Authors:
Marisa Masvoula
;
Panagiotis Kanellis
and
Drakoulis Martakos
Affiliation:
National and Kapodistrian University of Athens, Greece
Keyword(s):
Evolving Connectionist Systems, Negotiation forecasts, Predictive Decision Making.
Related
Ontology
Subjects/Areas/Topics:
Agents
;
Artificial Intelligence
;
Artificial Intelligence and Decision Support Systems
;
B2B, B2C and C2C
;
B2C/B2B Considerations
;
Biomedical Engineering
;
Biomedical Signal Processing
;
Business and Social Applications
;
Communication and Software Technologies and Architectures
;
Computational Intelligence
;
e-Business
;
Enterprise Information Systems
;
Health Engineering and Technology Applications
;
Human-Computer Interaction
;
Intelligent Agents
;
Internet Technology
;
Methodologies and Methods
;
Neural Network Software and Applications
;
Neural Networks
;
Neurocomputing
;
Neurotechnology, Electronics and Informatics
;
Pattern Recognition
;
Physiological Computing Systems
;
Sensor Networks
;
Signal Processing
;
Society, e-Business and e-Government
;
Soft Computing
;
Software Agents and Internet Computing
;
Theory and Methods
;
Web Information Systems and Technologies
Abstract:
Predictive decision making increases the individual or joint gain of negotiators, and has been extensively studied. One particular skill of predicting agents is the forecast of their opponents’ future offers. Current systems focus on enhancing learning techniques in the decision making module of negotiating agents, with the purpose to develop more robust systems. Empirical studies are conducted in bounded problem spaces, where data distribution is known or assumed. Our proposal concentrates on the incorporation of learning structures in agents’ decision making, capable of forecasting opponents’ future offers even in open problem spaces, which is the case in most negotiation situations.