Spain

ECAI 2020

Dynamic Algorithm Configuration:Foundation of a New Meta-Algorithmic Framework

meta-algorithmic

dynamic algorithm configuration

reinforcement learning

AAMAS is the leading scientific conference for research in autonomous agents and multi-agent systems. The AAMAS conference series was initiated in 2002 as the merging of three respected scientific meetings: the International Conference on Multi-Agent Systems (ICMAS), the International Workshop on Agent Theories, Architectures, and Languages (ATAL), and the International Conference on Autonomous Agents (AA). The aim of the joint conference is to provide a single, high-profile, internationally-respected archival forum for scientific research in the theory and practice of autonomous agents and multi-agent systems.

Browse keynotes, discussions, panels and over 300 presentations.


AAMAS 2020

AAMAS is the leading scientific conference for research in autonomous agents and multi-agent systems. The AAMAS conference series was initiated in 2002 as the merging of three respected scientific meetings: the International Conference on Multi-Agent Systems (ICMAS), the International Workshop on Agent Theories, Architectures, and Languages (ATAL), and the International Conference on Autonomous Agents (AA). 

technical paper

ECAI2020 featured a full programme of technical papers, as well as many AI-related events of interest for researchers, students and all attendants who are interested on contemporary AI, including:




**An extensive programme of workshops, tutorials, and invited speakers**

**The Prestigious Applications of Intelligent Systems conference (PAIS)**

**The Frontiers in AI track sessions**

**The Lab To Market event**

**The Women in AI time**

**The EU Challenges forum**

… and many more!

ECAI 2020 featured a full programme of technical papers, as well as many AI-related events of interest for researchers, students and all attendants who are interested on contemporary AI.

CDeepEx: Contrastive Deep Explanations

Variational Objectives for Markovian Dynamics with Backward Simulation;

Combination of Active Learning and Self-Paced Learning for Deep Answer Selection with Bayesian Neural Network

Fleet-Wide Policy Iteration using Gaussian Processes

Learning Normative Behaviors through Abstraction

A Formal Model of Affordances for Flexible Robotic Task Execution

Meta-Embedding as Auxiliary Task Regularization

A New Framework of Evolutionary Multi-Objective Algorithms with an Unbounded External Archive

Neural Large Neighborhood Search for the Capacitated Vehicle Routing Problem

Improving Document Classification with Multi-Sense Embeddings

https://d3smihljt9218e.cloudfront.net/transcript/1256/subtitle/2f202140a52947fefabb014f9b6c8dec.vtt

English (automatic)

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CDeepEx: Contrastive Deep Explanations

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Learning Context-aware Task Reasoning for Meta Reinforcement Learning

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Next from ECAI 2020

CDeepEx: Contrastive Deep Explanations

Similar lecture

Learning Context-aware Task Reasoning for Meta Reinforcement Learning

Downloads