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20172026
most citedTowards learning domain-independent planning heuristics

4 citations · 5 across the 9 of their papers we have counts for

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cs.AI2025

Adaptive GR(1) Specification Repair for Liveness-Preserving Shielding in Reinforcement Learning

Tiberiu-Andrei Georgescu, Alexander W. Goodall, Dalal Alrajeh +2

Shielding is widely used to enforce safety in reinforcement learning (RL), ensuring that an agent's actions remain compliant with formal specifications. Classical shielding approac…

cs.AI2020

Combining Experts' Causal Judgments

Dalal Alrajeh, Hana Chockler, Joseph Y. Halpern

Consider a policymaker who wants to decide which intervention to perform in order to change a currently undesirable situation. The policymaker has at her disposal a team of experts…

cs.AI2019

Learning Neural Search Policies for Classical Planning

Pawel Gomoluch, Dalal Alrajeh, Alessandra Russo +1

Heuristic forward search is currently the dominant paradigm in classical planning. Forward search algorithms typically rely on a single, relatively simple variation of best-first s…

cs.AI2018

Learning Classical Planning Strategies with Policy Gradient

Pawel Gomoluch, Dalal Alrajeh, Alessandra Russo

A common paradigm in classical planning is heuristic forward search. Forward search planners often rely on simple best-first search which remains fixed throughout the search proces…

cs.AI20174 cited

Towards learning domain-independent planning heuristics

Pawel Gomoluch, Dalal Alrajeh, Alessandra Russo +1

Automated planning remains one of the most general paradigms in Artificial Intelligence, providing means of solving problems coming from a wide variety of domains. One of the key f…