ISISTAN   23985
INSTITUTO SUPERIOR DE INGENIERIA DEL SOFTWARE
Unidad Ejecutora - UE
artículos
Título:
Agents that learn how to generate arguments from other agents
Autor/es:
ARIEL MONTESERIN; ANALIA ADRIANA AMANDI
Revista:
NEW GENERATION COMPUTING
Editorial:
SPRINGER
Referencias:
Lugar: Berlin; Año: 2014 vol. 32 p. 31 - 58
ISSN:
0288-3635
Resumen:
Learning how to argue is a key ability for a negotiator agent. In this paper, we propose an approach that allows agents to learn how to build arguments by observing how other agents argue in a negotiation context. Particularly, our approach enables the agent to infer the rulesfor argument generation that other agents apply to build their arguments. To carry out this goal, the agent stores the arguments uttered by other agents and the facts of the negotiation context where each argument is uttered. Then, an algorithm for fuzzy generalized association rules is applied to discover the desired rules. This kind of algorithm allows us (a) to obtain general rules that can be applied to dierent negotiation contexts; and (b) to deal with the uncertainty about the knowledge of what facts of the context are taken into account by the agents. The experimental results showed that it is possible to infer argument generation rules from a reduced number of observed arguments.