Signifying the West: Colonialist Design in Age of Empires III: The WarChiefs

"Forward," the highlighted Tomahawk and Rider units respond as they move across the mapped territory of a hill to a treasure guarded by two bears they must now kill. The WarChiefs, an expansion of the Age of Empires III Real Time Strategy (RTS) game for the PC, uses both Western and Native representations in game mechanics, sound, image, text, and narrative. This paper compares Indigenous and Western perspectives of interactivity, narrative, and space and time in a close reading of single-player campaign Fire and Shadow. In doing so, this paper asks: How does The WarChiefs, and thus the RTS genre, signify colonialist design aesthetic

Robustness and Flexibility of GHOST

Dans les actes de AAAI Eleventh Conference on Artificial Intelligence and Interactive Digital EntertainmentInternational audienceGHOST is a framework to help game developers to model and implement their own optimization problems, or to simply instantiate a problem already encoded in GHOST. Previous works show that GHOST leads to high-quality solutions in some tens of milliseconds for three RTS-related problems: build order, wall-in placement and target selection. In this paper, we present two new problems in GHOST: pathfinding and resource allocation. The goal of this paper is to show the robustness of the framework, having very good results for a problem it is not designed for (pathfinding), and to show its flexibility, where it is easy to propose different models of the same problem (resource allocation problem)

Esports Enthusiasts and Gamers: Motivations, Behaviors, and Attitudes Towards Gambling

This study examined what the driving factors behind why people watch esports and play video games, and their views on casino gambling. This research takes into account several motivational models and theories for video game and media consumption, including the Uses and Gratifications Theory. In addition, motivations and behaviors in regards to gambling were also examined. Although there is plenty of research on gambling motivations, none looks primarily at how esports and video game enthusiasts in specific feel about gambling. In-depth Interviews were conducted on esports and video game enthusiasts to understand what they enjoy about esports and gaming, and what they like and don’t like about casino gaming. Results showed a wide range of motivations behind video game play, but challenge, skill, and socialization were the most common. For gameplay itself, people tended to really enjoy teamwork and collaboration. None of the participants gambled too often, and cited a lack of interactivity and value as primary reasons. One aspect of casino games that many found frustrating, is that their decisions seem to rarely have an impact on the outcome of a game, unlike video games. With video games, nearly each press of the button has a degree of significance. Casinos and casino game manufacturers alike should examine what it is that drives people to play video games and watch esports, and import those qualities into their casino gaming experience

Online Build-Order Optimization for Real-Time Strategy Agents Using Multi-Objective Evolutionary Algorithms

The investigation introduces a novel approach for online build-order optimization in real-time strategy (RTS) games. The goal of our research is to develop an artificial intelligence (AI) RTS planning agent for military critical decision- making education with the ability to perform at an expert human level, as well as to assess a players critical decision- making ability or skill-level. Build-order optimization is modeled as a multi-objective problem (MOP), and solutions are generated utilizing a multi-objective evolutionary algorithm (MOEA) that provides a set of good build-orders to a RTS planning agent. We de ne three research objectives: (1) Design, implement and validate a capability to determine the skill-level of a RTS player. (2) Design, implement and validate a strategic planning tool that produces near expert level build-orders which are an ordered sequence of actions a player can issue to achieve a goal, and (3) Integrate the strategic planning tool into our existing RTS agent framework and an RTS game engine. The skill-level metric we selected provides an original and needed method of evaluating a RTS players skill-level during game play. This metric is a high-level description of how quickly a player executes a strategy versus known players executing the same strategy. Our strategic planning tool combines a game simulator and an MOEA to produce a set of diverse and good build-orders for an RTS agent. Through the integration of case-base reasoning (CBR), planning goals are derived and expert build- orders are injected into a MOEA population. The MOEA then produces a diverse and approximate Pareto front that is integrated into our AI RTS agent framework. Thus, the planning tool provides an innovative online approach for strategic planning in RTS games. Experimentation via the Spring Engine Balanced Annihilation game reveals that the strategic planner is able to discover build-orders that are better than an expert scripted agent and thus achieve faster strategy execution times

UCT for tactical assault battles in real-time strategy games

We consider the problem of tactical assault planning in real-time strategy games where a team of friendly agents must launch an assault on an enemy. This problem offers many challenges including a highly dynamic and uncertain environment, multiple agents, durative actions, numeric attributes, and different optimization objectives. While the dynamics of this problem are quite complex, it is often possible to provide or learn a coarse simulation-based model of a tactical domain, which makes Monte-Carlo planning an attractive approach. In this thesis, we investigate the use of UCT, a recent Monte-Carlo planning algorithm for this problem. UCT has recently shown impressive successes in the area of games, particularly Go, but has not yet been considered in the context of multi-agent tactical planning. We discuss the challenges of adapting UCT to our domain and an implementation which allows for the optimization of user specified objective functions. We present an evaluation of our approach on a range of tactical assault problems with different objectives in the RTS game Wargus. The results indicate that our planner is able to generate superior plans compared to several baselines and a human player

Strategi Adaptif Kelompok di Permainan Taktik Menggunakan Goal-Oriented Action Planning

Meningkatnya kompleksitas permainan elektronik modern seirama dengan peningkatan kebutuhan akan agen cerdas yang dapat dibangun dengan mudah. Salah satu permainan elektronik yang membutuhkan agen cerdas tersebut adalah permainan real-time tactics (RTT). Dalam tipe permainan ini perencanaan aksi yang baik dapat membuat permainan yang menantang bagi pemain. Penelitian ini mengekplorasi kemungkinan penggunaan dari (GOAP) pada sebuah permainan RTT. Dengan menggunakan GOAP, dinamisme taktik dapat dibentuk dengan waktu penggunaan yang tidak terasa berat dalam permainan. ================================================================================================================ Along with improvement of modern electronic games, necessity of an intelligent agent that easily build is needed. One of electronic games that need good intelligent agent is realtime tactics. In this game type, good action planning is necessary to provide best experience to the player. We explore usage possibility of Goal-Oriented Action Planning (GOAP) in tactical game. Using GOAP, tactic dinamism can be provided with reasonable amount of runtime

Étude comparative de planificateurs appliqués au domaine des jeux vidéo

RÉSUMÉ L’utilisation de la planification dans une architecture de prise de décision d’un jeu vidéo est une technique récente et il existe peu de références démontrant son efficacité par rapport aux techniques usuelles. Notre étude consiste en une évaluation expérimentale de deux planificateurs, GOAP et HTN, dans l’élaboration d’agents autonomes dans un jeu de tir. L’objectif du projet de recherche est de déterminer s’il est avantageux ou non d’utiliser ces planificateurs selon les critères suivants : la qualité de l’agent, la qualité de la planification, la jouabilité et certains attributs de qualité non fonctionnels tels que les limitations comportementales,la facilité d’implémentation et la robustesse face aux changements de conception. Nous avons comparé les deux types de planificateurs à une technique usuelle, la machine à états finis. Les résultats obtenus montrent que les architectures utilisant la planification offrent une qualité d’agent supérieure à la machine à états finis, nécessitent en moyenne moins de temps de calcul, ne nécessitent pas de prévoir toutes les situations auxquelles l’agent fera face et sont plus robustes aux changements de conception. Toutefois, elles résultent en un agent moins réactif et l’implémentation de l’architecture GOAP est une tâche plus complexe que l’implémentation de la machine à états. Finalement, GOAP offre une qualité de l’agent légèrement supérieure à HTN, mais ce dernier est plus facile d’implémentation. En définitive, sans toutefois être sans inconvénient, les planificateurs possèdent des avantages à être utilisés dans une architecture de prise de décision d’un jeu de tir. Quant au planificateur le plus approprié, le choix devrait être réalisé en fonction des exigences spécifiques du projet.----------ABSTRACT The use of planning in a decision making architecture of a video game is a recent technique and there are few references demonstrating its effectiveness compared to conventional techniques. Our study provides an experimental evaluation of two planners, GOAP and HTN, in the development of autonomous agents in a shooting game. The objective of the project is to determine whether it is advantageous or not to use these planners based on the following criteria: quality of the agent, quality of planning, gameplay and some non-functional quality attributes such as behavioral limitations, ease of implementation and robustness to design changes. We compared the two types of planners to a conventional technique, the finite state machine. The results show that the planning architectures offer a superior quality of agent than the finite state machine, require less computing time, do not require to anticipate all situations that the agent will face and are more robust to changes in design. However, they result in a less reactive agent and the implementation of the GOAP architecture is a more complex task than implementing the state machine. Finally, GOAP provides a slightly superior agent quality than HTN, but the latter is easier to implement. Ultimately, though not without downsides, planners have advantages for use in a decision making architecture of a shooter. As for the most appropriate planner, the choice should be made according to specific project requirements

Automated planning for pathfinding in real-time strategy games

This thesis is focused on the design of a new path planning algorithm to solve path planning problems in dynamic, partially observable and real-time environments such as Real-Time Strategy(RTS) games. The emphasis is put on fast action selection motivating the use of Monte-Carlo planning techniques. Three main contributions are presented in this thesis. The first contribution is a Monte-Carlo planning technique, called MCRT, that performs selective action sampling and limits how many times a particular state-action pair is explored to balance the trade-off between exploration of new actions and exploitation of the current best action. The thesis also presents two variations of MCRT as the second contribution. The first variation of MCRT randomly selects an action as a sample at each state seen during the look-ahead search. The second variation, called MCRT-CAS, performs the selective action sampling using corridors. The third contribution is the design of four real-time path planners that exploit MCRT and its variations to solve path planning problems in real-time. Three of these planners are empirically evaluated using four standard pathfinding benchmarks (and over 1000 instances). Performance of these three planners is compared against two recent rival algorithms (Real-time D*-Lite (RTD) and Local Search Space-Learning Real-Time A* (LSS-LRTA)). These rival algorithms are based on real-time heuristic search. The results show that a variation of MOCART, called MOCART-CAS, performs action selection significantly faster than the rival planners. The fourth planner, called the MG-MOCART planner, is evaluated using a typical Real-Time Strategy game. The MG-MOCART planner can solve the path planning problems with multiple goals. This planner is compared against four rivals: Upper Confidence bounds applied to Trees (UCT), LSS-LRTA, Real-Time Dynamic Programming (RTDP) and a rapidly-exploring random tree (RRT) planner. The performance is measured using score and planning cost. The results show that the MG-MOCART planner performs better than its rival techniques with respect to score and planning cost.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Dienstekomposition in intelligenten Umgebungen basierend auf KI-Planung

In intelligenten Umgebungen wird das Zusammenspiel mehrerer Dienste benötigt, welches durch eine Dienstekomposition erzielt werden kann. KIPlanung ist eine Methode, dies umzusetzen. Im Rahmen der vorliegenden Arbeit wurde experimentell das Laufzeitverhalten von verschiedenen Planern untersucht. Daneben wurden die Möglichkeiten der Modellierung von Problemen der Dienstekomposition evaluiert, was zu einer Richtline für die verteilte Modellierung von Dienstbeschreibungen führte. Basierend auf den Erfahrungen wurde ein Composer entworfen und umgesetzt, der verschiedene Planer nutzen kann