Name
Abstract | ||
|---|---|---|
Teamwork is now a critical competence in the higher education area, and it has become a critical task in educational and management environments. Unfortunately, looking for optimal or near optimal teams is a costly task for humans due to the exponential number of outcomes. For this reason, in this paper we present a computer-aided policy that facilitates the automatic generation of near optimal teams based on collective intelligence, coalition structure generation, and Bayesian learning. We carried out simulations in hypothetic classroom scenarios that show that the policy is capable of converging towards the optimal solution as long as students do not have great difficulties evaluating others. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1007/978-3-642-40846-5_17 | HYBRID ARTIFICIAL INTELLIGENT SYSTEMS |
Field | DocType | Volume |
Teamwork,Bayesian inference,Collective intelligence,Computer science,Knowledge management,Higher education,Management science | Conference | 8073 |
ISSN | Citations | PageRank |
0302-9743 | 1 | 0.41 |
References | Authors | |
10 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Juan M. Alberola | 1 | 109 | 17.14 |
| Elena del Val Noguera | 2 | 30 | 5.05 |
| Victor Sanchez-Anguix | 3 | 102 | 14.87 |
| Vicente Julián | 4 | 546 | 87.40 |