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Vassilis Vassiliades

Vassilis Vassiliades
  • PhD
  • Group Leader at CYENS Centre of Excellence

About

48
Publications
11,350
Reads
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1,100
Citations
Current institution
CYENS Centre of Excellence
Current position
  • Group Leader
Additional affiliations
June 2019 - October 2020
Research Centre on Interactive Media, Smart Systems and Emerging Technologies - RISE LTD
Position
  • Group Leader
February 2019 - May 2019
Research Centre on Interactive Media, Smart Systems and Emerging Technologies - RISE LTD
Position
  • Research Associate
December 2017 - May 2018
National Institute for Research in Computer Science and Control
Position
  • Engineer
Education
January 2009 - July 2015
University of Cyprus
Field of study
  • Computer Science
October 2007 - October 2008
University of Birmingham
Field of study
  • Intelligent Systems Engineering
September 2003 - June 2007
University of Cyprus
Field of study
  • Computer Science

Publications

Publications (48)
Preprint
Full-text available
We focus on a relatively unexplored learning paradigm known as {\em Online Unsupervised Continual Learning} (O-UCL), where an agent receives a non-stationary, unlabeled data stream and progressively learns to identify an increasing number of classes. This paradigm is designed to model real-world applications where encountering novelty is the norm,...
Preprint
Full-text available
This work is a preliminary study on using Exploratory Landscape Analysis (ELA) for Quality Diversity (QD) problems. We seek to understand whether ELA features can potentially be used to characterise QD problems paving the way for automating QD algorithm selection. Our results demonstrate that ELA features are affected by QD optimisation differently...
Conference Paper
Quadruped robots have emerged as an evolving technology that currently leverages simulators to develop a robust controller capable of functioning in the real-world without the need for further training. However, since it is impossible to predict all possible real-world situations, our research explores the possibility of enabling them to continue l...
Article
Simulating crowds with realistic behaviors is a difficult but very important task for a variety of applications. Quantifying how a person balances between different conflicting criteria such as goal seeking, collision avoidance and moving within a group is not intuitive, especially if we consider that behaviors differ largely between people. Inspir...
Article
Full-text available
Model Inversion (MI) attacks, that aim to recover semantically meaningful reconstructions for each target class, have been extensively studied and demonstrated to be successful in the white-box setting. On the other hand, black-box MI attacks demonstrate low performance in terms of both effectiveness, i.e., reconstructing samples which are identifi...
Article
Full-text available
Developing expert systems that make use of artificial intelligence (AI) to provide predictive analytics as well as targeted recommendations for decision support has been gaining momentum in recent years. Both academia and industry are looking into creating such systems to solve real-world problems and tackle specific challenges. In our work, we inv...
Article
Full-text available
Many enterprises are under threat of targeted attacks aiming at data exfiltration. To launch such attacks, in recent years, attackers with their malware have exploited a covert channel that abuses the domain name system (DNS) named DNS tunneling. Although several research efforts have been made to detect DNS tunneling, the existing methods rely on...
Preprint
Full-text available
Deploying sophisticated deep learning models on embedded devices with the purpose of solving real-world problems is a struggle using today's technology. Privacy and data limitations, network connection issues, and the need for fast model adaptation are some of the challenges that constitute today's approaches unfit for many applications on the edge...
Chapter
Traditional optimization algorithms search for a single global optimum that maximizes (or minimizes) the objective function. Multimodal optimization algorithms search for the highest peaks in the search space that can be more than one. Quality-Diversity algorithms are a recent addition to the evolutionary computation toolbox that do not only search...
Preprint
Full-text available
Traditional optimization algorithms search for a single global optimum that maximizes (or minimizes) the objective function. Multimodal optimization algorithms search for the highest peaks in the search space that can be more than one. Quality-Diversity algorithms are a recent addition to the evolutionary computation toolbox that do not only search...
Article
Named Data Networking (NDN) has the potential to create a more secure future Internet. It is therefore crucial to investigate its vulnerabilities in order to make it safer against information leakage attacks. In NDN, malware inside an enterprise can encode confidential information into Interest names and send it to the attacker. One of the counterm...
Article
Full-text available
Most policy search (PS) algorithms require thousands of training episodes to find an effective policy, which is often infeasible with a physical robot. This survey article focuses on the extreme other end of the spectrum: how can a robot adapt with only a handful of trials (a dozen) and a few minutes? By analogy with the word “big-data,” we refer t...
Article
Full-text available
Named Data Networking (NDN) has emerged as a future networking architecture having the potential to replace the Internet. In order to do so, NDN needs to cope with inherent problems of the Internet such as attacks that cause information leakage from an enterprise. Since NDN has not yet been deployed on a large scale, it is currently unknown how suc...
Preprint
Full-text available
Most policy search algorithms require thousands of training episodes to find an effective policy, which is often infeasible with a physical robot. This survey article focuses on the extreme other end of the spectrum: how can a robot adapt with only a handful of trials (a dozen) and a few minutes? By analogy with the word "big-data", we refer to thi...
Article
Full-text available
Evolution has produced an astonishing diversity of species, each filling a different niche. Algorithms like MAP-Elites mimic this divergent evolutionary process to find a set of behaviorally diverse but high-performing solutions, called the elites. Our key insight is that species in nature often share a surprisingly large part of their genome, in s...
Preprint
Evolution has produced an astonishing diversity of species, each filling a different niche. Algorithms like MAP-Elites mimic this divergent evolutionary process to find a set of behaviorally diverse but high-performing solutions, called the elites. Our key insight is that species in nature often share a surprisingly large part of their genome, in s...
Conference Paper
Full-text available
The most data-efficient algorithms for reinforcement learning (RL) in robotics are based on uncertain dynamical models: after each episode, they first learn a dynamical model of the robot, then they use an optimization algorithm to find a policy that maximizes the expected return given the model and its uncertainties. It is often believed that this...
Article
Full-text available
The recently introduced Multi-dimensional Archive of Phenotypic Elites (MAP-Elites) is an evolutionary algorithm capable of producing a large archive of diverse, high-performing solutions in a single run. It works by discretizing a continuous feature space into unique regions according to the desired discretization per dimension. While simple, this...
Conference Paper
Full-text available
Illumination algorithms are a recent addition to the evolutionary computation toolbox that allows the generation of many diverse and high-performing solutions in a single run. Nevertheless, traditional multimodal optimization algorithms also search for diverse and high-performing solutions: could some multimodal optimization algorithms be better at...
Conference Paper
Full-text available
Illumination algorithms are a new class of evolutionary algorithms capable of producing large archives of diverse and high-performing solutions. Examples of such algorithms include Novelty Search with Local Competition (NSLC), the Multi-dimensional Archive of Phenotypic Elites (MAP-Elites) and the newly introduced Centroidal Voronoi Tessellation (C...
Preprint
Full-text available
The most data-efficient algorithms for reinforcement learning (RL) in robotics are based on uncertain dynamical models: after each episode, they first learn a dynamical model of the robot, then they use an optimization algorithm to find a policy that maximizes the expected return given the model and its uncertainties. It is often believed that this...
Conference Paper
Full-text available
The recently introduced Intelligent Trial-and-Error (IT&E) algorithm showed that robots can adapt to damage in a matter of a few trials. The success of this algorithm relies on two components: prior knowledge acquired through simulation with an intact robot, and Bayesian optimization (BO) that operates on-line, on the damaged robot. While IT&E lead...
Article
Full-text available
The recently introduced Multi-dimensional Archive of Phenotypic Elites (MAP-Elites) is an evolutionary algorithm capable of producing a large archive of diverse, high-performing solutions in a single run. It works by discretizing a continuous feature space into unique regions according to the desired discretization per dimension. While simple, this...
Article
Full-text available
The high probability of hardware failures prevents many advanced robots (e.g. legged robots) to be confidently deployed in real-world situations (e.g post-disaster rescue). Instead of attempting to diagnose the failure(s), robots could adapt by trial-and-error in order to be able to complete their tasks. However, the best trial-and-error algorithms...
Preprint
Full-text available
The high probability of hardware failures prevents many advanced robots (e.g., legged robots) from being confidently deployed in real-world situations (e.g., post-disaster rescue). Instead of attempting to diagnose the failures, robots could adapt by trial-and-error in order to be able to complete their tasks. In this situation, damage recovery can...
Conference Paper
Full-text available
Predictions on sequential data, when both the upstream and downstream information is important, is a difficult and challenging task. The Bidirectional Recurrent Neural Network (BRNN) architecture has been designed to deal with this class of problems. In this paper, we present the development and implementation of the Scaled Conjugate Gradient (SCG)...
Article
Full-text available
A central question in artificial intelligence is how to design agents capable of switching between different behaviors in response to environmental changes. Taking inspiration from neuroscience, we address this problem by utilizing artificial neural networks (NNs) as agent controllers, and mechanisms such as neuromodulation and synaptic gating. The...
Thesis
Full-text available
This thesis investigates adaptation in dynamic environments, by focusing on the areas of reinforcement learning (RL) and adaptive artificial neural networks (ANNs). In dynamic environments, there is a need for fast adaptation, and standard methods are not very efficient as they assume that the environment does not change. The purpose of this thesis i...
Article
Full-text available
We consider the problem of designing local reinforcement learning rules for artificial neural network (ANN) controllers. Motivated by the universal approximation properties of ANNs, we adopt an ANN representation for the learning rules, which are optimized using evolutionary algorithms. We evaluate the ANN rules in partially observable versions of...
Article
Full-text available
Filtering of Protein Secondary Structure Prediction (PSSP) aims to provide physicochemically realistic results, while it usually improves the predictive performance. We performed a comparative study on this challenging problem, utilizing both machine learning techniques and empirical rules and we found that combinations of the two lead to the highe...
Conference Paper
Full-text available
This paper compares and investigates single-agent reinforcement learning (RL) algorithms on the simple and an extended taxi problem domain, and multiagent RL algorithms on a multiagent extension of the simple taxi problem domain we created. In particular, we extend the Policy Hill Climbing (PHC) and the Win or Learn Fast-PHC (WoLF-PHC) algorithms b...
Article
Full-text available
This paper investigates multiagent reinforcement learning (MARL) in a general-sum game where the payoffs' structure is such that the agents are required to exploit each other in a way that benefits all agents. The contradictory nature of these games makes their study in multiagent systems quite challenging. In particular, we investigate MARL with s...
Conference Paper
Full-text available
Successful protein secondary structure prediction is an important step towards modelling protein 3D structure, with several practical applications. Even though in the last four decades several PSSP algorithms have been proposed, we are far from being accurate. The Bidirectional Recurrent Neural Network (BRNN) architecture of Baldi et al. [1] is cur...
Conference Paper
Full-text available
In this paper, we investigate the importance of rewards in Multiagent Reinforcement Learning in the context of the Iterated Prisoner's Dilemma. We use an evolutionary algorithm to evolve valid payoff structures with the aim of encouraging mutual cooperation. An exhaustive analysis is performed by investigating the effect of: i) the lower and upper...
Conference Paper
Full-text available
This paper investigates Multiagent Reinforcement Learning (MARL) in a general-sum game where the payoffs’ structure is such that the agents are required to exploit each other in a way that benefits all agents. The contradictory nature of these games makes their study in multiagent systems quite challenging. In particular, we investigate MARL with s...

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