Dynamic real-time hierarchical heuristic search for pathfinding.

Movement of Units in Real-Time Strategy (RTS) Games is a non-trivial and challenging task mainly due to three factors which are constraints on CPU and memory usage, dynamicity of the game world, and concurrency. In this paper, we are focusing on finding a novel solution for solving the pathfinding problem in RTS Games for the units which are controlled by the computer. The novel solution combines two AI Planning approaches: Hierarchical Task Network (HTN) and Real-Time Heuristic Search (RHS). In the proposed solution, HTNs are used as a dynamic abstraction of the game map while RHS works as planning engine with interleaving of plan making and action executions. The article provides algorithmic details of the model while the empirical details of the model are obtained by using a real-time strategy game engine called ORTS (Open Real-time Strategy). The implementation of the model and its evaluation methods are in progress however the results of the automatic HTN creation are obtained for a small scale game map

Game State and Action Abstracting Monte Carlo Tree Search for General Strategy Game-Playing

When implementing intelligent agents for strategy games, we observe that search-based methods struggle with the complexity of such games. To tackle this problem, we propose a new variant of Monte Carlo Tree Search which can incorporate action and game state abstractions. Focusing on the latter, we developed a game state encoding for turn-based strategy games that allows for a flexible abstraction. Using an optimization procedure, we optimize the agent's action and game state abstraction to maximize its performance against a rule-based agent. Furthermore, we compare different combinations of abstractions and their impact on the agent's performance based on the Kill the King game of the Stratega framework. Our results show that action abstractions have improved the performance of our agent considerably. Contrary, game state abstractions have not shown much impact. While these results may be limited to the tested game, they are in line with previous research on abstractions of simple Markov Decision Processes. The higher complexity of strategy games may require more intricate methods, such as hierarchical or time-based abstractions, to further improve the agent's performance

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)

A cloud-based path-finding framework: Improving the performance of real-time navigation in games

This paper reviews current research in Cloud utilisation within games and finds that there is little beyond Cloud gaming and Cloud MMOs. To this end, a proof-of-concept Cloud-based Path-finding framework is introduced. This was developed to determine the practicality of relocating the computation for navigation problems from consumer-grade clients to powerful business-grade servers, with the aim of improving performance. The results gathered suggest that the solution might be impractical. However, because of the poor quality of the data, the results are largely inconclusive. Thus recommendations and questions for future research are posed.N/

A Bayesian Model for RTS Units Control applied to StarCraft

International audienceIn real-time strategy games (RTS), the player must reason about high-level strategy and planning while having effective tactics and even individual units micro-management. Enabling an artificial agent to deal with such a task entails breaking down the complexity of this environment. For that, we propose to control units locally in the Bayesian sensory motor robot fashion, with higher level orders integrated as perceptions. As complete inference encompassing global strategy down to individual unit needs is intractable, we embrace incompleteness through a hierarchical model able to deal with uncertainty. We developed and applied our approach on a StarCraft AI

Pathfinding in Games

Commercial games can be an excellent testbed to artificial intelligence (AI) research, being a middle ground between synthetic, highly abstracted academic benchmarks, and more intricate problems from real life. Among the many AI techniques and problems relevant to games, such as learning, planning, and natural language processing, pathfinding stands out as one of the most common applications of AI research to games. In this document we survey recent work in pathfinding in games. Then we identify some challenges and potential directions for future work. This chapter summarizes the discussions held in the pathfinding workgroup

A panorama of artificial and computational intelligence in games

This paper attempts to give a high-level overview of the field of artificial and computational intelligence (AI/CI) in games, with particular reference to how the different core research areas within this field inform and interact with each other, both actually and potentially. We identify ten main research areas within this field: NPC behavior learning, search and planning, player modeling, games as AI benchmarks, procedural content generation, computational narrative, believable agents, AI-assisted game design, general game artificial intelligence and AI in commercial games. We view and analyze the areas from three key perspectives: (1) the dominant AI method(s) used under each area; (2) the relation of each area with respect to the end (human) user; and (3) the placement of each area within a human-computer (player-game) interaction perspective. In addition, for each of these areas we consider how it could inform or interact with each of the other areas; in those cases where we find that meaningful interaction either exists or is possible, we describe the character of that interaction and provide references to published studies, if any. We believe that this paper improves understanding of the current nature of the game AI/CI research field and the interdependences between its core areas by providing a unifying overview. We also believe that the discussion of potential interactions between research areas provides a pointer to many interesting future research projects and unexplored subfields.peer-reviewe

Penerapan Algoritme Basic Theta* Pada Game Hexaconquest

Pada zaman sekarang, hampir semua game Turn-based Strategy memberikan fitur singleplayer pada tipe permainan yang dapat dilakukan pemainnya. Jika pemain manusia hanya satu orang, maka pemain lainnya harus digerakkan oleh komputer. Disinilah peran AI(Artificial Intelligence) atau kecerdasan buatan. Kecerdasan buatan digunakan pada game agar pemain manusia dapat merasa seakan-akan melawan manusia sehingga dia dapat melatih kemampuan bermainnya terlebih dahulu dengan melawan komputer sebelum melawan pemain manusia lain. Algoritme yang sering digunakan oleh AI pada game untuk mencari jalan terbaik menuju lokasi tujuannya adalah algoritme A*. Namun, tidak selalu A* merupakan solusi terbaik dalam pathfinding. Penulis mencoba menerapkan algoritme Basic Theta* pada game strategi berbasis giliran atau Turn-Based Strategy yang bernama Hexaconquest. Algoritme pathfinding Basic Theta* akan dibandingkan dengan algoritme pathfinding dasar pada game Hexaconquest yakni algoritme A*. Performa kedua algoritme akan dibandingkan dengan melihat jumlah frame per second, waktu eksekusi, dan jumlah cost node yang dilewati oleh agen algoritme. Dari hasil penelitian ini dapat disimpulkan bahwa algoritme Basic Theta* mampu mencari rute yang lebih pendek untuk setiap pergerakan agennya, namun performa algoritme ini masih kurang baik dibandingkan dengan performa algoritme A*. Algoritme Basic Theta* dapat memberikan solusi dengan jarak terpendek, sedangkan A* dapat memberikan solusi dengan lebih cepat dan ringan