Abstract
The 2D soccer simulation league is one of the best test beds for the research of artificial intelligence (AI). It has achieved great successes in the domain of multi-agent cooperation and machine learning. However, the problem of integral offensive strategy has not been solved because of the dynamic and unpredictable nature of the environment. In this paper, we present a novel offensive strategy based on multi-group ant colony optimization (MACO-OS). The strategy uses the pheromone evaporation mechanism to count the preference value of each attack action in different environments, and saves the values of success rate and preference in an attack information tree in the background. The decision module of the attacker then selects the best attack action according to the preference value. The MACO-OS approach has been successfully implemented in our 2D soccer simulation team in RoboCup competitions. The experimental results have indicated that the agents developed with this strategy, along with related techniques, delivered outstanding performances.
Highlights
We present a novel offensive strategy based on multi-group ant colony optimization (MACO-OS)
The MACO-OS approach has been successfully implemented in our 2D soccer simulation team in RoboCup competitions
As one of oldest leagues in RoboCup, the 2D soccer simulation league has achieved great success and inspired many researchers all over the world to engage in this game each year [1]
Summary
As one of oldest leagues in RoboCup, the 2D soccer simulation league has achieved great success and inspired many researchers all over the world to engage in this game each year [1]. The 2D soccer simulation league provides an important experimental platform, which is a fully distributed, multi-agent stochastic domain with continuous state, action and observation space [2]. FInCoOrdReRr EtoCTIOBNySdeFpOicRtinMg attack actions as paths of foraging, parameters solve the problem of "a solution per state", we set an ant (pheromone level, heuristic information and preference grPoNauogp.e for eLainche Neoq.uivalent state (an eqDuivealleentet state is function) are defineRdeaps floalcloewsw: ith pco1unrspiodseersaeduotfahstoharinss intervalIninsttihtuetpeieocfeIwntiesellifguenncttioMna) cfohrinthees, paper. Definition 2 - Heuristic information (denoted as ηkl) is the experience of ants in the k-th group to choose the path l (corresponding to the l-th attack action selected by agent), and ηkl may be expressed by the access frequency of path l
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