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Deep Reinforcement Learning and Mean-Variance Strategies for Responsible Portfolio Optimization
Portfolio optimization involves determining the optimal allocation of portfolio assets in order to maximize a given investment objective.
Surrogate Assisted Monte Carlo Tree Search in Combinatorial Optimization
Industries frequently adjust their facilities network by opening new branches in promising areas and closing branches in areas where they expect low profits.
Accelerating Cutting-Plane Algorithms via Reinforcement Learning Surrogates
Discrete optimization belongs to the set of $\mathcal{NP}$-hard problems, spanning fields such as mixed-integer programming and combinatorial optimization.
Contrastive Explanations of Plans Through Model Restrictions
We formally define model-based compilations in PDDL2. 1 of each constraint derived from a user question in the taxonomy, and empirically evaluate the compilations in terms of computational complexity.
Towards Efficient Anytime Computation and Execution of Decoupled Robustness Envelopes for Temporal Plans
One of the major limitations for the employment of model-based planning and scheduling in practical applications is the need of costly re-planning when an incongruence between the observed reality and the formal model is encountered during execution.
Towards Explainable AI Planning as a Service
Explainable AI is an important area of research within which Explainable Planning is an emerging topic.
Towards Providing Explanations for AI Planner Decisions
In order to engender trust in AI, humans must understand what an AI system is trying to achieve, and why.
