Yara KhalufGhent University | UGhent · Department of Information Technology
Yara Khaluf
Ph.D.
About
42
Publications
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Introduction
My research focuses on complex systems and distributed cognition in particular on collective decision-making.
I mainly interested in understanding and modeling how decisions are taken in a various range of natural collective and inspire that type of intelligence to inject in artificial systems. I use a variety of tools including mathematical modeling, multi-agent simulations, and physics-based simulations.
Additional affiliations
October 2015 - present
October 2015 - present
January 2014 - July 2015
Education
April 2006 - March 2009
September 1999 - June 2004
Publications
Publications (42)
Increased fragmentation caused by habitat loss represents a major threat to the persistence of animal populations. How fragmentation affects populations depends on the rate at which individuals move between spatially separated patches. Whereas negative effects of habitat loss on biodiversity are well-known, effects of fragmentation per se on popula...
Robot swarms have been used extensively to examine best-of-N decisions; however, most studies presume that robots can reliably estimate the quality values of the various options. In an attempt to bridge the gap to reality, in this study, we assume robots with low-quality sensors take inaccurate measurements in both directions of overestimating and...
![Conspecific encounter rates ζ\documentclass[12pt]{minimal}...](publication/356469299/figure/fig1/AS:1093351914913797@1637686818639/Conspecific-encounter-rates-zdocumentclass12ptminimal-usepackageamsmath_Q320.jpg)
![Group search efficiency η\documentclass[12pt]{minimal}...](publication/356469299/figure/fig2/AS:1093351919108096@1637686819066/Group-search-efficiency-edocumentclass12ptminimal-usepackageamsmath_Q320.jpg)
![Probability density function P(ηi)\documentclass[12pt]{minimal}...](publication/356469299/figure/fig3/AS:1093351919091712@1637686819277/Probability-density-function-Peidocumentclass12ptminimal-usepackageamsmath_Q320.jpg)
![The influence of the threshold τc\documentclass[12pt]{minimal}...](publication/356469299/figure/fig5/AS:1093351919095826@1637686819611/The-influence-of-the-threshold-tcdocumentclass12ptminimal-usepackageamsmath_Q320.jpg)
In collective foraging, interactions between conspecifics can be exploited to increase foraging efficiencies. Many collective systems exhibit short interaction ranges, making information about patches rich in resources only locally available. In environments wherein these patches are difficult to locate, collective systems might exhibit altruistic...
Foragers within a group might increase individual foraging efficiencies by using public information to assess local resource availability. This information is often expressed as a change in behavior at resource encounter, which can be detected by nearby individuals. When the resource landscape displays sufficient degrees of clustering or fractility...
Increased fragmentation caused by habitat loss presents a major threat to the persistence of animal populations. Whereas the negative effects of habitat loss on biodiversity are well-known, the effects of fragmentation per se on population dynamics and ecosystem stability remain less understood. How fragmentation affects populations is strongly det...
Building structures is a remarkable collective process but its automation remains an open challenge. Robot swarms provide a promising solution to this challenge. However, collective construction involves a number of difficulties regarding efficient robots allocation to the different activities, particularly if the goal is to reach an optimal constr...
A key aspect of foraging in robot swarms is optimizing the search efficiency when both the environment and target density are unknown. Hence, designing optimal exploration strategies is desirable. This paper proposes a novel approach that extends the individual Lévy walk to a collective one. To achieve this, we adjust the individual motion through...
Foraging for resources is critical to the survival of many animal species. When resources are scarce, individuals can benefit from interactions, effectively parallelizing the search process. Moreover, communication between conspecifics can result in aggregation around salient patches, rich in resources. However, individual foragers often have short...
Group interactions are widely observed in nature to optimize a set of critical collective behaviors, most notably sensing and decision making in uncertain environments. Nevertheless, these interactions are commonly modeled using local (proximity) networks, in which individuals interact within a certain spatial range. Recently, other interaction top...
Efficient random searches are essential to the survival of foragers searching for sparsely distributed targets. Lévy walks have been found to optimize the search over a wide range of constraints. When targets are distributed within patches, generating a spatial memory over the detected targets can be beneficial towards optimizing the search efficie...
Many real-world networks exhibit community structures and non-trivial clustering associated with the occurrence of a considerable number of triangular subgraphs known as triadic motifs. Triads are a set of distinct triangles that do not share an edge with any other triangle in the network. Network motifs are subgraphs that occur significantly more...
![Minimal average degree ⟨nm⟩(ps)\documentclass[12pt]{minimal}...](publication/335610410/figure/fig2/AS:962659532935204@1606527326874/Minimal-average-degree-nmpsdocumentclass12ptminimal-usepackageamsmath_Q320.jpg)
Decentralised systems composed of a large number of locally interacting agents often rely on coherent behaviour to execute coordinated tasks. Agents cooperate to reach a coherent collective behaviour by aligning their individual behaviour to the one of their neighbours. However, system noise, determined by factors such as individual exploration or...
In a collaborative society, sharing information is advantageous
for each individual as well as for the whole community. Maximizing
the number of agent-to-agent interactions per time
becomes an appealing behavior due to fast information spreading
that maximizes the overall amount of shared information.
However, if malicious agents are part of societ...
In many complex systems observed in nature, properties such as scalability, adaptivity, or rapid information exchange are often accompanied by the presence of features that are scale-free, i.e., that have no characteristic scale. Following this observation, we investigate the existence of scale-free features in artificial collective systems using s...
In model-based Reinforcement Learning, an agent aims to learn a transition model between attainable states. Since the agent initially has zero knowledge of the transition model, it needs to resort to random exploration in order to learn the model. In this work, we demonstrate how the Ornstein-Uhlenbeck process can be used as a sampling scheme to ge...
Behavioral disturbances of persons with dementia residing in a nursing home impose a significant burden on other residents and on the care staff. A social robot can provide an adequate technological support tool for the caregivers by approaching a resident that exhibits a behavioral disturbance. In this paper, we focus on how to position the robot...
Autonomous decision-making is a fundamental requirement for the intelligent behavior of individual agents and systems. For artificial systems, one of the key design prerequisites is providing the system with the ability to make proper decisions. Current literature on collective artificial systems designs decision-making mechanisms inspired mostly b...
Exploration of an unknown environment is one of the most prominent tasks for multi-robot systems. In this paper, we focus on the specific problem of how a swarm of simulated robots can collectively sample a particular environment feature. We propose an energy-efficient approach for collective sampling, in which we aim to optimize the statistical qu...
In order to efficiently execute tasks, autonomous collective systems are required to rapidly reach accurate consensus, no matter how the group is distributed over the environment. Finding consensus in a group of agents that are in motion is a particularly great challenge, especially at larger scales and extensive environments. Nevertheless, numerou...
We propose a novel application of the Ant Colony Optimization algorithm to efficiently allocate a swarm of homogeneous robots to a set of tasks that need to be accomplished by specific deadlines. We exploit the local communication between robots to periodically evaluate the quality of the allocation solutions, and agents select independently among...
One of the key tasks of autonomous mobile robots is to explore the unknown environment under limited energy and deadline conditions. In this paper, we focus on one of the most efficient random walks found in the natural and biological system, i.e., Lévy walk. We show how Lévy properties disappear in larger robot swarm sizes because of spatial inter...
Self-organized collective coordinated behaviour is an impressive phenomenon, observed in a variety of natural and artificial systems, in which coherent global structures or dynamics emerge from local interactions between individual parts. If the degree of collective integration of a system does not depend on size, its level of robustness and adapti...
Through this study, we introduce the idea of applying scheduling techniques to allocate spatial resources that are shared among multiple robots moving in a static environment and having temporal constraints on the arrival time to destinations. To illustrate this idea, we present an exemplified algorithm that plans and assigns a motion path to each...
In this paper, we show that non-uniform distributions in swarms of agents have an impact on the scalability of collective decision-making. In particular, we highlight the relevance of noise-induced bistability in very sparse swarm systems and the failure of these systems to scale. Our work is based on three decision models. In the first model, each...
This article investigates the use of the integral of linear birth-death processes in the context of analyzing swarm robotics systems. We show that when a robot swarm can be modeled as a linear birth-death process, well-established results can be used to compute the expected value and/or the distribution of important swarm performance measures, such...
Hybrid societies are self-organizing, collective systems, which are composed of different components, for example, natural and artificial parts (bio-hybrid) or human beings interacting with and through technical systems (socio-technical). Many different disciplines investigate methods and systems closely related to the design of hybrid societies. A...
Swarm robotics is a branch of collective robotics systems that offers a set of remarkable advantages over other systems. The global behavior of swarm systems emerges from the local rules implemented at the individual level. Therefore, characterizing a global performance obtained at the swarm level is one of the main challenges, especially under com...
Relating microscopic features (individual level) to macroscopic fea-
tures (swarm level) of self-organizing collective systems is challenging. In
this paper, we report the mathematical derivation of a macroscopic model
starting from a microscopic one for the example of collective decision-
making. The collective system is based on the application o...
Developing swarm robotics systems for real-time applications is a challenging mission. Task deadlines are among the kind of constraints which characterize a large set of real applications. This paper focuses on devising and analyzing a task allocation strategy that allows swarm robotics systems to execute tasks characterized by soft deadlines and t...
Swarm robotics is a branch of collective robotics that outperforms many other systems due to its large number of robots. It allows for performing several tasks that are beyond the capability of a single or multi robot systems. Its global behaviour emerges from the local rules implemented on the level of its individual robots. Thus, estimating the o...
Wireless sensor and ad-hoc networks have been integrated into many self-organized tasks, including self-organized real-time tasks. Swarm robotics is a new field of research, which offers a set of advantages like motion, redundancy, flexibility, etc. compared to both sensor networks and ad-hoc ones. On the other hand, there are some difficulties in...
Swarm robotics is a new area of research which has gained a lot of interest according to the advantages it offers over current distributed systems. It is becoming in many wireless distributed applications, the potential alternative for the existing systems. Time synchronization as a key concept in distributed systems, will be a main requirement in...
Cooperation is a key concept used in multi-robot systems for performing complex tasks. In swarm robotics, a self-organized cooperation is applied, where robots with limited intelligence cooperate and interact locally to build up the desired global behavior. In this paper, we are studying a mobile object tracking scenario performed by a swarm of rob...
In this paper a middleware for distributed automotive systems is developed. The goal of this middleware is to support the
load bal- ancing and service optimization in automotive infotainment and entertainment systems. These systems provide navigation,
telecommunication, Internet, audio/video and many other services where a kind of dynamic load bala...
Projects
Projects (8)
Special Issue link: https://www.mdpi.com/journal/applsci/special_issues/ai_robot_swarms
Dear Colleagues,
Swarm robotics is a research field that focuses on the combination of swarm intelligence and robotics. Swarm intelligence describes the mechanisms of intelligence achieved at the system (global) level resulting from individuals’ simple behaviors and intensive interactions. Swarm intelligence is observed across different natural systems, including ant colonies, bird colonies, etc. Importing swarm intelligence to simple robots evolves into a promising distributed and autonomous system that shows great potential in several application areas, collectively referred to as swarm robotics.
Today, despite the significant advantages of robot swarms (e.g., their high resilience and scalability), they are mostly still restricted to the laboratory. This is due to a number of challenges including the safety of these systems, the dynamic environments in which they are deployed, their coordination under realistic circumstances, and their communication mechanisms. To overcome such challenges and advance swarm robotics research, these systems can benefit from numerous approaches developed recently in the field of artificial intelligence, including machine learning algorithms, complex networks, advanced decision-making algorithms, and others.
Dr. Yara Khaluf
Guest Editor
