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Alfonso Emilio Gerevini

6 accepted papers

2025

Learning Trajectories of Figurative Language for Pre-Trained Language Models

EMNLP 2025

Figurative language and figures of speech, such as metaphors and hyperboles, are used every day in written and oral communication among human beings. Nonetheless, this imaginative use of words in a non literal way requires a solid understanding of semantics and a deep real-world knowledge. In the lo

2024

An Effective Polynomial Technique for Compiling Conditional Effects Away

AAAI 2024technical

The paper introduces a novel polynomial compilation technique for the sound and complete removal of conditional effects in classical planning problems. Similar to Nebel's polynomial compilation of conditional effects, our solution also decomposes each action with conditional effects into several sim…

2024

Dealing with Numeric and Metric Time Constraints in PDDL3 via Compilation to Numeric Planning

AAAI 2024technical

This paper studies an approach to planning with PDDL3 constraints involving mixed propositional and numeric conditions, as well as metric time constraints. We show how the whole PDDL3 with instantaneous actions can be compiled away into a numeric planning problem without PDDL3 constraints, enablin…

2024

Planning for Temporally Extended Goals in Pure-Past Linear Temporal Logic (Extended Abstract)

IJCAI 2024poster

We study classical planning for temporally extended goals expressed in Pure-Past Linear Temporal Logic (PPLTL). PPLTL is as expressive as Linear-time Temporal Logic on finite traces (LTLf), but as shown in this paper, it is computationally much better behaved for planning. Specifically, we show…

2022

Planning with Qualitative Action-Trajectory Constraints in PDDL

IJCAI 2022poster

In automated planning the ability of expressing constraints on the structure of the desired plans is important to deal with solution quality, as well as to express control knowledge. In PDDL3, this is supported through state-trajectory constraints corresponding to a class of LTLf formulae. In this p…

Cited by 15SourcePDFScholar
2021

On-line Learning of Planning Domains from Sensor Data in PAL: Scaling up to Large State Spaces

AAAI 2021technical

We propose an approach to learn an extensional representation of a discrete deterministic planning domain from observations in a continuous space navigated by the agent actions. This is achieved through the use of a perception function providing the likelihood of a real-value observation being in a…

Cited by 14SourcePDFScholar