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This thesis addresses the problem of estimating the state in multi-agent decision and control systems. In particular, a novel approach to state estimation is developed that uses partial order theory in order to overcome some of the severe computational complexity issues arising in multi-agent systems. Within this approach, state estimation algorithms are developed, which enjoy proved convergence properties and are scalable with the number of agents. <p> The dynamic evolution of the systems under study are characterized by the interplay of continuous and discrete variables. Continuous variables usually represent physical quan- tities such as position, velocity, voltage, and current, while the discrete variables usually represent quantities internal to the decision protocol that is used for coordination, com- munication, and control. Within the proposed state estimation approach, the estimation of continuous and discrete variables is developed in the same mathematical framework, as a joint continuous-discrete space is considered for the estimator. This way, the dichotomy between the continuous and discrete world is overcome for the purpose of state estimation. <p> Application examples are considered, which include the state estimation in competi- tive multi-robot systems and in multi-agent discrete event systems, and the monitoring of distributed environments.