Bidding in Periodic Double Auctions Using Heuristics and Dynamic Monte Carlo Tree Search

Bidding in Periodic Double Auctions Using Heuristics and Dynamic Monte Carlo Tree Search

Moinul Morshed Porag Chowdhury, Christopher Kiekintveld, Son Tran, William Yeoh

Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence
Main track. Pages 166-172. https://doi.org/10.24963/ijcai.2018/23

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.
Keywords:
Agent-based and Multi-agent Systems: Multi-agent Planning
Agent-based and Multi-agent Systems: Multi-agent Learning
Machine Learning: Machine Learning
Uncertainty in AI: Sequential Decision Making
Agent-based and Multi-agent Systems: Agent-Based Simulation and Emergence
Agent-based and Multi-agent Systems: Economic Paradigms, Auctions and Market-Based Systems
Heuristic Search and Game Playing: Game Playing and Machine Learning