RTS Game Research Articles

Defense behavior of real time strategy games: comparison between HFSM and FSM

<span>RTS Game is one of the popular genre in PC gaming, which has been played by various type of players frequently. In RTS game, NPC Defense Building (Tower) has attacking behavior to the closest enemy without considering certain enemy parameters. This causes the NPC Tower to be more predictable by the opponent and easily defeated if NPC attacked by enemies in the group. Thus, this research simulates NPC Tower using Hierarchical Finite State Machine (HFSM) method compared with Finite State Machine (FSM). In this study, NPC Tower detects enemies by seeing at four parameters namely NPC Tower Health, Enemy's Health, Enemy Type, and Tower Distance to enemies. NPC Tower will attack the most dangerous enemy according to the ‘Degree of Danger’ parameter. Then use the decision-making logic of the rule-based system. The output of NPC Tower are three type of behaviors namely Aggressive Attacking, Regular Attacking, and Attack with Special Skill. From the test results of 3 NPC Tower, Kamandaka NPC Tower with HFSM method is winning 8.92% compare to Kamandaka Tower with FSM method. For Gayatri Tower NPC obtained equal results using both HFSM and FSM. Meanwhile, Adikara NPC with HFSM method is 4.62% superior to Adikara Tower with FSM method.</span>

Chain of command in autonomous cooperative agents for battles in real-time strategy games

This paper investigates incorporating chain of command in swarm intelligence of honey bees to create groups of ranked co-operative autonomous agents for an RTS game in to create and re-enact battle simulations. The behaviour of the agents are based on the foraging and defensive behaviours of honey bees, adapted to a human environment. The chain of command is implemented using a hierarchical decision model. The groups consist of multiple model-based reflex agents, with individual blackboards for working memory, with a colony level blackboard to mimic the foraging patterns and include commands received from ranking agents. An agent architecture and environment are proposed that allows for creation of autonomous cooperative agents. The behaviour of agents is then evaluated both mathematically and empirically using an adaptation of anytime universal intelligence test and agent believability metric.

Design of mobile augmented reality game based on image recognition

The work studied a complete set of development system for mobile augmented reality game based on combination of AR technology and RTS game. An effective image recognition strategy was proposed through SIFT feature matching algorithm. Integrated with cloud image recognition module, the response speed of image recognition module was improved by eliminating error matching point. Through optimization of the scheme and existing technologies, the improved image recognition algorithm was applied to the game system, realizing RTS game system on mobile intelligent terminal. The experimental results showed that the proposed method meets the requirement of augmented reality system in terms of efficiency and accuracy. In addition, the demand of users for information expansion can be satisfied by the method to some extent, which is more applicable than traditional augmented reality.

Coevolving Robust Build-Order Iterative Lists for Real-Time Strategy Games

We investigate and develop a coevolutionary approach to finding strong, robust build orders for real-time strategy games. Which units to produce and the order in which to produce them is one important aspect of real-time strategy gameplay. In real-time strategy games, creating plans to address unit production problems are called “build orders.” Our research compares build orders produced from a coevolutionary algorithm, genetic algorithm (GA), and hill climber (HC) to exhaustive search. GAs find the strongest build orders, while coevolution produces more robust build orders than a genetic algorithm or HC. Case injection into the coevolutionary teachset and population can be used to bias coevolution into producing build orders that beat specific opponents or play like specific players, while maintaining robustness. Finally, in this paper, we extend our representation by adding branching and iteration to the build-action sequence and show that this more complex representation enables coevolution to find stronger build orders. We believe this study is a start toward a promising approach for creating strong, robust build orders for RTS games.

Analysing the influence of the fitness function on genetically programmed bots for a real-time strategy game

Finding the global best strategy for an autonomous agent (bot) in a RTS game is a hard problem, mainly because the techniques applied to do this must deal with uncertainty and real-time planning in order to control the game agents. This work describes an approach applying a Genetic Programming (GP) algorithm to create the behavioural engine of bots able to play a simple RTS. Normally it is impossible to know in advance what kind of strategies will be the best in the most general case of this problem. So GP, which searches the general decision tree space, has been introduced and used successfully. However, it is not straightforward what fitness function would be the most convenient to guide the evolutionary process in order to reach the best solutions and also being less sensitive to the uncertainty present in the context of games. Thus, in this paper three different evaluation functions have been proposed, and a detailed analysis of their performance has been conducted. The paper also analyses several aspects of the obtained bots, in addition to their final performance on battles, such as the evolution of the decision trees (behavioural models) themselves, or the influence on the results of noise or uncertainty. The results show that a victory-based fitness, which prioritises the number of victories, contributes to generate better bots, on average, than other functions based on other numerical aspects of the battles, such as the number of resources gathered, or the number of units generated.

Mitologi Dalam Video Game: Pesan-pesan Politik Dalam Video Games Amerika Serikat

<p><em>Video Game, especially in Indonesia, has been long seeing as kid toy with minimum or no psychological impact to the player. This view is a serious mistake since video game able to transmit violence message to political message into their audience or player. Political message especially is very omnipresent in such game as Red Alert, Generals and others FPS or RTS games. The message on these games is higly political and contains political views the developer has. This writing tries to read the political messages on games like Red Alert and Counter Strike to find myth the developer create or believe and search out why these myths is present. Barthes analysis on semiotics were used to read not only the denotative meaning of the message, but also find the connotative message and finaly find the myths wrapped around the games.</em></p>

Heuristic Search Exploiting Non-additive and Unit Properties for RTS-game Unit Micromanagement

This paper presents an approach that integrates fuzzy integral and fast heuristic search for improving the quality of unit micromanagement in the popular RTS game StarCraft. Unit micromanagement, i.e., detailed control of units in combat, is one of the most challenging problems posed by RTS games and is often tackled with search algorithms such as Minimax or Alpha-Beta. Due to vast state and action spaces, the game tree is often very large, and search algorithms must rely on evaluation methods from a certain limited depth rather than exploring deeper into the tree. We therefore attempt to apply fuzzy integral and aim for an evaluation method with high accuracy in the search. To achieve this aim, we propose a new function that allows fuzzy integral to cope with not only non-additive properties but also unit properties in RTS games. Experimental results are reported at the end of this paper, showing that our approach outperforms an existing approach in terns of win rates in this domain.

Attractiveness of real time strategy games

Game refinement idea is a unique theory that has been proposed based on the uncertainty of game outcome. A game refinement measure was derived from the game information progress model and has been applied in the domains such as board games and sports games. The present challenge is to apply the game refinement theory in the domain of RTS games. To do so, we use StarCraft II as a testbed and introduce a concept of strategy tree in order to construct a game tree of a RTS game. Then, game refinement values are calculated and compared with other type of games. It is found that StarCraft II has a zone value of game refinement.

Geometrical vs topological measures for the evolution of aesthetic maps in a RTS game

This paper presents a procedural content generation (PCG) method that is able to generate aesthetic maps for a real-time strategy game. The maps were characterized based on either their geometrical properties or their topological measures (obtained in this latter case from the sphere-of-influence graph induced by each map). Using these features, a distance function between maps can be defined. This function is used in turn to determine how close/far each map generated by the PCG method (a self-adaptive evolutionary algorithm) is to a collection of maps which were taken initially to be aesthetic or non-aesthetic. This correspondence guided a multi-objective evolutionary approach whereby maps close to aesthetic maps and far to non-aesthetic maps are sought. Self-organizing maps are used to ascertain whether the so-generated maps naturally cluster together with aesthetic maps, as well as to provide a qualitative assessment of the ability of each set of features to characterize the latter.

On balance and dynamism in procedural content generation with self-adaptive evolutionary algorithms

We consider search-based procedural content generation in the context of Planet Wars, an RTS game. The objective of this work is to generate maps for the aforementioned game, that result in an interesting game-play. In order to characterize interestingness we focus on the properties of balance and dynamism. The former captures the fact that no player is overwhelmed by the opponent during the game, whereas the latter tries to model the fact that there is a lot of action during the game. To measure these properties on a given map, we conduct several games on them using top AI bots and collect statistics which are, in turn, used as inputs of a fuzzy rule base. This system is embedded within an evolutionary algorithm that features self-adaptation of mutation parameters as well as variable-length chromosomes (thus implying maps of different sizes). The experimentation focuses both on the optimization of balance and dynamism as stand-alone properties and in the analysis of the different tradeoffs attainable through them. To reach this goal a multi objective approach is used. We analyze both the usefulness of map-size self-adaptation in each scenario, as well as the properties of maps leading to different tradeoffs between dynamism and balance.

A Rough - Neuro Model for Classifying Opponent Behavior in Real Time Strategy Games

Real Time strategy games offer an environment where game AI is known to conduct actuality. One feature of realistic behavior in game AI is the ability to recognize the strategy of the opponent player. This is known as opponent modeling. In this paper, aclassification Rough-Neuro hybrid model of the RTS opponent player behavior process is proposed. As a mean to achieve better game performance, reduction of the agent decision space andbetter high-level winning of real-time strategy games. The Rough -Neuro methodology allows the classification model to some extent simulate opponent behavior in playing RTS games. The methodology incorporates a two -stage hybrid mechanism. Rough sets for reduction of relevant attributes and artificial neural networks forclassification opponent behavior during game playing. The proposed hybrid approach has been tested on an open source 3D RTS game called Glest. From our results we can deduce that the tactic may be successfully used for foretelling the demeanor of contender in the Glest game.

Heuristic Planning in Adversarial Dynamic Domains

Agents in highly dynamic adversarial domains, such as RTS games, must continually make time-critical decisions to adapt their behaviour to the changing environment. In such a context, the planning agent must consider his opponent's actions as uncontrollable, or at best influenceable. In general nondeterministic domains where there is no clear turn-taking protocol, most heuristic search methods to date do not explicitly reason about the opponent's actions when guiding the state space exploration towards goal or high-reward states. In contrast, we are investigating a domain-independent heuristic planning approach which reasons about the dynamics and uncontrollability of the opponent's behaviours in order to provide better guidance to the search process of the planner. Our planner takes as input the opponent's behaviours recognized by a plan recognition module and uses them to identify opponent's actions that lead to low-utility projected states. We believe such explicit heuristic reasoning about the potential behaviours of the opponent is crucial when planning in adversarial domains, yet is missing in today's planning approaches.

A Multi-Agent Potential Field-based Bot for a Full RTS Game Scenario

Computer games in general, and real time strategy games in particular is a challenging task for both AI research and game AI programmers. The player, or AI bot, must use its work- ers to gather resources. They must be spent wisely on struc- tures such as barracks or factories, mobile units such as sol- diers, workers and tanks. The constructed units can be used to explore the game world, hunt down the enemy forces and destroy the opponent buildings. We propose a multi-agent architecture based on artificial potential fields for a full real time strategy scenario. We validate the solution by participat- ing in a yearly open real time strategy game tournament and show that the bot, even though not using any form of path planning for navigation, is able to perform well and win the tournament.

Opponent modelling for case-based adaptive game AI

In previous work we introduced a novel approach to adaptive game AI that was focussed on the rapid and reliable adaptation to game circumstances. We named the approach ‘case-based adaptive game AI’. In the approach, domain knowledge required to adapt to game circumstances is gathered automatically by the game AI, and is exploited immediately (i.e., without trials and without resource-intensive learning) to evoke effective behaviour in a controlled manner in online play. In the research discussed in this article we investigate to what extent incorporating opponent modelling enhances the performance of case-based adaptive game AI. In our approach, models of the opponent players are generated automatically, on the basis of observations drawn from a multitude of games. We performed experiments that test the enhanced approach in an actual, complex RTS game, and observed that the effectiveness of case-based adaptive game AI increases significantly when opponent modelling is incorporated. From these results we may conclude that opponent modelling further improves the basis for implementation of case-based adaptive game AI in commercially available video games.