This talk will cover algorithmic design principles for intelligent systems exhibiting anticipatory, flexible, autonomous, and sustainable behavior. In particular, you‚Äôll be exposed to anticipatory multi-objective machine learning strategies for automating the resolution of conflicts in sequential decision-making under multiple, noisy, and cost-adjusted optimization criteria. The goal of anticipatory machine learning is to improve decision processes by taking advantage of predictive modeling, data-driven simulation, and prescriptive analytics. You‚Äôll thus realize how anticipating multiple conflicting scenarios contributes for preserving the decision maker future freedom of action, as preferences are learned and refined over time.‚Äã You‚Äôll then be in a good position to understand how an anticipatory hypervolume-based multi-objective Bayesian metaheuristic can incorporate meta-preferences to improve financial portfolio selection in real and simulated markets. In addition, you‚Äôll learn about connections between conditional future hypervolume maximization and the causal entropic principle proposed by Wissner-Gross and Freer (2013). Finally, you‚Äôll be stimulated to engage in a discussion about the relevance of the anticipatory multi-objective approach to artificial general intelligence. I hope you join us for an exciting discussion!