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Influence-based Decision-making in Uncertain Environments

Total Cost €


EC-Contrib. €






Project "INFLUENCE" data sheet

The following table provides information about the project.


Organization address
address: STEVINWEG 1
city: DELFT
postcode: 2628 CN

contact info
title: n.a.
name: n.a.
surname: n.a.
function: n.a.
email: n.a.
telephone: n.a.
fax: n.a.

 Coordinator Country Netherlands [NL]
 Project website
 Total cost 1˙475˙560 €
 EC max contribution 1˙475˙560 € (100%)
 Programme 1. H2020-EU.1.1. (EXCELLENT SCIENCE - European Research Council (ERC))
 Code Call ERC-2017-STG
 Funding Scheme ERC-STG
 Starting year 2018
 Duration (year-month-day) from 2018-02-01   to  2023-01-31


Take a look of project's partnership.

# participants  country  role  EC contrib. [€] 
1    TECHNISCHE UNIVERSITEIT DELFT NL (DELFT) coordinator 1˙475˙560.00


 Project objective

Decision-theoretic sequential decision making (SDM) is concerned with endowing an intelligent agent with the capability to choose actions that optimize task performance. SDM techniques have the potential to revolutionize many aspects of society and recent successes, e.g., agents that play Atari games and beat a world champion in the game of Go, have sparked renewed interest in this field.

However, despite these successes, fundamental problems of scalability prevents these methods from addressing other problems with hundreds or thousands of state variables. For instance, there is no principled way of computing an optimal or near-optimal traffic light control plan for an intersection that takes into account the current state of traffic in an entire city. I will develop one in this project.

To achieve this, I will develop a new class of influence-based SDM methods that overcome scalability issues for such problems by using novel ways of abstraction. Considered from a decentralized system perspective, the intersection’s local problem is manageable, but the influence that the rest of the network exerts on it is complex. The key idea is that by using (deep) machine learning methods, we can learn sufficiently accurate representations of such influence to facilitate near-optimal decisions.

This project will construct a theoretical framework for such approximate influence representations and SDM methods that use them. Scalability of these methods will be demonstrated by rigorous empirical evaluation on two simulated challenge domains: traffic lights control in an entire city, and robotic order picking in a large-scale autonomous warehouse.

If successful, INFLUENCE will produce a range of influence-based SDM algorithms that can, in a principled manner, deal with a broad range of very large complex problems consisting of hundreds or thousands of variables, thus making an important step towards realizing the promise of autonomous agent technology.


year authors and title journal last update
List of publications.
2019 Katt, Sammie; Oliehoek, Frans; Amato, Christopher
Bayesian Reinforcement Learning in Factored POMDPs
published pages: , ISSN: , DOI:
Proceedings of the 18th International Conference on Autonomous Agents and MultiAgent Systems 2019-09-02
2018 Oliehoek, Frans A; Savani, Rahul; Gallego, Jose; Pol, Elise van der; Groß, Roderich
Beyond Local Nash Equilibria for Adversarial Networks
published pages: , ISSN: , DOI:
Benelearn 2018 Pre-proceedings 2019-09-02

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The information about "INFLUENCE" are provided by the European Opendata Portal: CORDIS opendata.

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