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We consider the problem of designing agents able to compute optimal decisionsby composing data from multiple sources to tackle tasks involving: (i) trackinga desired behavior while minimizing an agent-specific cost; (ii) satisfyingsafety constraints. After formulating the control problem, we show that this isconvex under a suitable assumption and find the optimal solution. Theeffectiveness of the results, which are turned in an algorithm, is illustratedon a connected cars application via in-silico and in-vivo experiments with realvehicles and drivers. All the experiments confirm our theoretical predictionsand the deployment of the algorithm on a real vehicle shows its suitability forin-car operation.

Optimal Decision-Making for Autonomous Agents via Data Composition