Final published version, 277 KB, PDF document
Final published version
Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review
Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review
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TY - GEN
T1 - Multi-agent team formation
T2 - solving complex problems by aggregating opinions (Doctoral Consortium)
AU - Soriano Marcolino, Leandro
PY - 2015/3/4
Y1 - 2015/3/4
N2 - It is known that we can aggregate the opinions of different agents to find high-quality solutions to complex problems. However, choosing agents to form a team is still a great challenge. Moreover, it is essential to use a good aggregation methodology in order to unleash the potential of a given team in solving complex problems. In my thesis, I present two different novel models to aid in the team formation process. Moreover, I propose a new methodology for extracting rankings from existing agents. I show experimental results both in the Computer Go domain and in the building design domain.
AB - It is known that we can aggregate the opinions of different agents to find high-quality solutions to complex problems. However, choosing agents to form a team is still a great challenge. Moreover, it is essential to use a good aggregation methodology in order to unleash the potential of a given team in solving complex problems. In my thesis, I present two different novel models to aid in the team formation process. Moreover, I propose a new methodology for extracting rankings from existing agents. I show experimental results both in the Computer Go domain and in the building design domain.
KW - Multi-agent systems
KW - Distributed problem solving
M3 - Conference contribution/Paper
SP - 4257
EP - 4258
BT - Proceedings of the 29th Conference on Artificial Intelligence (AAAI 2015)
PB - AAAI
ER -