Learning and adaptation of strategies in automated negotiations between context-aware agents

Abstract

This work presents the hypothesis that guided the research efforts and a summary of the contributions of the doctoral thesis '`Aprendizaje y adaptación de estrategias para negociación automatizada entre agentes conscientes del contexto'. Succinctly, the thesis focuses on agents for automated bilateral negotiations that make use of the context as a key source of information to learn and adapt negotiation strategies in two levels of temporal abstraction. At the highest level, agents employ reinforcement learning to select strategies according to contextual circumstances. At the lowest level, agents use Gaussian Processes and artificial Theory of Mind to model their opponents and adapt their strategies. Agents are then tested in two Peer-to-Peer markets comprising an Eco-Industrial Park and a Smart Grid. The results highlight the significance for the automation of bilateral negotiations of incorporating the context as an informative source.