Abstract
As shopbots spread through Internet collecting information about lowest prices / highest qualities human sellers will turn out to be too slow to tune the prices and thus unprofitable in comparison with smart agents — pricebots. One of the most promising approaches to building pricebots is Q-learning. Its advantages: flexibility to act under changing conditions of virtual markets, Q-learning sellers can take into account not only immediate rewards but also profits far ahead, and don’t need information neither on buyer demand nor on competitors’ behaviour. But up to now Q-learning sellers used state representation exponential in the number of sellers acting in the market and could function successfully only with one competitor which no doubt is unrealistic. We are proposing a new state representation independent of the number of sellers that allowed to 10 agents to find the prices that maximize cumulative profits under conditions of high competition in three moderately realistic economic models. It was also shown that due to their flexibility Q-learning sellers managed to collect more profit than pricebots based on two other generally used approaches even though one of them possessed much more information about buyer demand and competitors’ behaviour. The proposed representation doesn’t depend on the number of sellers and in principle Q-learning sellers using it can function in the markets with arbitrary number of competitors.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
DeLong, J.B., Froomkin, A.M.: Speculative Microeconomics for Tomorrow’s Economy. First Monday 5(2) (2000)
Rosenschein, J.S., Zlotkin, G.: Designing conventions for automated negotiation. AI Magazine 15(3), 29–46 (1994)
Greenwald, A.R., Kephart, J.O.: Shopbots and pricebots. In: Dean, T. (ed.) IJCAI, pp. 506–511. Morgan Kaufmann, San Francisco (1999)
Sutton, R.S., Barto, A.G.: Reinforcement learning: an introduction. MIT Press, Cambridge (1998)
Watkins, C.J.C.H., Dayan, P.: Technical note q-learning. Machine Learning 8, 279–292 (1992)
Tesauro, G., Kephart, J.O.: Pricing in agent economies using multi-agent q-learning. Autonomous Agents and Multi-Agent Systems 5(3), 289–304 (2002)
Tesauro, G.: Pricing in agent economies using neural networks and multi-agent q-learning. In: Sun, R., Giles, C.L. (eds.) IJCAI-WS 1999. LNCS (LNAI), vol. 1828, pp. 288–307. Springer, Heidelberg (2001)
Mitchell, T.: Machine Learning. McGraw-Hill, New York (1997)
Sairamesh, J., Kephart, J.O.: Price dynamics of vertically differentiated information markets. In: ICE’98, Proceedings of the first international conference on Information and computation economies, pp. 28–36. ACM Press, New York (1998)
Vidal, J.M., Durfee, E.H.: Learning nested agent models in an information economy. Journal of Experimental and Theoretical Artificial Intelligence 10(3), 291–308 (1998)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2007 Springer Berlin Heidelberg
About this paper
Cite this paper
Akchurina, N., Kleine Büning, H. (2007). Virtual Markets: Q-Learning Sellers with Simple State Representation. In: Gorodetsky, V., Zhang, C., Skormin, V.A., Cao, L. (eds) Autonomous Intelligent Systems: Multi-Agents and Data Mining. AIS-ADM 2007. Lecture Notes in Computer Science(), vol 4476. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72839-9_16
Download citation
DOI: https://doi.org/10.1007/978-3-540-72839-9_16
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-72838-2
Online ISBN: 978-3-540-72839-9
eBook Packages: Computer ScienceComputer Science (R0)