The importance of context-dependent learning in negotiation agents


  • Dan Ezequiel Kröhling INGAR (CONICET/UTN)
  • Omar Chiotti
  • Ernesto Martínez



Agents, Automated Negotiation, Negotiation Intelligence, Internet of Things, Reinforcement Learning


Automated negotiation between artificial agents is essential to deploy Cognitive Computing and Internet of Things. The behavior of a negotiation agent depends significantly on the influence 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 effect of context-relevant variables in learning and evolving strategies. In this paper, a novel context-driven representation for automated negotiation is introduced. Also, a simple negotiation 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 other negotiation agents in the existing literature, it is shown using our context-aware agent that it makes no sense to negotiate without taking context-relevant variables into account. Our context-aware negotiation agent has been implemented in the GENIUS environment, and results obtained are significant and quite revealing.


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How to Cite

Kröhling, D. E., Chiotti, O., & Martínez, E. (2019). The importance of context-dependent learning in negotiation agents. Inteligencia Artificial, 22(63), 135–149.