The importance of context-dependent learning in negotiation agents

FEDERICO HERNÁNDEZ; OMAR JUAN ALFREDO CHIOTTI; DAN EZEQUIEL KRÖHLING; ERNESTO CARLOS MARTÍNEZ

Buenos Aires

Congreso; 47 JAIIO - Jornadas Argentinas de Informática; 2018

SADIO Sociedad Argentina de Informática / Universidad de Palermo

Automated negotiation between articial agents is essential to deploy Cognitive Computing and Internet of Things. The behavior of a negotiating agent depends signicantly on the infuence of environmental conditions or contextual variables, since they affect not only a given agent preferences and strategies, but also those of other agents. Despite this, the existing literature on automated negotiation is scarce about how to properly account for the eect of context-relevant variables in learning and evolving strategies. In this paper, a novel context-driven representation for automated negotiation is proposed. Also, a simple negotiating agent that queries available information from its environment, internally models contextual variables, and learns how to take advantage of this knowledge by playing against himself using reinforcement learning is proposed. Through a set of episodes against negotiating agents in the existing literature, it is shown that it makes no sense to negotiate without taking context-relevant variables into account. The context-aware negotiating agent has been implemented in the GENIUS negotiation environment, and results obtained are significant and revealing.