Publications associées à Planification (intelligence artificielle)

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What Face and Body Shapes Can Tell Us About Height

Pascal Fua, Helge Jochen Rhodin, Semih Günel

Recovering a person's height from a single image is important for virtual garment fitting, autonomous driving and surveillance. However, it is also very challenging without absolute scale information. Here, we examine the rarely addressed case, where camer ...

IEEE COMPUTER SOC2019

Manifold Learning and Optimal Control for Obstacle Avoidance in Autonomous Driving

Colin Neil Jones, Sanket Sanjay Diwale

A novel manifold learning approach is presented to incorporate computationally efficient obstacle avoidance constraints in optimal control algorithms. The method presented provides a significant computational benefit by reducing the number of constraints r ...

2019

Courtesy As a Means to Coordinate

Boi Faltings, Panayiotis Danassis

We investigate the problem of multi-agent coordination under rationality constraints. Specifically, role allocation, task assignment, resource allocation, etc. Inspired by human behavior, we propose a framework (CA^3NONY) that enables fast convergence to e ...

International Foundation for Autonomous Agents and Multiagent Systems2019

Incorporating Location Aspects in Process Integration Methodology

François Maréchal, Ivan Daniel Kantor, Hür Ebuzer Bütün

The large potential for waste resource and heat recovery in industry has been motivating research toward increasing efficiency. Process integration methods have proven to be effective tools in improving industrial sites while decreasing their resource and ...

2019

Distributed Coupled Learning Over Adaptive Networks

Ali H. Sayed, Sulaiman A S A E Alghunaim

This work develops an effective distributed algorithm for the solution of stochastic optimization problems that involve partial coupling among both local constraints and local cost functions. While the collection of networked agents is interested in discov ...

IEEE2018

Impacts of Constraints and Constraint Handling Strategies for Multi-Objective Mechanical Design Problems

Jürg Alexander Schiffmann, Cyril Picard

Multi-objective optimization tools are becoming increasingly popu-lar in mechanical engineering and allow decision-makers to better understand the inevitable trade-offs. Mechanical design problems can however combine properties that make the use of optimiz ...

2018

A Single-Phase, Proximal Path-Following Framework

Volkan Cevher, Quoc Tran Dinh, Anastasios Kyrillidis

We propose a new proximal path-following framework for a class of constrained convex problems. We consider settings where the nonlinear-and possibly nonsmooth-objective part is endowed with a proximity operator, and the constraint set is equipped with a se ...

INFORMS2018

SAT Based Exact Synthesis using DAG Topology Families

Giovanni De Micheli, Mathias Soeken, Winston Jason Haaswijk

SAT based exact synthesis is a powerful technique, with applications in logic optimization, technology mapping, and synthesis for emerging technologies. However, its runtime behavior can be unpredictable and slow. In this paper, we propose to add a new typ ...

IEEE2018

Planification (intelligence artificielle)

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What Face and Body Shapes Can Tell Us About Height

Consolidation of database check constraints

Manifold Learning and Optimal Control for Obstacle Avoidance in Autonomous Driving

Nested Convex Bodies are Chaseable

Courtesy As a Means to Coordinate

Incorporating Location Aspects in Process Integration Methodology

Distributed Coupled Learning Over Adaptive Networks

Impacts of Constraints and Constraint Handling Strategies for Multi-Objective Mechanical Design Problems

A Single-Phase, Proximal Path-Following Framework

SAT Based Exact Synthesis using DAG Topology Families

Impacts of Constraints and Constraint Handling Strategies for Multi-Objective Mechanical Design Problems

Consolidation of database check constraints

What Face and Body Shapes Can Tell Us About Height

Incorporating Location Aspects in Process Integration Methodology

Courtesy As a Means to Coordinate

Distributed Coupled Learning Over Adaptive Networks

A Single-Phase, Proximal Path-Following Framework

SAT Based Exact Synthesis using DAG Topology Families

Manifold Learning and Optimal Control for Obstacle Avoidance in Autonomous Driving

Nested Convex Bodies are Chaseable