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3. Combat Models for RTS Games

Author

Uriarte, Alberto and Ontañón, Santiago

Subjects
Computer Science - Artificial Intelligence
Abstract

Game tree search algorithms, such as Monte Carlo Tree Search (MCTS), require access to a forward model (or "simulator") of the game at hand. However, in some games such forward model is not readily available. This paper presents three forward models for two-player attrition games, which we call "combat models", and show how they can be used to simulate combat in RTS games. We also show how these combat models can be learned from replay data. We use StarCraft as our application domain. We report experiments comparing our combat models predicting a combat output and their impact when used for tactical decisions during a real game.

Published
2016

4. StarCraft Bots and Competitions

Author

Churchill, David, primary, Preuss, Mike, additional, Richoux, Florian, additional, Synnaeve, Gabriel, additional, Uriarte, Alberto, additional, Ontañnón, Santiago, additional, and Čertický, Michal, additional

Published
2016
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5. RTS AI Problems and Techniques

Author

Ontañón, Santiago, primary, Synnaeve, Gabriel, additional, Uriarte, Alberto, additional, Richoux, Florian, additional, Churchill, David, additional, and Preuss, Mike, additional

Published
2015
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17. Conversaciones en centros residenciales de personas mayores durante la covid-19: impacto en las profesionales de los cuidados.

Author

Lázaro, Raquel, Pinzón, Sandra, Díaz-Veiga, Pura, Castejón Villarejo, Penelope, Azurmendi, Maider, and Uriarte, Alberto

Subjects
COVID-19
Abstract

Copyright of Zerbitzuan is the property of Zerbitzuan and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Published
2020
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19. GHOST: A Combinatorial Optimization Solver for RTS-related Problems

Author

Richoux, Florian, Uriarte, Alberto, Baffier, Jean-François, Theory, Algorithms and Systems for Constraints (TASC), Inria Rennes – Bretagne Atlantique, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Département informatique - EMN, Mines Nantes (Mines Nantes)-Mines Nantes (Mines Nantes)-Laboratoire d'Informatique de Nantes Atlantique (LINA), Centre National de la Recherche Scientifique (CNRS)-Mines Nantes (Mines Nantes)-Université de Nantes (UN)-Centre National de la Recherche Scientifique (CNRS)-Université de Nantes (UN), Drexel University, The University of Tokyo (UTokyo), Laboratoire d'Informatique de Nantes Atlantique (LINA), Mines Nantes (Mines Nantes)-Université de Nantes (UN)-Centre National de la Recherche Scientifique (CNRS)-Mines Nantes (Mines Nantes)-Université de Nantes (UN)-Centre National de la Recherche Scientifique (CNRS)-Département informatique - EMN, Mines Nantes (Mines Nantes)-Inria Rennes – Bretagne Atlantique, and Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)

Subjects
Optimization, StarCraft, CSP, Combinatorics, Solver, ComputingMilieux_PERSONALCOMPUTING, Real-Time Strategy, Game AI, GeneralLiterature_MISCELLANEOUS, COP, [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI]
Abstract

International audience; This paper presents GHOST, a combinatorial optimization solver an RTS AI developer can use as a blackbox to solve any problems encoded by a constraint satisfaction/optimization problem. We show a way to model three very different RTS problems by a constraint satisfaction/optimization problem, each problem belonging to a specific level of abstraction, and test our solver on them, using StarCraft as a testbed. For the three problems (the target selection problem, the wall-ion problem and the build order planning problem), GHOST shows very good results computed within some tens of milliseconds. Game AI

Published
2016

23. Herbert

Author

Duff, Alexander M., primary, Ma, Jun, additional, Sepelak, Shannon, additional, Uriarte, Alberto, additional, Wu, Wenjie, additional, and Zhu, Jichen, additional

Published
2014
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24. <sc>ghost</sc>: A Combinatorial Optimization Framework for Real-Time Problems.

Author

Richoux, Florian, Uriarte, Alberto, and Baffier, Jean-Francois

Abstract

This paper presents GHOST, a combinatorial optimization framework that a real-time strategy (RTS) AI developer can use to model and solve any problem encoded as a constraint satisfaction/optimization problem (CSP/COP). We show a way to model three different problems as a CSP/COP, using instances from the RTS game StarCraft as test beds. Each problem belongs to a specific level of abstraction (the target selection as reactive control problem, the wall-in as a tactics problem, and the build order planning as a strategy problem). In our experiments, GHOST shows good results computed within some tens of milliseconds. We also show that GHOST outperforms state-of-the-art constraint solvers, matching them on the resources allocation problem, a common combinatorial optimization problem. [ABSTRACT FROM PUBLISHER]

Published
2016
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