Market-based control: a paradigm for distributed resource allocation
Market-based control: a paradigm for distributed resource allocation
Saving energy using market-based control
Market-based control
The use of computer-assisted auctions for allocating tradeable pollution permits
Market-based control
High-performance bidding agents for the continuous double auction
Proceedings of the 3rd ACM conference on Electronic Commerce
Strategic sequential bidding in auctions using dynamic programming
Proceedings of the first international joint conference on Autonomous agents and multiagent systems: part 2
Applying evolutionary game theory to auction mechanism design
Proceedings of the 4th ACM conference on Electronic commerce
On Agent-Mediated Electronic Commerce
IEEE Transactions on Knowledge and Data Engineering
A Fuzzy-Logic Based Bidding Strategy for Autonomous Agents in Continuous Double Auctions
IEEE Transactions on Knowledge and Data Engineering
Evolutionary game theory and multi-agent reinforcement learning
The Knowledge Engineering Review
What evolutionary game theory tells us about multiagent learning
Artificial Intelligence
Agent-human interactions in the continuous double auction
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 2
IEEE Transactions on Evolutionary Computation - Special issue on computational finance and economics
Evolutionary stability of behavioural types in the continuous double auction
TADA/AMEC'06 Proceedings of the 2006 AAMAS workshop and TADA/AMEC 2006 conference on Agent-mediated electronic commerce: automated negotiation and strategy design for electronic markets
An evolutionary game-theoretic comparison of two double-auction market designs
AAMAS'04 Proceedings of the 6th AAMAS international conference on Agent-Mediated Electronic Commerce: theories for and Engineering of Distributed Mechanisms and Systems
IEEE Transactions on Evolutionary Computation
Market-Based Task Allocation Mechanisms for Limited-Capacity Suppliers
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Stronger CDA strategies through empirical game-theoretic analysis and reinforcement learning
Proceedings of The 8th International Conference on Autonomous Agents and Multiagent Systems - Volume 1
Adaptive Adjustment of Starting Price for Agents in Continuous Double Auctions
PRIMA '09 Proceedings of the 12th International Conference on Principles of Practice in Multi-Agent Systems
Using economic regulation to prevent resource congestion in large-scale shared infrastructures
Future Generation Computer Systems
Autonomous Agents and Multi-Agent Systems
Trading agents for the smart electricity grid
Proceedings of the 9th International Conference on Autonomous Agents and Multiagent Systems: volume 1 - Volume 1
Strategy exploration in empirical games
Proceedings of the 9th International Conference on Autonomous Agents and Multiagent Systems: volume 1 - Volume 1
An Equilibrium Analysis of Competing Double Auction Marketplaces Using Fictitious Play
Proceedings of the 2010 conference on ECAI 2010: 19th European Conference on Artificial Intelligence
Preference-based English reverse auctions
Artificial Intelligence
Hierarchically structured energy markets as novel smart grid control approach
KI'11 Proceedings of the 34th Annual German conference on Advances in artificial intelligence
Human-agent auction interactions: adaptive-aggressive agents dominate
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume One
Scaling simulation-based game analysis through deviation-preserving reduction
Proceedings of the 11th International Conference on Autonomous Agents and Multiagent Systems - Volume 2
The global financial markets: an ultra-large-scale systems perspective
Proceedings of the 17th Monterey conference on Large-Scale Complex IT Systems: development, operation and management
Autonomous Agents and Multi-Agent Systems
Hi-index | 0.00 |
In this paper, we describe a novel bidding strategy that autonomous trading agents can use to participate in Continuous Double Auctions (CDAs). Our strategy is based on both short and long-term learning that allows such agents to adapt their bidding behaviour to be efficient in a wide variety of environments. For the short-term learning, the agent updates the aggressiveness of its bidding behaviour (more aggressive means it will trade off profit to improve its chance of transacting, less aggressive that it targets more profitable transactions and is willing to trade off its chance of transacting to achieve them) based on market information observed after any bid or ask appears in the market. The long-term learning then determines how this aggressiveness factor influences an agent's choice of which bids or asks to submit in the market, and is based on market information observed after every transaction (successfully matched bid and ask). The principal motivation for the short-term learning is to enable the agent to immediately respond to market fluctuations, while for the long-term learning it is to adapt to broader trends in the way in which the market demand and supply changes over time. We benchmark our strategy against the current state of the art (ZIP and GDX) and show that it outperforms these benchmarks in both static and dynamic environments. This is true both when the population is homogeneous (where the increase in efficiency is up to 5.2%) and heterogeneous (in which case there is a 0.85 probability of our strategy being adopted in a two-population evolutionary game theoretic analysis).