Estimating utility-functions for negotiating agents : Using conjoint analysis as an alternative approach to expected utility measurement
Identifieur interne :
000A40 ( PascalFrancis/Corpus );
précédent :
000A39;
suivant :
000A41
Estimating utility-functions for negotiating agents : Using conjoint analysis as an alternative approach to expected utility measurement
Auteurs : Marc Becker ;
Hans Czap ;
Malte Poppensieker ;
Alexander StotzSource :
-
Lecture notes in computer science [ 0302-9743 ] ; 2005.
RBID : Pascal:05-0408036
Descripteurs français
- Pascal (Inist)
- Système multiagent,
Intelligence artificielle,
Agent logiciel,
Homme,
Négociation,
Préférence,
Décomposition fonction,
Commercialisation,
Fonction utilité,
Théorie utilité,
Utilité attendue,
Modélisation,
Psychologie,
Algorithme apprentissage,
Erreur mesure,
..
English descriptors
- KwdEn :
- Artificial intelligence,
Bargaining,
Expected utility,
Function decomposition,
Human,
Learning algorithm,
Marketing,
Measurement error,
Modeling,
Multiagent system,
Preference,
Psychology,
Software agents,
Utility function,
Utility theory.
Abstract
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.
Notice en format standard (ISO 2709)
Pour connaître la documentation sur le format Inist Standard.
| pA |
| A01 | 01 | 1 | | @0 0302-9743 |
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| A05 | | | | @2 3550 |
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| A08 | 01 | 1 | ENG | @1 Estimating utility-functions for negotiating agents : Using conjoint analysis as an alternative approach to expected utility measurement |
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| A09 | 01 | 1 | ENG | @1 MATES 2005 : multiagent system technologies : Koblenz, 11-13 September 2005 |
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| A11 | 01 | 1 | | @1 BECKER (Marc) |
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| A11 | 02 | 1 | | @1 CZAP (Hans) |
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| A11 | 03 | 1 | | @1 POPPENSIEKER (Malte) |
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| A11 | 04 | 1 | | @1 STOTZ (Alexander) |
|---|
| A12 | 01 | 1 | | @1 EYMANN (Torsten) @9 ed. |
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| A12 | 02 | 1 | | @1 KLUGL (Franziska) @9 ed. |
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| A12 | 03 | 1 | | @1 LAMERSDORF (Winfried) @9 ed. |
|---|
| A12 | 04 | 1 | | @1 KLUSCH (Matthias) @9 ed. |
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| A12 | 05 | 1 | | @1 HUHNS (Michael N.) @9 ed. |
|---|
| A14 | 01 | | | @1 Department of Business Information Systems I, University of Trier @2 54296 Trier @3 DEU @Z 1 aut. @Z 2 aut. @Z 3 aut. @Z 4 aut. |
|---|
| A20 | | | | @1 94-105 |
|---|
| A21 | | | | @1 2005 |
|---|
| A23 | 01 | | | @0 ENG |
|---|
| A26 | 01 | | | @0 3-540-28740-X |
|---|
| A43 | 01 | | | @1 INIST @2 16343 @5 354000124419200090 |
|---|
| A44 | | | | @0 0000 @1 © 2005 INIST-CNRS. All rights reserved. |
|---|
| A45 | | | | @0 23 ref. |
|---|
| A47 | 01 | 1 | | @0 05-0408036 |
|---|
| A60 | | | | @1 P @2 C |
|---|
| A61 | | | | @0 A |
|---|
| A64 | 01 | 1 | | @0 Lecture notes in computer science |
|---|
| A66 | 01 | | | @0 DEU |
|---|
| C01 | 01 | | ENG | @0 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. |
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| C02 | 01 | X | | @0 001D02C |
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| C03 | 01 | X | FRE | @0 Système multiagent @5 06 |
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| C03 | 01 | X | ENG | @0 Multiagent system @5 06 |
|---|
| C03 | 01 | X | SPA | @0 Sistema multiagente @5 06 |
|---|
| C03 | 02 | X | FRE | @0 Intelligence artificielle @5 07 |
|---|
| C03 | 02 | X | ENG | @0 Artificial intelligence @5 07 |
|---|
| C03 | 02 | X | SPA | @0 Inteligencia artificial @5 07 |
|---|
| C03 | 03 | 3 | FRE | @0 Agent logiciel @5 08 |
|---|
| C03 | 03 | 3 | ENG | @0 Software agents @5 08 |
|---|
| C03 | 04 | X | FRE | @0 Homme @5 18 |
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| C03 | 04 | X | ENG | @0 Human @5 18 |
|---|
| C03 | 04 | X | SPA | @0 Hombre @5 18 |
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| C03 | 05 | X | FRE | @0 Négociation @5 19 |
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| C03 | 05 | X | ENG | @0 Bargaining @5 19 |
|---|
| C03 | 05 | X | SPA | @0 Negociación @5 19 |
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| C03 | 06 | X | FRE | @0 Préférence @5 20 |
|---|
| C03 | 06 | X | ENG | @0 Preference @5 20 |
|---|
| C03 | 06 | X | SPA | @0 Preferencia @5 20 |
|---|
| C03 | 07 | X | FRE | @0 Décomposition fonction @5 21 |
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| C03 | 07 | X | ENG | @0 Function decomposition @5 21 |
|---|
| C03 | 07 | X | SPA | @0 Descomposición función @5 21 |
|---|
| C03 | 08 | X | FRE | @0 Commercialisation @5 22 |
|---|
| C03 | 08 | X | ENG | @0 Marketing @5 22 |
|---|
| C03 | 08 | X | SPA | @0 Comercialización @5 22 |
|---|
| C03 | 09 | X | FRE | @0 Fonction utilité @5 23 |
|---|
| C03 | 09 | X | ENG | @0 Utility function @5 23 |
|---|
| C03 | 09 | X | SPA | @0 Función utilidad @5 23 |
|---|
| C03 | 10 | X | FRE | @0 Théorie utilité @5 24 |
|---|
| C03 | 10 | X | ENG | @0 Utility theory @5 24 |
|---|
| C03 | 10 | X | SPA | @0 Teoría utilidad @5 24 |
|---|
| C03 | 11 | X | FRE | @0 Utilité attendue @5 25 |
|---|
| C03 | 11 | X | ENG | @0 Expected utility @5 25 |
|---|
| C03 | 11 | X | SPA | @0 Utilidad espera @5 25 |
|---|
| C03 | 12 | X | FRE | @0 Modélisation @5 26 |
|---|
| C03 | 12 | X | ENG | @0 Modeling @5 26 |
|---|
| C03 | 12 | X | SPA | @0 Modelización @5 26 |
|---|
| C03 | 13 | X | FRE | @0 Psychologie @5 27 |
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| C03 | 13 | X | ENG | @0 Psychology @5 27 |
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| C03 | 13 | X | SPA | @0 Psicología @5 27 |
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| C03 | 14 | X | FRE | @0 Algorithme apprentissage @5 28 |
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| C03 | 14 | X | ENG | @0 Learning algorithm @5 28 |
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| C03 | 14 | X | SPA | @0 Algoritmo aprendizaje @5 28 |
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| C03 | 15 | X | FRE | @0 Erreur mesure @5 41 |
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| C03 | 15 | X | ENG | @0 Measurement error @5 41 |
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| C03 | 15 | X | SPA | @0 Error medida @5 41 |
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| C03 | 16 | X | FRE | @0 . @4 INC @5 82 |
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| N21 | | | | @1 283 |
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| N44 | 01 | | | @1 OTO |
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| N82 | | | | @1 OTO |
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|
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| pR |
| A30 | 01 | 1 | ENG | @1 Multiagent system technologies. German conference @2 3 @3 Koblenz DEU @4 2005-09-11 |
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|
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Format Inist (serveur)
| NO : | PASCAL 05-0408036 INIST |
|---|
| ET : | Estimating utility-functions for negotiating agents : Using conjoint analysis as an alternative approach to expected utility measurement |
|---|
| AU : | BECKER (Marc); CZAP (Hans); POPPENSIEKER (Malte); STOTZ (Alexander); EYMANN (Torsten); KLUGL (Franziska); LAMERSDORF (Winfried); KLUSCH (Matthias); HUHNS (Michael N.) |
|---|
| AF : | Department of Business Information Systems I, University of Trier/54296 Trier/Allemagne (1 aut., 2 aut., 3 aut., 4 aut.) |
|---|
| DT : | Publication en série; Congrès; Niveau analytique |
|---|
| SO : | Lecture notes in computer science; ISSN 0302-9743; Allemagne; Da. 2005; Vol. 3550; Pp. 94-105; Bibl. 23 ref. |
|---|
| LA : | Anglais |
|---|
| EA : | 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. |
|---|
| CC : | 001D02C |
|---|
| FD : | Système multiagent; Intelligence artificielle; Agent logiciel; Homme; Négociation; Préférence; Décomposition fonction; Commercialisation; Fonction utilité; Théorie utilité; Utilité attendue; Modélisation; Psychologie; Algorithme apprentissage; Erreur mesure; . |
|---|
| ED : | Multiagent system; Artificial intelligence; Software agents; Human; Bargaining; Preference; Function decomposition; Marketing; Utility function; Utility theory; Expected utility; Modeling; Psychology; Learning algorithm; Measurement error |
|---|
| SD : | Sistema multiagente; Inteligencia artificial; Hombre; Negociación; Preferencia; Descomposición función; Comercialización; Función utilidad; Teoría utilidad; Utilidad espera; Modelización; Psicología; Algoritmo aprendizaje; Error medida |
|---|
| LO : | INIST-16343.354000124419200090 |
|---|
| ID : | 05-0408036 |
|---|
Links to Exploration step
Pascal:05-0408036
Le document en format XML
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</keywords><front><div type="abstract" xml:lang="en">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.</div>
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</fA09><fA11 i1="01" i2="1"><s1>BECKER (Marc)</s1>
</fA11><fA11 i1="02" i2="1"><s1>CZAP (Hans)</s1>
</fA11><fA11 i1="03" i2="1"><s1>POPPENSIEKER (Malte)</s1>
</fA11><fA11 i1="04" i2="1"><s1>STOTZ (Alexander)</s1>
</fA11><fA12 i1="01" i2="1"><s1>EYMANN (Torsten)</s1>
<s9>ed.</s9>
</fA12><fA12 i1="02" i2="1"><s1>KLUGL (Franziska)</s1>
<s9>ed.</s9>
</fA12><fA12 i1="03" i2="1"><s1>LAMERSDORF (Winfried)</s1>
<s9>ed.</s9>
</fA12><fA12 i1="04" i2="1"><s1>KLUSCH (Matthias)</s1>
<s9>ed.</s9>
</fA12><fA12 i1="05" i2="1"><s1>HUHNS (Michael N.)</s1>
<s9>ed.</s9>
</fA12><fA14 i1="01"><s1>Department of Business Information Systems I, University of Trier</s1>
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</fA47><fA60><s1>P</s1>
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</fA66><fC01 i1="01" l="ENG"><s0>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.</s0>
</fC01><fC02 i1="01" i2="X"><s0>001D02C</s0>
</fC02><fC03 i1="01" i2="X" l="FRE"><s0>Système multiagent</s0>
<s5>06</s5>
</fC03><fC03 i1="01" i2="X" l="ENG"><s0>Multiagent system</s0>
<s5>06</s5>
</fC03><fC03 i1="01" i2="X" l="SPA"><s0>Sistema multiagente</s0>
<s5>06</s5>
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<s5>07</s5>
</fC03><fC03 i1="02" i2="X" l="ENG"><s0>Artificial intelligence</s0>
<s5>07</s5>
</fC03><fC03 i1="02" i2="X" l="SPA"><s0>Inteligencia artificial</s0>
<s5>07</s5>
</fC03><fC03 i1="03" i2="3" l="FRE"><s0>Agent logiciel</s0>
<s5>08</s5>
</fC03><fC03 i1="03" i2="3" l="ENG"><s0>Software agents</s0>
<s5>08</s5>
</fC03><fC03 i1="04" i2="X" l="FRE"><s0>Homme</s0>
<s5>18</s5>
</fC03><fC03 i1="04" i2="X" l="ENG"><s0>Human</s0>
<s5>18</s5>
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<s5>18</s5>
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<s5>19</s5>
</fC03><fC03 i1="05" i2="X" l="ENG"><s0>Bargaining</s0>
<s5>19</s5>
</fC03><fC03 i1="05" i2="X" l="SPA"><s0>Negociación</s0>
<s5>19</s5>
</fC03><fC03 i1="06" i2="X" l="FRE"><s0>Préférence</s0>
<s5>20</s5>
</fC03><fC03 i1="06" i2="X" l="ENG"><s0>Preference</s0>
<s5>20</s5>
</fC03><fC03 i1="06" i2="X" l="SPA"><s0>Preferencia</s0>
<s5>20</s5>
</fC03><fC03 i1="07" i2="X" l="FRE"><s0>Décomposition fonction</s0>
<s5>21</s5>
</fC03><fC03 i1="07" i2="X" l="ENG"><s0>Function decomposition</s0>
<s5>21</s5>
</fC03><fC03 i1="07" i2="X" l="SPA"><s0>Descomposición función</s0>
<s5>21</s5>
</fC03><fC03 i1="08" i2="X" l="FRE"><s0>Commercialisation</s0>
<s5>22</s5>
</fC03><fC03 i1="08" i2="X" l="ENG"><s0>Marketing</s0>
<s5>22</s5>
</fC03><fC03 i1="08" i2="X" l="SPA"><s0>Comercialización</s0>
<s5>22</s5>
</fC03><fC03 i1="09" i2="X" l="FRE"><s0>Fonction utilité</s0>
<s5>23</s5>
</fC03><fC03 i1="09" i2="X" l="ENG"><s0>Utility function</s0>
<s5>23</s5>
</fC03><fC03 i1="09" i2="X" l="SPA"><s0>Función utilidad</s0>
<s5>23</s5>
</fC03><fC03 i1="10" i2="X" l="FRE"><s0>Théorie utilité</s0>
<s5>24</s5>
</fC03><fC03 i1="10" i2="X" l="ENG"><s0>Utility theory</s0>
<s5>24</s5>
</fC03><fC03 i1="10" i2="X" l="SPA"><s0>Teoría utilidad</s0>
<s5>24</s5>
</fC03><fC03 i1="11" i2="X" l="FRE"><s0>Utilité attendue</s0>
<s5>25</s5>
</fC03><fC03 i1="11" i2="X" l="ENG"><s0>Expected utility</s0>
<s5>25</s5>
</fC03><fC03 i1="11" i2="X" l="SPA"><s0>Utilidad espera</s0>
<s5>25</s5>
</fC03><fC03 i1="12" i2="X" l="FRE"><s0>Modélisation</s0>
<s5>26</s5>
</fC03><fC03 i1="12" i2="X" l="ENG"><s0>Modeling</s0>
<s5>26</s5>
</fC03><fC03 i1="12" i2="X" l="SPA"><s0>Modelización</s0>
<s5>26</s5>
</fC03><fC03 i1="13" i2="X" l="FRE"><s0>Psychologie</s0>
<s5>27</s5>
</fC03><fC03 i1="13" i2="X" l="ENG"><s0>Psychology</s0>
<s5>27</s5>
</fC03><fC03 i1="13" i2="X" l="SPA"><s0>Psicología</s0>
<s5>27</s5>
</fC03><fC03 i1="14" i2="X" l="FRE"><s0>Algorithme apprentissage</s0>
<s5>28</s5>
</fC03><fC03 i1="14" i2="X" l="ENG"><s0>Learning algorithm</s0>
<s5>28</s5>
</fC03><fC03 i1="14" i2="X" l="SPA"><s0>Algoritmo aprendizaje</s0>
<s5>28</s5>
</fC03><fC03 i1="15" i2="X" l="FRE"><s0>Erreur mesure</s0>
<s5>41</s5>
</fC03><fC03 i1="15" i2="X" l="ENG"><s0>Measurement error</s0>
<s5>41</s5>
</fC03><fC03 i1="15" i2="X" l="SPA"><s0>Error medida</s0>
<s5>41</s5>
</fC03><fC03 i1="16" i2="X" l="FRE"><s0>.</s0>
<s4>INC</s4>
<s5>82</s5>
</fC03><fN21><s1>283</s1>
</fN21><fN44 i1="01"><s1>OTO</s1>
</fN44><fN82><s1>OTO</s1>
</fN82><pR><fA30 i1="01" i2="1" l="ENG"><s1>Multiagent system technologies. German conference</s1>
<s2>3</s2>
<s3>Koblenz DEU</s3>
<s4>2005-09-11</s4>
</fA30><server><NO>PASCAL 05-0408036 INIST</NO>
<ET>Estimating utility-functions for negotiating agents : Using conjoint analysis as an alternative approach to expected utility measurement</ET>
<AU>BECKER (Marc); CZAP (Hans); POPPENSIEKER (Malte); STOTZ (Alexander); EYMANN (Torsten); KLUGL (Franziska); LAMERSDORF (Winfried); KLUSCH (Matthias); HUHNS (Michael N.)</AU>
<AF>Department of Business Information Systems I, University of Trier/54296 Trier/Allemagne (1 aut., 2 aut., 3 aut., 4 aut.)</AF>
<DT>Publication en série; Congrès; Niveau analytique</DT>
<SO>Lecture notes in computer science; ISSN 0302-9743; Allemagne; Da. 2005; Vol. 3550; Pp. 94-105; Bibl. 23 ref.</SO>
<LA>Anglais</LA>
<EA>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.</EA>
<CC>001D02C</CC>
<FD>Système multiagent; Intelligence artificielle; Agent logiciel; Homme; Négociation; Préférence; Décomposition fonction; Commercialisation; Fonction utilité; Théorie utilité; Utilité attendue; Modélisation; Psychologie; Algorithme apprentissage; Erreur mesure; .</FD>
<ED>Multiagent system; Artificial intelligence; Software agents; Human; Bargaining; Preference; Function decomposition; Marketing; Utility function; Utility theory; Expected utility; Modeling; Psychology; Learning algorithm; Measurement error</ED>
<SD>Sistema multiagente; Inteligencia artificial; Hombre; Negociación; Preferencia; Descomposición función; Comercialización; Función utilidad; Teoría utilidad; Utilidad espera; Modelización; Psicología; Algoritmo aprendizaje; Error medida</SD>
<LO>INIST-16343.354000124419200090</LO>
<ID>05-0408036</ID>
</server>
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