Resumen de Non-Cooperative Games for Self-Interested Planning Agents
Jaume Magí Jordán Prunera 
Multi-Agent Planning (MAP) is a topic of growing interest that deals with the problem of automated planning in domains where multiple agents plan and act together in a shared environment. In most cases, agents in MAP are cooperative (altruistic) and work together towards a collaborative solution. However, when rational self-interested agents are involved in a MAP task, the ultimate objective is to find a joint plan that accomplishes the agents' local tasks while satisfying their private interests.
Among the MAP scenarios that involve self-interested agents, non-cooperative MAP refers to problems where non-strictly competitive agents feature common and conflicting interests. In this setting, conflicts arise when self-interested agents put their plans together and the resulting combination renders some of the plans non-executable, which implies a utility loss for the affected agents. Each participant wishes to execute its plan as it was conceived, but congestion issues and conflicts among the actions of the different plans compel agents to find a coordinated stable solution.
Non-cooperative MAP tasks are tackled through non-cooperative games, which aim at finding a stable (equilibrium) joint plan that ensures the agents' plans are executable (by addressing planning conflicts) while accounting for their private interests as much as possible. Although this paradigm reflects many real-life problems, there is a lack of computational approaches to non-cooperative MAP in the literature.
This PhD thesis pursues the application of non-cooperative games to solve non-cooperative MAP tasks that feature rational self-interested agents. Each agent calculates a plan that attains its individual planning task, and subsequently, the participants try to execute their plans in a shared environment. We tackle non-cooperative MAP from a twofold perspective. On the one hand, we focus on agents' satisfaction by studying desirable properties of stable solutions, such as optimality and fairness. On the other hand, we look for a combination of MAP and game-theoretic techniques capable of efficiently computing stable joint plans while minimizing the computational complexity of this combined task. Additionally, we consider planning conflicts and congestion issues in the agents' utility functions, which results in a more realistic approach.
To the best of our knowledge, this PhD thesis opens up a new research line in non-cooperative MAP and establishes the basic principles to attain the problem of synthesizing stable joint plans for self-interested planning agents through the combination of game theory and automated planning.
