Abstract
A key to an optimal assortment of goods and pricing of individual items in a store is the knowledge about potential customer’s behaviour. In this paper we present the simulation of individual customers based on a multiagent system which models the important elements and external influences as single agents. An agent can be member of several agent groups which are represented as holons. We model each individual customer as an agent which behaves according the customer’s individual preferences. These preferences are extracted from real world data, such as customer cards, sales data and interviews. The customer’s shopping behaviour is represented in behaviour networks (Bayesian nets) which are stored in the customer agents’ knowledge bases. The behaviour of a representative group of customers induces the overall sales figures, which support decisions what to sell at which price. The presented concepts are based on ideas of Joachim Hertel from DACOS and Jörg Siekmann from the DFKI. They are implemented as a prototype, which provides, after further evaluation, the basis for a new and final system to be used by retailers.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Russell, S., Norvig, P.: Artificial Intelligence: A Modern Approach, 2nd edn. Prentice Hall, Englewood Cliffs (2003)
Jager, W.: Modelling Consumer Behaviour. Rijksuniversiteit Groningen, Dissertation (June 2000)
Buchta, C., Mazanec, J.: SIMSEG/ACM – A Simulation Environment for Artificial Consumer Markets. Working Paper Nr. 79 (May 2001)
Fischer, K., Schillo, M., Siekmann, J.: Holonic Multiagent Systems: The Foundation for the Organization of Multiagent Systems. In: Mařík, V., McFarlane, D.C., Valckenaers, P. (eds.) HoloMAS 2003. LNCS (LNAI), vol. 2744, pp. 71–80. Springer, Heidelberg (2003) (in print)
Minsky, M.: The Society of Mind. Simon and Schuster, Touchstone (1986)
Sinus Sociovision, Paris, France, http://www.sociovision.com
Europanel, London, UK, www.europanel.com
Jameson, A.: Systems That Adapt to Their Users. Tutorial presented at IJCAI (2001), http://www.dfki.de/~jameson
Schaefer, R.: Benutzermodellierung mit dynamischen Bayes’schen Netzen als Grundlage adaptiver Dialogsysteme. In: Dissertation, University of Saarland (1998)
Pearl, J.: Probabilistic Reasoning in Intelligent Systems. Morgan Kaufmann Publishers INC., San Francisco (1988)
Neapolitan, R.E.: Probabilistic Reasoning in Expert Systems: Theory and Algorithms. A Wiley-Interscience Publication. John Wiley & Sons, Inc., New York (1998)
Kroeber-Riel, W., Weinberg, P.: Konsumentenverhalten, vol. 7. Aufl., Vahlen München (1999)
Poh, H.-L., Yao, J., Jasic, T.: Neural Networks for the Analysis and Forecasting of Advertising and Promotion Impact. Intelligent Systems in Accounting, Finance and Management 7(4) (1998)
Thiesing, F.M.: Analyse und Prognose von Zeitreihen mit Neuronalen Netzen. Dissertation (Mai 1998)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2003 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Schwaiger, A., Stahmer, B. (2003). SimMarket: Multiagent-Based Customer Simulation and Decision Support for Category Management. In: Schillo, M., Klusch, M., Müller, J., Tianfield, H. (eds) Multiagent System Technologies. MATES 2003. Lecture Notes in Computer Science(), vol 2831. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-39869-1_7
Download citation
DOI: https://doi.org/10.1007/978-3-540-39869-1_7
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-20124-3
Online ISBN: 978-3-540-39869-1
eBook Packages: Springer Book Archive