Search Results for author: Elad Liebman

Found 7 papers, 1 papers with code

Pattern-Based Time-Series Risk Scoring for Anomaly Detection and Alert Filtering -- A Predictive Maintenance Case Study

no code implementations24 May 2024 Elad Liebman

In this paper, we propose a fast and efficient approach to anomaly detection and alert filtering based on sequential pattern similarities.

Anomaly Detection Fault Detection +1

N-Agent Ad Hoc Teamwork

no code implementations16 Apr 2024 Caroline Wang, Arrasy Rahman, Ishan Durugkar, Elad Liebman, Peter Stone

POAM is a policy gradient, multi-agent reinforcement learning approach to the NAHT problem, that enables adaptation to diverse teammate behaviors by learning representations of teammate behaviors.

Autonomous Driving Multi-agent Reinforcement Learning +4

Utilizing Mood-Inducing Background Music in Human-Robot Interaction

no code implementations28 Aug 2023 Elad Liebman, Peter Stone

This research fills this gap by reporting the results of an experiment in which human participants were required to complete a task in the presence of an autonomous agent while listening to background music.

Decision Making

DM$^2$: Decentralized Multi-Agent Reinforcement Learning for Distribution Matching

1 code implementation1 Jun 2022 Caroline Wang, Ishan Durugkar, Elad Liebman, Peter Stone

The theoretical analysis shows that under certain conditions, each agent minimizing its individual distribution mismatch allows the convergence to the joint policy that generated the target distribution.

Multi-agent Reinforcement Learning reinforcement-learning +2

Artificial Musical Intelligence: A Survey

no code implementations17 Jun 2020 Elad Liebman, Peter Stone

Computers have been used to analyze and create music since they were first introduced in the 1950s and 1960s.

BIG-bench Machine Learning Music Recommendation +1

Representative Selection in Non Metric Datasets

no code implementations26 Feb 2015 Elad Liebman, Benny Chor, Peter Stone

This paper considers the problem of representative selection: choosing a subset of data points from a dataset that best represents its overall set of elements.


DJ-MC: A Reinforcement-Learning Agent for Music Playlist Recommendation

no code implementations9 Jan 2014 Elad Liebman, Maytal Saar-Tsechansky, Peter Stone

In this work we present DJ-MC, a novel reinforcement-learning framework for music recommendation that does not recommend songs individually but rather song sequences, or playlists, based on a model of preferences for both songs and song transitions.

Music Recommendation Recommendation Systems +2

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