{"paper":{"title":"A Game-Theoretic Learning Framework for Multi-Agent Intelligent Wireless Networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.GT","authors_text":"Jin Chen, Jinlong Wang, Lijun Kong, Luliang Jia, Ximing Wang, Xin Liu, Yijun Yang, Yuhua Xu","submitted_at":"2018-12-04T08:20:15Z","abstract_excerpt":"In this article, we introduce a game-theoretic learning framework for the multi-agent wireless network. By combining learning in artificial intelligence (AI) with game theory, several promising properties emerge such as obtaining high payoff in the unknown and dynamic environment, coordinating the actions of agents and making the adversarial decisions with the existence of malicious users. Unfortunately, there is no free lunch. To begin with, we discuss the connections between learning in AI and game theory mainly in three levels, i.e., pattern recognition, prediction and decision making. Then"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1812.01267","kind":"arxiv","version":3},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"}