{'fraction_non_alphanumeric': 0.01620745542949757, 'fraction_numerical': 0.0, 'mean_word_length': 5.0588235294117645, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Supervised Learning for Agent Positioning by using Self-organizing Map

We propose a multi-agent cooperative method that helps each agent to cope with partial observation and reduces the number of teaching data. It learns cooperative actions between agents by using the Self-Organizing Map as supervised learning. Input Vectors of the Self-Organizing Map are the data that reflects the operator’s intention. We show that our proposed method can acquire cooperative actions between agents and reduce the number of teaching data by two evaluation experiments using the pursuit problem that is one of multi-agent system.