Abstract. 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.

No abstract

Advancement of Artificial Intelligence has contributed in the enhancement of agent strategies with learning techniques. We provide an overview of learning methods that form the core of state-of-the art negotiators. The main objective is to facilitate the comprehension of the domain by framing current systems with respect to learning objectives and phases of application. We also aim to reveal current trends, virtues and weaknesses of applied methods.

No abstract

No abstract