Concept

Automated planning and scheduling

Related publications (38)

Augmenting large language models with chemistry tools

Philippe Schwaller, Oliver Tobias Schilter, Andres Camilo Marulanda Bran, Carlo Baldassari

Large language models (LLMs) have shown strong performance in tasks across domains but struggle with chemistry-related problems. These models also lack access to external knowledge sources, limiting their usefulness in scientific applications. We introduce ...

Nature Portfolio2024

Ride-hail vehicle routing (RIVER) as a congestion game

Kenan Zhang

The RIde-hail VEhicle Routing (RIVER) problem describes how drivers in a ride-hail market form a dynamic routing strategy according to the expected reward in each zone of the market. We model this decision-making problem as a Markov decision process (MDP), ...

2023

From C/C plus plus Code to High-Performance Dataflow Circuits

Paolo Ienne, Andrea Guerrieri, Lana Josipovic

High-level synthesis (HLS) tools typically generate statically scheduled datapaths. Static scheduling implies that the resulting circuits have a hard time exploiting parallelism in code with potential memory dependences, with control dependences, or where ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2022

Robust Inverse Reinforcement Learning under Transition Dynamics Mismatch

Volkan Cevher, Luca Viano, Yu-Ting Huang, Kamalaruban Parameswaran

We study the inverse reinforcement learning (IRL) problem under a transition dynamics mismatch between the expert and the learner. Specifically, we consider the Maximum Causal Entropy (MCE) IRL learner model and provide a tight upper bound on the learner’s ...

2021

Reference tracking stochastic model predictive control over unreliable channels and bounded control actions

Colin Neil Jones

A stochastic model predictive control framework over unreliable Bernoulli communication channels, in the presence of unbounded process noise and under bounded control inputs, is presented for tracking a reference signal. The data losses in the control chan ...

2021

Safe Convex Learning under Uncertain Constraints

Maryam Kamgarpour, Andreas Krause, Ilnura Usmanova

We address the problem of minimizing a convex smooth function f(x) over a compact polyhedral set D given a stochastic zeroth-order constraint feedback model. This problem arises in safety-critical machine learning applications, such as personalized medicin ...

PMLR2019

Frequency-Aware Model Predictive Control

Marco Hutter, Farbod Farshidian

Transferring solutions found by trajectory optimization to robotic hardware remains a challenging task. When the optimization fully exploits the provided model to perform dynamic tasks, the presence of unmodeled dynamics renders the motion infeasible on th ...

2019

Consistent Tomography Under Partial Observations Over Adaptive Networks

Ali H. Sayed

This paper studies the problem of inferring whether an agent is directly influenced by another agent over a network. Agent i influences agent j if they are connected (according to the network topology), and if agent j uses the data from agent i to update i ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2019

Exploring The Borderlands Of The Gathering Problem

Rachid Guerraoui, El Mahdi El Mhamdi, Alexandre David Olivier Maurer, Vladislav Tempez

Problems of pattern formation have been extensively studied in distributed computing. One of this problems is the gathering problem: agents must gather at a same position in a distributed manner. When gathering is not possible, a close problem is the conve ...

2019

An Algorithm for Odor Source Localization based on Source Term Estimation

Alcherio Martinoli, Ali Marjovi, Faezeh Rahbar

Finding sources of airborne chemicals with mobile sensing systems finds applications across the security, safety, domestic, medical, and environmental domains. In this paper, we present an algorithm based on source term estimation for odor source localizat ...

2019

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