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CS Colloquium: Ariel Barel (Technion, Israeli Institute of Technology) - Applied Deep-Learning methods for Expediting Path Selection in Real-Time MAP

Speaker: Ariel Barel, Technion, Israeli Institute of Technology

Talk Title: Applied Deep-Learning methods for Expediting Path Selection in Real-Time MAP

Series: Computer Science Colloquium

Abstract: Multi-Agent Path Finding (MAPF) is an NP-hard problem that plays a key role in numerous domains ranging from warehouse automation to computer games. In this research we are given a large pre-calculated set of legal paths from all possible sources to all possible destinations. The aim is to select paths from this set such that they do not collide with static obstacles nor with each other and minimize the maximal execution time of all tasks (Makespan). This selection should be calculated in near real-time, i.e., extremely fast compared to classic MAPF algorithms.

We investigate how Deep-Learning methods may speed up the search process, as trained Neural Networks have potential to make computations extremely fast. Training dataset may be generated by solving the "online" problem offline. The idea is to train the network to recognize patterns in the training examples and apply them to new, previously unseen, settings of the problem, i.e., new pairs of sources and destinations. The main challenges are definition of NN architecture and input representation.

This work addresses well-formed environments where agents may wait indefinitely at their sources but must follow a wait-free path once deployed. Moreover, our framework allows assignment of multiple agents per source and requires that all calculations complete before the first deployment, making scheduling a key component of the solution.

This lecture satisfies requirements for CSCI 591: Research Colloquium.

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https://usc.zoom.us/j/98857434920

Meeting ID: 988 5743 4920
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Biography: Dr. Ariel Barel is an academic visitor at the Technion, Israeli Institute of Technology. He received the PhD degrees in Computer Science from the Technion in the field of Distributed Control of Multi-Agent Systems. His current interest also includes Machine Learning implementations to expedite traditional planning algorithms. For more info and publications visit his personal web page https://arielba.cswp.cs.technion.ac.il/


Host: Christopher Leet (cjleet@usc.edu), Sven Koenig (skoenig@usc.edu)

Webcast: https://usc.zoom.us/j/98857434920

Location: Online - Zoom

WebCast Link: https://usc.zoom.us/j/98857434920

Audiences: Everyone Is Invited

Contact: Computer Science Department