Estimating Utility-Functions for Negotiating Agents: Using Conjoint Analysis as an Alternative Approach to Expected Utility Measurement

Utility-based software agents are especially suited to represent human principals in recurring automatic negotiation applications. In order to work efficiently, utility-based agents need to obtain models of the relevant part of the principal’s preference structure – represented by utility functions. So far agent theory usually applies expected utility measurement. It has, as we will show, certain shortcomings in real life applications. As an alternative, we suggest an approach based on con-joint analysis, which is a well-understood procedure widely used in marketing research and psychology, but gets only small recognition in agent theory. It offers a user-friendly way to derive quantitative utility values for multi-attribute alternatives from the principal’s preferences. In this paper, we introduce the technique in detail along with some extensions and improvements suited for agent applications. Additionally a learning algorithm is derived, keeping track of changes of the principal’s preference structure and adjusting measurement errors.

[1]  R. Luce,et al.  Simultaneous conjoint measurement: A new type of fundamental measurement , 1964 .

[2]  Damaraju Raghavarao,et al.  Combinatorial Analysis and Experimental Design: A Review of “Constructions and Combinatorial Problems in Design of Experiments” by Damaraju Raghavarao , 1972 .

[3]  Leonid Sheremetov,et al.  Weiss, Gerhard. Multiagent Systems a Modern Approach to Distributed Artificial Intelligence , 2009 .

[4]  J. Kruskal.  Nonmetric multidimensional scaling: A numerical method , 1964 .

[5]  E. Kuntsche,et al.  Multivariate Analysemethoden: Eine anwendungsorientierte Einführung. , 2001 .

[6]  Analía Amandi,et al.  User - interface agent interaction: personalization issues , 2004, Int. J. Hum. Comput. Stud..

[7]  Allan D. Shocker,et al.  Estimating the weights for multiple attributes in a composite criterion using pairwise judgments , 1973 .

[8]  Markus Klein,et al.  Die Conjoint-Analyse: eine Einführung in das Verfahren mit einem Ausblick auf mögliche sozialwissenschaftliche Anwendungen , 2002 .

[9]  Shorter Communication A Note on the Determination and Construction of Minimal Orthogonal Main-Effect Plans , 1992 .

[10]  Gerhard Weiss,et al.  Multiagent systems: a modern approach to distributed artificial intelligence , 1999 .

[11]  S. Addelman.  Orthogonal Main-Effect Plans for Asymmetrical Factorial Experiments , 1962 .

[12]  P. Green,et al.  Thirty Years of Conjoint Analysis: Reflections and Prospects , 2001 .

[13]  Sandip Sen,et al.  Learning in multiagent systems , 1999 .

[14]  J. Kruskal.  Multidimensional scaling by optimizing goodness of fit to a nonmetric hypothesis , 1964 .

[15]  Roger N. Shepard,et al.  Multidimensional scaling : theory and applications in the behavioral sciences , 1974 .

[16]  S. Oskamp.  Attitudes and opinions. 3rd ed , 1977 .

[17]  R. Selten,et al.  Bounded rationality: The adaptive toolbox , 2000 .

[18]  J. Kruskal.  Analysis of Factorial Experiments by Estimating Monotone Transformations of the Data , 1965 .

[19]  Hans Czap,et al.  Multi-agent systems and microeconomic theory: a negotiation approach to solve scheduling problems in high dynamic environments , 2003, 36th Annual Hawaii International Conference on System Sciences, 2003. Proceedings of the.

[20]  Howard Raiffa,et al.  Games and Decisions: Introduction and Critical Survey. , 1958 .

[21]  E GreenPaul,et al.  Thirty Years of Conjoint Analysis , 2001 .

[22]  M. Allais.  Le comportement de l'homme rationnel devant le risque : critique des postulats et axiomes de l'ecole americaine , 1953 .

[23]  D. Raghavarao.  Constructions and Combinatorial Problems in Design of Experiments , 1971 .

[24]  J. Neumann,et al.  Theory of games and economic behavior , 1945, 100 Years of Math Milestones.