A Multiagent Potential Field-Based Bot for Real-Time Strategy Games
نویسندگان
چکیده
منابع مشابه
A Multiagent Potential Field-Based Bot for Real-Time Strategy Games
Bots for real-time strategy (RTS) games may be very challenging to implement. A bot controls a number of units that will have to navigate in a partially unknown environment, while at the same time avoid each other, search for enemies, and coordinate attacks to fight them down. Potential fields are a technique originating from the area of robotics where it is used in controlling the navigation o...
متن کاملUsing multi-agent potential fields in real-time strategy games
Bots for Real Time Strategy (RTS) games provide a rich challenge to implement. A bot controls a number of units that may have to navigate in a partially unknown environment, while at the same time search for enemies and coordinate attacks to fight them down. Potential fields is a technique originating from the area of robotics where it is used in controlling the navigation of robots in dynamic ...
متن کاملCase-based Plan Recognition for Real-time Strategy Games
The current game industry around the world is one of the fastest growing industries. One gaming genre that is very popular is the real-time strategy games. However, current implementations of games apply extensive usage of FSM that makes them highly predictable and provides less replayability. Thus, this paper looks at the possibility of employing case-based plan recognition for NPCs so as to m...
متن کاملCase-Based Planning and Execution for Real-Time Strategy Games
Artificial Intelligence techniques have been successfully applied to several computer games. However in some kinds of computer games, like real-time strategy (RTS) games, traditional artificial intelligence techniques fail to play at a human level because of the vast search spaces that they entail. In this paper we present a real-time case based planning and execution approach designed to deal ...
متن کاملTime-Based Reward Shaping in Real-Time Strategy Games
Real-Time Strategy (RTS) is a challenging domain for AI, since it involves not only a large state space, but also dynamic actions that agents execute concurrently. This problem cannot be optimally solved through general Q-learning techniques, so we propose a solution using a Semi Markov Decision Process (SMDP). We present a time-based reward shaping technique, TRS, to speed up the learning proc...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: International Journal of Computer Games Technology
سال: 2009
ISSN: 1687-7047,1687-7055
DOI: 10.1155/2009/910819