Roni Stern

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University of Southern California ∙ PARC ∙ Ben-Gurion University of the Negev

Playing Angry Birds with a Domain-Independent PDDL+ Planner

This demo paper presents the first system for playing the popular Angry ...
07/09/2021 ∙ by Wiktor Piotrowski, et al. ∙ 0 ∙ share

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Safe Learning of Lifted Action Models

Creating a domain model, even for classical, domain-independent planning...
07/09/2021 ∙ by Brendan Juba, et al. ∙ 0 ∙ share

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Partial Disclosure of Private Dependencies in Privacy Preserving Planning

In collaborative privacy preserving planning (CPPP), a group of agents j...
02/14/2021 ∙ by Rotem Lev Lehman, et al. ∙ 0 ∙ share

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Improving Continuous-time Conflict Based Search

Conflict-Based Search (CBS) is a powerful algorithmic framework for opti...
01/24/2021 ∙ by Anton Andreychuk, et al. ∙ 0 ∙ share

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Anomaly Detection for Aggregated Data Using Multi-Graph Autoencoder

In data systems, activities or events are continuously collected in the ...
01/11/2021 ∙ by Tomer Meirman, et al. ∙ 0 ∙ share

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Revisiting Bounded-Suboptimal Safe Interval Path Planning

Safe-interval path planning (SIPP) is a powerful algorithm for finding a...
06/01/2020 ∙ by Konstantin Yakovlev, et al. ∙ 0 ∙ share

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Bidding in Spades

We present a Spades bidding algorithm that is superior to recreational h...
12/24/2019 ∙ by Gal Cohensius, et al. ∙ 5 ∙ share

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Learning Test Traces

Modern software projects include automated tests written to check the pr...
09/04/2019 ∙ by Eyal Hadad, et al. ∙ 0 ∙ share

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Predicting Software Tests Traces

Modern software projects include automated tests written to check the pr...
09/04/2019 ∙ by Eyal Hadad, et al. ∙ 0 ∙ share

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Multi-Agent Pathfinding: Definitions, Variants, and Benchmarks

The MAPF problem is the fundamental problem of planning paths for multip...
06/19/2019 ∙ by Roni Stern, et al. ∙ 5 ∙ share

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Multi-Agent Pathfinding (MAPF) with Continuous Time

MAPF is the problem of finding paths for multiple agents such that every...
01/16/2019 ∙ by Anton Andreychuk, et al. ∙ 0 ∙ share

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Modifying Optimal SAT-based Approach to Multi-agent Path-finding Problem to Suboptimal Variants

In multi-agent path finding (MAPF) the task is to find non-conflicting p...
07/02/2017 ∙ by Pavel Surynek, et al. ∙ 0 ∙ share

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Efficient, Safe, and Probably Approximately Complete Learning of Action Models

In this paper we explore the theoretical boundaries of planning in a set...
05/24/2017 ∙ by Roni Stern, et al. ∙ 0 ∙ share

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Roni Stern

Featured Co-authors

Sven Koenig

Konstantin Yakovlev

Reshef Meir

T. K. Satish Kumar

Pavel Surynek

Brendan Juba

Hang Ma

Jiaoyang Li

Gilad Katz

Anton Andreychuk

Liron Cohen

Playing Angry Birds with a Domain-Independent PDDL+ Planner

Safe Learning of Lifted Action Models

Partial Disclosure of Private Dependencies in Privacy Preserving Planning

Improving Continuous-time Conflict Based Search

Anomaly Detection for Aggregated Data Using Multi-Graph Autoencoder

Revisiting Bounded-Suboptimal Safe Interval Path Planning

Bidding in Spades

Learning Test Traces

Predicting Software Tests Traces

Multi-Agent Pathfinding: Definitions, Variants, and Benchmarks

Multi-Agent Pathfinding (MAPF) with Continuous Time

Modifying Optimal SAT-based Approach to Multi-agent Path-finding Problem to Suboptimal Variants

Efficient, Safe, and Probably Approximately Complete Learning of Action Models