Predicting agents tactics in automated negotiation

This work presents a learning mechanism that applies nonlinear regression analysis to predict a negotiation agent´s behaviour based only the opponent´s previous offers. The behaviour of negotiation agents in this study is determined by their tactics in the form of decision functions. Heuristics based on estimates of an agent´s tactics are drawn from a series of experiments. The findings of this empirical study show that this approach can be used to obtain better deals than existing decision function tactics. The learning mechanism can be used online, without any prior knowledge about other agents and is therefore, very useful in open systems where agents have little or no information about each other.