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MULTI AGENT GAME OF KABADDI: STRATEGIES AND SIMULATIONAuthor: Nagarjuna Samrat Date: 2015-05-26 Report no: IIIT/TH/2015/17 Advisor:Kamalakar Karlapalem AbstractKabaddi is a popular and an intense game played in Indian sub-continent. The key feature of the game is simultaneous embodiment of cooperation and competition among the players. In particular, the player has to implicitly or explicitly cooperate and coordinate with in a team, while competing with opponent team. The game is played on the principle - capture the raider, while not getting caught by the raider. This principle of capture sounds similar to pursuit domain, a domain used in study of multi-agent systems. There were many strategies that were proposed for pursuit domain but none of them are suitable for Kabaddi due to the game rules and constraints on field space and time. The rules and regulations of the game makes it more interesting and challenging to develop strategies for a game. The major challenges involved in the game of Kabaddi are cooperation between players, decision making by a team (which strategy to use and when), competing against the opponent team. Thus Kabaddi can provide an environment and a framework for Multi-Robot Systems and MAS researchers to evaluate strategies involving both cooperation and competition. In this thesis, we present a Kabaddi game simulator which assists the user to program strategies and evaluate them on simulated games. The Kabaddi simulator package (KSP) is built in Java and can be imported as a Java library. All the game rules of Kabaddi are incorporated into the simulator. By observing many international games, we analyzed strategies deployed by a team in various scenarios and came up with a generic solution for the game. Formations play a key role in the anti-raiders game. We introduce the concept of risk that assists the anti-raiders to get into a formation through implicit cooperation. We also use the risk factor to avoid collision among agents. Three strategies straight line formation, semi-circular formation, circular formation are developed, tested and evaluated against pursuit domain strategy. Though the intermediate goal of anti-raiders is to capture the raider they also should compete in a game. That is, the team might need to change their strategy depending upon the state of a game and must seek to increase their score. To develop a strategy that is competitive we use MDP (Markov Decision Process) which considers multiple strategies as actions and deploys them based on the state of a game. This thesis also discusses on building robots that can play Kabaddi and a few simulations of Kabaddi strategies on a robot simulator. Full thesis: pdf Centre for Data Engineering |
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