Name
Abstract | ||
|---|---|---|
Argumentation can be used by a group of agents to discuss about the validity of hypotheses. In this paper we propose an argumentation-based frame-work for multiagent induction, where two agents learn separately from individual training sets, and then engage in an argumentation process in order to converge to a common hypothesis about the data. The result is a multiagent induction strategy in which the agents minimize the set of examples that they have to exchange (using argumentation) in order to converge to a shared hypothesis. The proposed strategy works for any induction algorithm which expresses the hypothesis as a set of rules. We show that the strategy converges to a hypothesis indistinguishable in training set accuracy from that learned by a centralized strategy. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.3233/978-1-60750-643-0-59 | CCIA |
Keywords | Field | DocType |
strategy converges,multiagent induction,argumentation process,multiagent induction strategy,shared hypothesis,induction algorithm,common hypothesis,argumentation-based example interchange,individual training set,centralized strategy,proposed strategy work | Training set,Computer science,Argumentation theory,Artificial intelligence | Conference |
Volume | ISSN | Citations |
220 | 0922-6389 | 0 |
PageRank | References | Authors |
0.34 | 8 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Santiago Ontaòón | 1 | 5 | 1.47 |
| Enric Plaza | 2 | 34 | 9.55 |