Modified Adversarial Hierarchical Task Network Planning in Real-Time Strategy Games
نویسندگان
چکیده
منابع مشابه
Adversarial Hierarchical-Task Network Planning for Real-Time Adversarial Games
Real-time strategy (RTS) games are hard from an AI point of view because they have enormous state spaces, combinatorial branching factors, allow simultaneous and durative actions, and players have very little time to choose actions. For these reasons, standard game tree search methods such as alphabeta search or Monte Carlo Tree Search (MCTS) are not sufficient by themselves to handle these gam...
متن کاملModified Adversarial Hierarchical Task Network Planning in Real-Time Strategy Games
The application of artificial intelligence (AI) to real-time strategy (RTS) games includes considerable challenges due to the very large state spaces and branching factors, limited decision times, and dynamic adversarial environments involved. To address these challenges, hierarchical task network (HTN) planning has been extended to develop a method denoted as adversarial HTN (AHTN), and this m...
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Real-Time Strategy (RTS) video games have proven to be a very challenging application area for artificial intelligence research. Existing AI solutions are limited by vast state and action spaces and real-time constraints. Most implementations efficiently tackle various tactical or strategic sub-problems, but there is no single algorithm fast enough to be successfully applied to big problem sets...
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In this paper, we propose an automatic mechanism of Hierarchical Task Networks (HTNs) creation for solving the problem of real-time path planning in Real-Time Strategy (RTS) Games. HTNs are created using an abstraction of the game map. A real-time heuristic search approach called Learning Real-Time A* (LRTA) is applied to execute the primitive tasks of the HTNs. The main purpose of using a HTN ...
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We describe a new integrated and automated AI planning and learning architecture, called Learn2SHOP. Learn2SHOP departs significantly from the previous works on AI planning and learning in that its modular architecture integrates Hierarchical Task Network (HTN) planning, concept learning, and computer simulations. Using simulations during the planning and learning process enables the system to ...
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ژورنال
عنوان ژورنال: Applied Sciences
سال: 2017
ISSN: 2076-3417
DOI: 10.3390/app7090872