A neural network-based model to learn agent's utility function
Learning opponents’ preferences has a great impact on the success of negotiation, specially, when there is partial information about opponents. This incomplete information can be effectively utilized by intelligent agents equipped with adaptive capacities to learn opponents’ preferences during negot...
| Main Authors: | , , , |
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| Format: | Conference or Workshop Item |
| Language: | English |
| Published: |
IEEE
2008
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| Online Access: | http://psasir.upm.edu.my/id/eprint/33085/1/A%20neural%20network-based%20model%20to%20learn%20agent%27s%20utility%20function.pdf |
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| author | Jazayeriy, Hamid Azmi Murad, Masrah Azrifah Sulaiman, Md. Nasir Udzir, Nur Izura |
| author_facet | Jazayeriy, Hamid Azmi Murad, Masrah Azrifah Sulaiman, Md. Nasir Udzir, Nur Izura |
| author_sort | Jazayeriy, Hamid |
| collection | UPM |
| description | Learning opponents’ preferences has a great impact on the success of negotiation, specially, when there is partial information about opponents. This incomplete information can be effectively utilized by intelligent agents equipped with adaptive capacities to learn opponents’ preferences during negotiation. This paper present a neural network based model, named ANUE, to estimate negotiators’ utility function. ANUE’s structure is inspired from mathematical interpretation of utility function. We have also presented eight test cases to evaluate ANUE’s performance where test cases cover all possible form of incomplete information concerning utility function. As a future work, we evaluate ANUE with proposed test cases. |
| first_indexed | 2024-03-06T08:25:04Z |
| format | Conference or Workshop Item |
| id | upm.eprints-33085 |
| institution | Universiti Putra Malaysia |
| language | English |
| last_indexed | 2024-03-06T08:25:04Z |
| publishDate | 2008 |
| publisher | IEEE |
| record_format | dspace |
| spelling | upm.eprints-330852019-05-28T07:35:28Z http://psasir.upm.edu.my/id/eprint/33085/ A neural network-based model to learn agent's utility function Jazayeriy, Hamid Azmi Murad, Masrah Azrifah Sulaiman, Md. Nasir Udzir, Nur Izura Learning opponents’ preferences has a great impact on the success of negotiation, specially, when there is partial information about opponents. This incomplete information can be effectively utilized by intelligent agents equipped with adaptive capacities to learn opponents’ preferences during negotiation. This paper present a neural network based model, named ANUE, to estimate negotiators’ utility function. ANUE’s structure is inspired from mathematical interpretation of utility function. We have also presented eight test cases to evaluate ANUE’s performance where test cases cover all possible form of incomplete information concerning utility function. As a future work, we evaluate ANUE with proposed test cases. IEEE 2008 Conference or Workshop Item PeerReviewed text en http://psasir.upm.edu.my/id/eprint/33085/1/A%20neural%20network-based%20model%20to%20learn%20agent%27s%20utility%20function.pdf Jazayeriy, Hamid and Azmi Murad, Masrah Azrifah and Sulaiman, Md. Nasir and Udzir, Nur Izura (2008) A neural network-based model to learn agent's utility function. In: 3rd International Symposium on Information Technology (ITSim'08), 26-28 Aug. 2008, Kuala Lumpur, Malaysia. . 10.1109/ITSIM.2008.4631653 |
| spellingShingle | Jazayeriy, Hamid Azmi Murad, Masrah Azrifah Sulaiman, Md. Nasir Udzir, Nur Izura A neural network-based model to learn agent's utility function |
| title | A neural network-based model to learn agent's utility function |
| title_full | A neural network-based model to learn agent's utility function |
| title_fullStr | A neural network-based model to learn agent's utility function |
| title_full_unstemmed | A neural network-based model to learn agent's utility function |
| title_short | A neural network-based model to learn agent's utility function |
| title_sort | neural network based model to learn agent s utility function |
| url | http://psasir.upm.edu.my/id/eprint/33085/1/A%20neural%20network-based%20model%20to%20learn%20agent%27s%20utility%20function.pdf |
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