TY - GEN
T1 - Learning the structure of utility graphs used in multi-issue negotiation through collaborative filtering
AU - Robu, Valentin
AU - La Poutré, Han
PY - 2009
Y1 - 2009
N2 - Graphical utility models represent powerful formalisms for modeling complex agent decisions involving multiple issues [2]. In the context of negotiation, it has been shown [10] that using utility graphs enables reaching Pareto-efficient agreements with a limited number of negotiation steps, even for high-dimensional negotiations involving complex complementarity/ substitutability dependencies between multiple issues. This paper considerably extends the results of [10], by proposing a method for constructing the utility graphs of buyers automatically, based on previous negotiation data. Our method is based on techniques inspired from item-based collaborative filtering, used in online recommendation algorithms. Experimental results show that our approach is able to retrieve the structure of utility graphs online, with a high degree of accuracy, even for highly non-linear settings and even if a relatively small amount of data about concluded negotiations is available.
AB - Graphical utility models represent powerful formalisms for modeling complex agent decisions involving multiple issues [2]. In the context of negotiation, it has been shown [10] that using utility graphs enables reaching Pareto-efficient agreements with a limited number of negotiation steps, even for high-dimensional negotiations involving complex complementarity/ substitutability dependencies between multiple issues. This paper considerably extends the results of [10], by proposing a method for constructing the utility graphs of buyers automatically, based on previous negotiation data. Our method is based on techniques inspired from item-based collaborative filtering, used in online recommendation algorithms. Experimental results show that our approach is able to retrieve the structure of utility graphs online, with a high degree of accuracy, even for highly non-linear settings and even if a relatively small amount of data about concluded negotiations is available.
UR - http://www.scopus.com/inward/record.url?scp=70350680089&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-03339-1_16
DO - 10.1007/978-3-642-03339-1_16
M3 - Conference contribution
AN - SCOPUS:70350680089
SN - 3642033377
SN - 9783642033377
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 192
EP - 206
BT - Multi-Agent Systems for Society - 8th Pacific Rim International Workshop on Multi-Agents, PRIMA 2005, Revised Selected Papers
T2 - 8th Pacific Rim International Workshop on Multi-Agents, PRIMA 2005
Y2 - 26 September 2005 through 28 September 2005
ER -