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Learning to be Competitive in the Market

Learning to be Competitive in the Market
Learning to be Competitive in the Market
Agents that buy and sell goods or services in an electronic market need to adapt to the environment's prevailing conditions if they are able to be successful. Here we propose an on-line, continuous learning mechanism that is especially adapted for agents to learn how to behave when negotiating for resources (goods or services). Taking advantage of the specific characteristics of the price adaptation problem, where the different price states are ordered, we propose a specific reinforcement learning strategy that simultaneously allows good stability and fast convergence. Our method works by positively reinforcing all the lower value states if a particular state is successful and negatively reinforcing all the higher value states when a failure occurs. The resulting adaptive behaviour proved, in several different maket situations, to perform better than non-adaptive agents and led to Nash equilibrium when faced with other adaptive opponents.
30-37
Oliveira, E.
68c9f56d-1afe-432d-81d9-3f14a1d76aa4
Fonseca, J. M.
70b3bab2-dad1-4c42-b323-260abdd965e6
Jennings, N. R.
ab3d94cc-247c-4545-9d1e-65873d6cdb30
Oliveira, E.
68c9f56d-1afe-432d-81d9-3f14a1d76aa4
Fonseca, J. M.
70b3bab2-dad1-4c42-b323-260abdd965e6
Jennings, N. R.
ab3d94cc-247c-4545-9d1e-65873d6cdb30

Oliveira, E., Fonseca, J. M. and Jennings, N. R. (1999) Learning to be Competitive in the Market. AAAI Workshop on Negotiation: Settling Conflicts and Identifying Opportunities, Orlando, Florida, United States. pp. 30-37 .

Record type: Conference or Workshop Item (Paper)

Abstract

Agents that buy and sell goods or services in an electronic market need to adapt to the environment's prevailing conditions if they are able to be successful. Here we propose an on-line, continuous learning mechanism that is especially adapted for agents to learn how to behave when negotiating for resources (goods or services). Taking advantage of the specific characteristics of the price adaptation problem, where the different price states are ordered, we propose a specific reinforcement learning strategy that simultaneously allows good stability and fast convergence. Our method works by positively reinforcing all the lower value states if a particular state is successful and negatively reinforcing all the higher value states when a failure occurs. The resulting adaptive behaviour proved, in several different maket situations, to perform better than non-adaptive agents and led to Nash equilibrium when faced with other adaptive opponents.

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Published date: 1999
Venue - Dates: AAAI Workshop on Negotiation: Settling Conflicts and Identifying Opportunities, Orlando, Florida, United States, 1999-01-01
Organisations: Agents, Interactions & Complexity

Identifiers

Local EPrints ID: 252171
URI: http://eprints.soton.ac.uk/id/eprint/252171
PURE UUID: 9c2a6f10-4534-4bea-abd1-01ea06172dfc

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Date deposited: 03 Dec 2002
Last modified: 11 Jun 2021 16:32

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