Articles | Sciaria - Science Aria

Hasan Ferit Eniser, Songtuan Lin, Nicola Müller, Anastasia Isychev, Valentin Wüstholz, Isabel Valera, Jörg Hoffmann, Maria Christakis

Using Action-Policy Testing in RL to Reduce the Number of Bugs

Informatics

Reinforcement learning is becoming ever more prominent in solving combinatorial search problems, in particular ones where states are images. Prior wor...

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This paper presents a novel approach to improving the robustness of Reinforcement Learning policies by integrating action-policy testing directly into...

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Shizhe Zhao, Yancheng Wu, Zhongqiang Ren

Bi-Objective Search for the Traveling Salesman Problem with Time Windows and Vacant Penalties

Informatics

This paper investigates a Traveling Salesman Problem with Time Windows and Vacant Penalties (TSP-TW-VP), which plans a path to service a set of machin...

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The paper "Bi-Objective Search for the Traveling Salesman Problem with Time Windows and Vacant Penalties" investigates a significant extension to the...

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Devin Wild Thomas, Wheeler Ruml

Real-time Cost-algebraic Heuristic Search

Informatics

Planning under time pressure arises in many situations. Real-time heuristic search, in which an agent must compute its next action within a prespecifi...

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The paper "Real-time Cost-algebraic Heuristic Search" addresses a critical challenge in the field of real-time planning: the difficulty in proving the...

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Matan Sudry, Tom Jurgenson, Erez Karpas

Task and Motion Planning Using Infinite Completion Tree and Agnostic Skills

Robotics

This work builds upon existing task and motion planning (TAMP) frameworks by integrating pre-trained Sequencing Task-Agnostic Policies (STAP) and Effo...

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This paper presents a compelling advancement in Task and Motion Planning (TAMP) by introducing a hierarchical framework designed to tackle long-horizo...

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Lior Siag, Ariel Felner, Shahaf Shperberg

Heuristics for Bounded-Suboptimal Search

Informatics

In heuristic search, it is well-established that different types of heuristics are suited for optimal heuristic search (OHS) and unbounded suboptimal...

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This paper addresses a crucial gap in the field of heuristic search, specifically concerning bounded-suboptimal search (BSS). The authors rightly high...

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Yaakov Sherma, Eyal Weiss, Oren Salzman

From Agent Centric to Obstacle Centric Planning: A Makespan-Optimal Algorithm for the Multi-Agent Warehouse Rearrangement Problem

Robotics

The Multi-Agent Warehouse Rearrangement (MAWR) problem calls for computing agents plans such that they collectively rearrange a warehouse environment...

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This paper tackles the Multi-Agent Warehouse Rearrangement (MAWR) problem, a complex and practical variant of Multi-Agent Path Finding (MAPF) and Mult...

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Dominik Schreiber, Christoph Jabs, Jeremias Berg

From Scalable SAT to MaxSAT: Massively Parallel Solution Improving Search

Informatics

Maximum Satisfiability (MaxSAT) is an essential framework for combinatorial optimization at the core of automated reasoning. However, to date, no nota...

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The paper "From Scalable SAT to MaxSAT: Massively Parallel Solution Improving Search" addresses a critical gap in automated reasoning and combinatoria...

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Muhammad Suhail Saleem, Rishi Veerapaneni, Maxim Likhachev

Lazy Heuristic Search for Solving POMDPs with Expensive-to-Compute Belief Transitions

Robotics

Heuristic search solvers like RTDP-Bel and LAO* have proven effective for computing optimal and bounded sub-optimal solutions for Partially Observable...

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This paper addresses a critical practical limitation in solving Partially Observable Markov Decision Processes (POMDPs) using heuristic search methods...

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Keisuke Okumura, Hiroki Nagai

Lightweight and Effective Preference Construction in PIBT for Large-Scale Multi-Agent Pathfinding

Robotics

PIBT is a computationally lightweight algorithm that can be applied to a variety of multi-agent pathfinding (MAPF) problems, generating the next colli...

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This paper presents a valuable contribution to the field of multi-agent pathfinding (MAPF), specifically focusing on improving the performance of PIBT...

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This paper introduces Hierarchical DeepPruner, a novel framework designed to tackle the significant computational challenges posed by NP-hard Combinat...