3 papers
cs.AI2025
Subgoaling Relaxation-based Heuristics for Numeric Planning with Infinite Actions
Ãngel Aso-Mollar, Diego Aineto, Enrico Scala +1
Numeric planning with control parameters extends the standard numeric planning model by introducing action parameters as free numeric variables that must be instantiated during pla…
cs.AI2025
Handling Infinite Domain Parameters in Planning Through Best-First Search with Delayed Partial Expansions
Ãngel Aso-Mollar, Diego Aineto, Enrico Scala +1
In automated planning, control parameters extend standard action representations through the introduction of continuous numeric decision variables. Existing state-of-the-art approa…
cs.AI2025
An Efficient Approach for Cooperative Multi-Agent Learning Problems
Ãngel Aso-Mollar, Eva Onaindia
In this article, we propose a centralized Multi-Agent Learning framework for learning a policy that models the simultaneous behavior of multiple agents that need to coordinate to s…