activity
20172020
most citedTowards learning domain-independent planning heuristics

4 citations · 5 across the 2 of their papers we have counts for

collaborators

6 papers

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.LO20191 cited

Minimal Assumptions Refinement for GR(1) Specifications

Davide G. Cavezza, Dalal Alrajeh, Andras Gyorgy

Reactive synthesis is concerned with finding a correct-by-construction controller from formal specifications, typically expressed in Linear Temporal Logic (LTL). The specifications…

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.LO2018

A Weakness Measure for GR(1) Formulae

Davide G. Cavezza, Dalal Alrajeh, András György

In spite of the theoretical and algorithmic developments for system synthesis in recent years, little effort has been dedicated to quantifying the quality of the specifications use…

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…