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Collective decision-making with goals

Arianna Novaro 1
1 IRIT-LILaC - Logique, Interaction, Langue et Calcul
IRIT - Institut de recherche en informatique de Toulouse
Abstract : Agents having to take a collective decision are often motivated by individual goals. In such scenarios, two key aspects need to be addressed. The first is defining how to select a winning alternative from the expressions of the agents. The second is making sure that agents will not manipulate the outcome. Agents should also be able to state their goals in a way that is expressive, yet not too burdensome. This dissertation studies the aggregation and the strategic component of multi-agent collective decisions where the agents use a compactly represented language. The languages we study are all related to logic: from propositional logic, to generalized CP-nets and linear temporal logic (LTL). Our main contribution is the introduction of the framework of goal-based voting, where agents submit individual goals expressed as formulas of propositional logic. Classical aggregation functions from voting, judgment aggregation, and belief merging are adapted to this setting and studied axiomatically and computationally. Desirable axiomatic properties known in the literature of social choice theory are generalized to this new type of propositional input, as well as the standard complexity problems aimed at determining the result. Another important contribution is the study of the aggregation of generalized CP-nets coming from multiple agents, i.e., CP-nets where the precondition of the preference statement is a propositional formula. We use different aggregators to obtain a collective ordering of the possible outcomes. Thanks to this thesis, two lines of research are thus bridged: the one on the aggregation of complete CP-nets, and the one on the generalization of CP-nets to incomplete preconditions. We also contribute to the study of strategic behavior in both collective decision-making and game-theoretic settings. The framework of goal-based voting is studied again under the assumption that agents can now decide to submit an untruthful goal if by doing so they can get a better outcome. The focus is on three majoritarian voting rules which are found to be manipulable. Therefore, we study restrictions on both the language of the goals and on the strategies allowed to the agents to discover islands of strategy-proofness. We also present a game-theoretic extension of a recent model of opinion diffusion over networks of influence. In the influence games defined here, agents hold goals expressed as formulas of LTL and they can choose whether to use their influence power to make sure that their goal is satisfied. Classical solution concepts such as weak dominance and winning strategy are studied for influence games, in relation to the structure of the network and the goals of the agents. Finally, we introduce a novel class of concurrent game structures (CGS) in which agents can have shared control over a set of propositional variables. Such structures are used for the interpretation of formulas of alternating-time temporal logic, thanks to which we can express the existence of a winning strategy for an agent in a repeated game (as, for instance, the influence games mentioned above). The main result shows by means of a clever construction that a CGS with shared control can be represented as a CGS with exclusive control. In conclusion, this thesis provides a valuable contribution to the field of collective decision-making by introducing a novel framework of voting based on individual propositional goals, it studies for the first time the aggregation of generalized CP-nets, it extends a framework of opinion diffusion by modelling rational agents who use their influence power as they see fit, and it provides a reduction of shared to exclusive control in CGS for the interpretation of logics of strategic reasoning. By using different logical languages, agents can thus express their goals and preferences over the decision to be taken, and desirable properties of the decision process can be ensured.
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Arianna Novaro. Collective decision-making with goals. Artificial Intelligence [cs.AI]. Université Paul Sabatier - Toulouse III, 2019. English. ⟨NNT : 2019TOU30179⟩. ⟨tel-02626791⟩

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