team CEC 2011 competition
Games provide an ideal test bed for computational intelligence and significant progress has been made in recent years, most notably in games such as Go, where the level of play is now competitive with expert human play on smaller boards. Recently, a significantly more complex class of games has received increasing attention: real-time video games. These games pose many new challenges, including strict time constraints, simultaneous moves and open-endedness. Unlike in traditional board games, computational play is generally unable to compete with human players. One driving force in improving the overall performance of artificial intelligence players are game competitions where practitioners may evaluate and compare their methods against those submitted by others and possibly human players as well. In this paper we introduce a new competition based on the popular arcade video game Ms Pac-Man: Ms Pac-Man versus Ghost Team. The competition, to be held at the Congress on Evolutionary Computation 2011 for the first time, allows participants to develop controllers for either the Ms Pac-Man agent or for the Ghost Team and unlike previous Ms Pac-Man competitions that relied on screen capture, the players now interface directly with the game engine. In this paper we introduce the competition, including a review of previous work as well as a discussion of several aspects regarding the setting up of the game competition itself. © 2011 IEEE
Fast Approximate Max-n Monte Carlo Tree Search for Ms Pac-Man
We present an application of Monte Carlo tree search (MCTS) for the game of Ms Pac-Man. Contrary to most applications of MCTS to date, Ms Pac-Man requires almost real-time decision making and does not have a natural end state. We approached the problem by performing Monte Carlo tree searches on a five player maxn tree representation of the game with limited tree search depth. We performed a number of experiments using both the MCTS game agents (for pacman and ghosts) and agents used in previous work (for ghosts). Performance-wise, our approach gets excellent scores, outperforming previous non-MCTS opponent approaches to the game by up to two orders of magnitude. © 2011 IEEE
Ghost direction detection and other innovations for Ms. Pac-Man
Ms. Pac-Man was developed in the 1980s, becoming one of the most popular arcade games of its time. It still has a significant following today and has recently attracted the attention of artificial intelligence researchers, in part, due to the fact that the agent must react in real time in order to navigate its way through the maze. This pape
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 àrea 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 físicos 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 vídeo 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 raciocínio 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 nível de tempo de pesquisa e
consumo de memória, assim como obter caminhos visualmente mais suaves
Using influence maps with heuristic search to craft sneak-attacks in Starcraft
Real-Time Strategy (RTS) games have consistently been popular among AI researchers over
the past couple of decades due to their complexity and difficulty to play for both humans and
AI. A popular strategy in RTS games is a “Sneak-Attack,” where one player tries to maneuver
some of their units into the base of their enemy without being seen for as long as possible
to surprise their enemy and deal massive damage to their economy. This thesis introduces
a novel method for finding Sneak-Attack paths in StarCraft: Brood War by combining
influence maps with heuristic search. The combined system creates paths that can guide
units effectively - and automatically - into the enemy’s base, by avoiding enemy unit vision
and minimizing both travel distance and unit damage. For StarCraft, this involves guiding
a loaded transport ship to the enemy’s base to drop off units for attack. Our results show
that the new system performs better than direct paths across a variety of maps in terms of
total transport deaths, total damage taken, as well as the total time spent by the transport
within enemy vision. We then utilize this new system to demonstrate an alternate use: a
proof of concept for calculating building placements to defend against enemy sneak-attacks
Capso: A Multi-Objective Cultural Algorithm System To Predict Locations Of Ancient Sites
ABSTRACT
CAPSO: A MULTI-OBJECTIVE CULTURAL ALGORITHM SYSTEM TO PREDICT LOCATIONS OF ANCIENT SITES
by
SAMUEL DUSTIN STANLEY
August 2019
Advisor: Dr. Robert Reynolds
Major: Computer Science
Degree: Doctor of Philosophy
The recent archaeological discovery by Dr. John O’Shea at University of Michigan of prehistoric caribou remains and Paleo-Indian structures underneath the Great Lakes has opened up an opportunity for Computer Scientists to develop dynamic systems modelling these ancient caribou routes and hunter-gatherer settlement systems as well as the prehistoric environments that they existed in. The Wayne State University Cultural Algorithm team has been interested assisting Dr. O’Shea’s archaeological team by predicting new structures in the Alpena-Amberley Ridge Region.
To further this end, we developed a rule-based expert prediction system to work with our team’s dynamic model of the Paleolithic environment. In order to evolve the rules and thresholds within this expert system, we developed a Pareto-based multi-objective optimizer called CAPSO, which stands for Cultural Algorithm Particle Swarm Optimizer. CAPSO is fully parallelized and is able to work with modern multicore CPU architecture, which enables CAPSO to handle “big data” problems such as this one.
The crux of our methodology is to set up a biobjective problem with the objectives being locations predicted by the expert system (minimize) vs. training set occupational structures within those predicted locations (maximize). The first of these quantities plays the role of “cost” while the second plays the role of “benefit”. Four separate such biobjective problems are created, one for each of the four relevant occupational structure types (hunting blinds, drive lines, caches, and logistical camps). For each of these problems, when CAPSO tunes the system’s rules and thresholds, it changes which locations are predicted and hence also which structures are flagged. By repeatedly tuning the rules and thresholds, CAPSO creates a Pareto Front of locations predicted vs. structures predicted for each of the four occupational structure types.
Statistical analysis of these Pareto Fronts reveals that as the number of structures predicted (benefit) increases linearly, the number of locations predicted (cost) increases exponentially. This pattern is referred to in the dissertation as the Accelerating Cost Hypothesis (ACH). The ACH statistically holds for all four structure types, and is the result of imperfect information
Monte-Carlo Tree Search Algorithm in Pac-Man Identification of commonalities in 2D video games for realisation in AI (Artificial Intelligence)
The research is dedicated to the game strategy, which uses the Monte-Carlo Tree Search algorithm for the Pac-Man agent. Two main strategies were heavily researched for Pac-Man’s behaviour (Next Level priority) and HS (Highest Score priority). The Pacman game best known as STPacman is a 2D maze game that will allow users to play the game using artificial intelligence and smart features such as, Panic buttons (where players can activate on or off when they want and when they do activate it Pacman will be controlled via Artificial intelligence). A Variety of experiments were provided to compare the results to determine the efficiency of every strategy. A lot of intensive research was also put into place to find a variety of 2D games (Chess, Checkers, Go, etc.) which have similar functionalities to the game of Pac-Man. The main idea behind the research was to see how effective 2D games will be if they were to be implemented in the program (Classes/Methods) and how well would the artificial intelligence used in the development of STPacman behave/perform in a variety of different 2D games. A lot of time was also dedicated to researching an ‘AI’ engine that will be able to develop any 2D game based on the users submitted requirements with the use of a spreadsheet functionality (chapter 3, topic 3.3.1 shows an example of the spreadsheet feature) which will contain near enough everything to do with 2D games such as the parameters (The API/Classes/Methods/Text descriptions and more). The spreadsheet feature will act as a tool that will scan/examine all of the users submitted requirements and will give a rough estimation(time) on how long it will take for the chosen 2D game to be developed. It will have a lot of smart functionality and if the game is not unique like chess/checkers it will automatically recognize it and alert the user of it
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)