Learning to coordinate for target selection

Learning to play an imperfect information card game using reinforcement learning Alpaslan, Ferda Nur; Baykal, Ömer; Demirdöver, Buğra Kaan (2022-08-01) Artificial intelligence and machine learning are widely popular in many areas. One of the most popular ones is gaming. Games are perfect testbeds for machine learning and artificial intelligence with various scenarios and types. This study aims to develop a self-learning intelligent agent to play the Hearts game. Hearts is one of the most popular trick-taking card games around the world. It is an imperfect information card game. In addition to having a huge state space, Hearts offers many extra challenges d...
Learning sequences of compatible actions among agents Polat, Faruk (2002-03-01) Action coordination in multiagent systems is a difficult task especially in dynamic environments. If the environment possesses cooperation, least communication, incompatibility and local information constraints, the task becomes even more difficult. Learning compatible action sequences to achieve a designated goal under these constraints is studied in this work. Two new multiagent learning algorithms called QACE and NoCommQACE are developed. To improve the performance of the QACE and NoCommQACE algorithms f...
A layered approach to learning coordination knowledge in multiagent environments Erus, Guray; Polat, Faruk (2007-12-01) Multiagent learning involves acquisition of cooperative behavior among intelligent agents in order to satisfy the joint goals. Reinforcement Learning (RL) is a promising unsupervised machine learning technique inspired from the earlier studies in animal learning. In this paper, we propose a new RL technique called the Two Level Reinforcement Learning with Communication (2LRL) method to provide cooperative action selection in a multiagent environment. In 2LRL, learning takes place in two hierarchical levels;...
Effect of human prior knowledge on game success and comparison with reinforcement learning Hasanoğlu, Mert.; Çakır, Murat Perit; Department of Cognitive Sciences (2019) This study aims to find out the effect of prior knowledge on the success of humans in a non-rewarding game environment, and then to compare human performance with a reinforcement learning method in an effort to observe to what extent this method can be brought closer to human behavior and performance with the data obtained. For this purpose, different versions of a simple 2D game were used, and data were collected from 32 participants. At the end of the experiment, it is concluded that prior knowledge, such...
Understanding the Dark Sides of Alternative Economies to Maximize Societal Benefit Watson, Forrest (SAGE Publications, 2020-06-01) Alternative economies can significantly contribute to societal flourishing, but the potential dark sides should also be considered. As shared commitments are the foundation of alternative economies, we draw on related literature to conceptualize various types of dark sides of an alternative economy. While less prominent than the well-being outcomes, we present qualitative data of when the participants of one alternative food network experienced disappointment, burnout, guilt, or division. Comparing with the...

Citation Formats

M. Tan, “Learning to coordinate for target selection,” M.S. - Master of Science, Middle East Technical University, 2003.