{"type":"ScholarlyArticle","title":"Bidding in Periodic Double Auctions Using Heuristics and Dynamic Monte Carlo Tree Search","authors":[{"name":"Moinul Morshed Porag Chowdhury","slug":"moinul-morshed-porag-chowdhury","given":"Moinul Morshed Porag","family":"Chowdhury","affiliation":"The University of Texas at El Paso"},{"name":"Christopher Kiekintveld","slug":"christopher-kiekintveld","given":"Christopher","family":"Kiekintveld","affiliation":"The University of Texas at El Paso"},{"name":"Son Tran","slug":"son-tran","given":"Son","family":"Tran","affiliation":""},{"name":"William Yeoh","slug":"william-yeoh","given":"William","family":"Yeoh","affiliation":"Washington University in St. Louis"}],"abstract":"In a Periodic Double Auction (PDA), there are multiple discrete trading periods for a single type of good. PDAs are commonly used in real-world energy markets to trade energy in specific time slots to balance demand on the power grid. Strategically, bidding in a PDA is complicated because the bidder must predict and plan for future auctions that may influence the bidding strategy for the current auction. We present a general bidding strategy for PDAs based on forecasting clearing prices and using Monte Carlo Tree Search (MCTS) to plan a bidding strategy across multiple time periods. In addition, we present a fast heuristic strategy that can be used either as a standalone method or as an initial set of bids to seed the MCTS policy. We evaluate our bidding strategies using a PDA simulator based on the wholesale market implemented in the Power Trading Agent Competition (PowerTAC) competition. We demonstrate that our strategies outperform state-of-the-art bidding strategies designed for that competition.","doi":"10.24963/ijcai.2018/23","url":"https://ijcai.org/proceedings/2018/23","pdf":"https://ijcai.org/proceedings/2018/0023.pdf","markdown":"https://ijcai.org/proceedings/2018/23.md","bibtex":"https://ijcai.org/proceedings/2018/bibtex/23","volume":{"slug":"2018","title":"Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2018","url":"https://ijcai.org/proceedings/2018/","year":2018,"location":"Stockholm","dates":"13-19 July 2018","editors":"Jérôme Lang","isbn":"978-0-9992411-2-7","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2018"},"section":"Agent-based and Multi-agent Systems","track":"Main track","number":23,"pages":{"first":166,"last":172},"keywords":["Agent-based and Multi-agent Systems: Multi-agent Learning","Agent-based and Multi-agent Systems: Economic Paradigms, Auctions and Market-Based Systems","Agent-based and Multi-agent Systems: Multi-agent Planning","Machine Learning: Machine Learning","Agent-based and Multi-agent Systems: Agent-Based Simulation and Emergence","Uncertainty in AI: Sequential Decision Making","Heuristic Search and Game Playing: Game Playing and Machine Learning"],"videos":[],"references":[{"position":1,"raw":"[Browne et al., 2012] Cameron B. 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