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Found 7 papers, 0 papers with code
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.
Contrastive Explanations of Centralized Multi-agent Optimization Solutions
We propose CMAoE, a domain-independent approach to obtain contrastive explanations by: (i) generating a new solution $S^\prime$ where property $P$ is enforced, while also minimizing the differences between $S$ and $S^\prime$; and (ii) highlighting the differences between the two solutions, with respect to the features of the objective function of the multi-agent system.
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.
Explaining Preference-driven Schedules: the EXPRES Framework
The EXPRES framework consists of: (i) an explanation generator that, based on a Mixed-Integer Linear Programming model, finds the best set of reasons that can explain an unsatisfied preference; and (ii) an explanation parser, which translates the generated explanations into human interpretable ones.
Optimal Admission Control for Multiclass Queues with Time-Varying Arrival Rates via State Abstraction
To improve the scalability of our approach to a greater number of task classes, we present an approximation based on state abstraction.
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.
