Multi-issue automated negotiations using agents

Software agents can perform effectively as negotiators in automated negotiation settings. We present a model for software agents that can automate negotiations by implementing a multi–issue learning heuristic that allows agents to learn from the bidding behavior of opponents. The performance of agents is evaluated using an experimental study involving human subjects. The results indicate that software agents can act as effective surrogates of human negotiators under some circumstances.