Predictive Automated Negotiators Employing Risk-Seeking and Risk-Averse Strategies
Intelligent agents that seek to automate various stages of the negotiation process are often enhanced with models of computational intelligence extending the cognitive abilities of the parties they represent. This paper is focused on predictive strategies employed by automated negotiators, and particularly those based on forecasting the counterpart’s responses. In this context a strategy supporting negotiations over multiple issues is presented and assessed. Various behaviors emerge with respect to negotiator’s attitude towards risk, resulting to different utility gains. Forecasting is conducted with the use of Multilayer Perceptrons (MLPs) and the training set is extracted online during the negotiation session. Two cases are examined: in the first separate MLPs are used for the estimations of each negotiable attribute, whereas in the second a single MLP is used to estimate the counterpart’s response. Experiments are conducted to search the architecture of the MLPs.