Resumo (PT):
Abstract (EN):
Agent technology has been applied to the Electronic Commerce
domain, giving birth to what is known as agent-mediated Electronic Commerce.
Current real-world applications refer only to the delegation of product or
merchant brokering tasks to software agents. Automated negotiation is a less
explored stage in this field, since it implies the trust of bargaining power to
software agents. We here present SMACE, a layered platform for agentmediated
Electronic Commerce, supporting multilateral and multi-issue
automated negotiations. In this system, the negotiation infrastructure through
which the software agents interact is independent from their negotiation
strategies. SMACE has been used to test several negotiation strategies. The
system includes agents that are capable of increasing their performance with
their own experience, by adapting to the market conditions. This adaptation is
reached through the use of Reinforcement Learning techniques. In order to test
the agents' adaptation process, several different experiments have been tried
out, and the respective results are here reported. These results allow us to
conclude that it is possible to build negotiation strategies that can outperform
others in some environments. In fact, knowledge gath
Language:
English
Type (Professor's evaluation):
Scientific
No. of pages:
10
License type: