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  • 1.
    Al-Rawi, Mohammed
    et al.
    Universidade de Aveiro, Portugal.
    Elmgren, Fredrik
    DeepVision AB, Linköping, Sweden.
    Frasheri, Mirgita
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Curuklu, Baran
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Yuan, Xin
    Universidad Politecnica de Madrid, Spain.
    Martínez, José-Fernán
    Universidad Politecnica de Madrid, Spain.
    Bastos, Joaquim
    Instituto de Telecomunicações - Pólo de Aveiro, Portugal.
    Rodriguez, Jonathan
    Universidade de Aveiro, Portugal.
    Pinto, Marc
    ECA Robotics, France.
    Algorithms for the Detection of First Bottom Returns and Objects in the Water Column in Side-Scan Sonar Images2017In: OCEANS '17 A Vision for our Marine Future OCEANS '17, Aberdeen, United Kingdom, 2017Conference paper (Refereed)
    Abstract [en]

    Underwater imaging has become an active research area in recent years as an effect of increased interest in underwater environments and is getting potential impact on the world economy, in what is called blue growth. Since sound propagates larger distances than electromagnetic waves underwater, sonar is typically used for underwater imaging. One interesting sonar image setting is comprised of using two parts (left and right) and is usually referred to as sidescan sonar. The image resulted from sidescan sonars, which is called waterfall image, usually has to distinctive parts, the water column and the image seabed. Therefore, the edge separating these two parts, which is called the first bottom return, is the real distance between the sonar and the seabed bottom (which is equivalent to sensor primary altitude). The sensory primary altitude can be measured if the imaging sonar is complemented by interferometric sonar, however, simple sonar systems have no way to measure the first bottom returns other than signal processing techniques. In this work, we propose two methods to detect the first bottom returns; the first is based on smoothing cubic spline regression and the second is based on a moving average filter to detect signal variations. The results of both methods are compared to the sensor primary altitude and have been successful in 22 images out of 25.

  • 2.
    Enoiu, Eduard Paul
    et al.
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Frasheri, Mirgita
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Test Agents: Adaptive, Autonomous and Intelligent Test Cases2018Manuscript (preprint) (Other academic)
    Abstract [en]

    Growth of software size, lack of resources to perform regression testing, and failure to detect bugs faster have seen increased reliance on continuous integration and test automation. Even with greater hardware and software resources dedicated to test automation, software testing is faced with enormous challenges, resulting in increased dependence on complex mechanisms for automated test case selection and prioritisation as part of a continuous integration framework. These mechanisms are currently using simple entities called test cases that are concretely realised as executable scripts. Our key idea is to provide test cases with more reasoning, adaptive behaviour and learning capabilities by using the concepts of intelligent software agents. We refer to such test cases as test agents. The model that underlie a test agent is capable of flexible and autonomous actions in order to meet overall testing objectives. Our goal is to increase the decentralisation of regression testing by letting test agents to know for themselves when they should be executing, how they should update their purpose, and when they should interact with each other. In this paper, we envision software test agents that display such adaptive autonomous behaviour. Emerging developments and challenges regarding the use of test agents are explored-in particular, new research that seeks to use adaptive autonomous agents in software testing.

  • 3.
    Enoiu, Eduard Paul
    et al.
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Frasheri, Mirgita
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Test agents: The next generation of test cases2019In: Proceedings - 2019 IEEE 12th International Conference on Software Testing, Verification and Validation Workshops, ICSTW 2019, Institute of Electrical and Electronics Engineers Inc. , 2019, p. 305-308Conference paper (Refereed)
    Abstract [en]

    Growth of software size, lack of resources to perform regression testing, and failure to detect bugs faster have seen increased reliance on continuous integration and test automation. Even with greater hardware and software resources dedicated to test automation, software testing is faced with enormous challenges, resulting in increased dependence on centralized and complex mechanisms for automated test case selection as part of continuous integration. These mechanisms are currently using static entities called test cases that are concretely realized as executable scripts. Our key vision is to provide test cases with more reasoning, adaptive behavior and learning capabilities by using the concepts of software agents. We refer to such test cases as test agents. The model that underlie a test agent is capable of flexible and autonomous actions in order to meet overall testing objectives. Our goal is to increase the decentralization of regression testing by letting test agents to know for themselves when they should be executing, how they should update their purpose, and when they should interact with each other. In this paper, we envision test agents that display such adaptive autonomous behavior. Existing and emerging developments and challenges regarding the use of test agents are explored - in particular, new research that seeks to use adaptive autonomous agents in software testing. 

  • 4.
    Frasheri, Mirgita
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Collaborative adaptive autonomous agents2018In: Proceedings of the International Joint Conference on Autonomous Agents and Multiagent Systems, AAMAS, International Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS) , 2018, Vol. 3, p. 1740-1742Conference paper (Refereed)
  • 5.
    Frasheri, Mirgita
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Collaborative Adaptive Autonomous Agents2018Licentiate thesis, comprehensive summary (Other academic)
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  • 6.
    Frasheri, Mirgita
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Modelling and Control of the Collaborative Behavior of Adaptive Autonomous Agents2020Doctoral thesis, comprehensive summary (Other academic)
    Abstract [en]

    Research on autonomous agents and vehicles has gained momentum in the past years, which is reflected in the extensive body of literature and the investment of big players of the industry in the development of products such as self-driving cars. Additionally, these systems are envisioned to continuously communicate and cooperate with one another in order to adapt to dynamic circumstances and unforeseeable events, and as a result will they fulfil their goals even more efficiently.The facilitation of such dynamic collaboration and the modelling of interactions between different actors (software agents, humans) remains an open challenge.This thesis tackles the problem of enabling dynamic collaboration by investigating the automated adjustment of autonomy of different agents, called Adaptive Autonomy (AA). An agent, in this context, is a software able to process and react to sensory inputs in the environment in which it is situated in, and is additionally capable of autonomous actions. In this work, the collaborative adaptive autonomous behaviour of agents is shaped by their willingness to interact with other agents, that captures the disposition of an agent to give and ask for help, based on different factors that represent the agent's state and its interests.The AA approach to collaboration is used in two different domains: (i) the hunting mobile search problem, and (ii) the coverage problem of mobile wireless sensor networks. In both cases, the proposed approach is compared to state-of-art methods.Furthermore, the thesis contributes on a conceptual level by combining and integrating the AA approach -- which is purely distributed -- with a high-level mission planner, in order to exploit the ability of dealing with local and contingent problems through the AA approach, while minimising the requests for a re-plan to the mission planner.

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  • 7.
    Frasheri, Mirgita
    et al.
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Cano-Garcia, Jose
    University of Malaga, Spain.
    Gonzalez-Parada, Eva
    University of Malaga, Spain.
    Curuklu, Baran
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Ekström, Mikael
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Papadopoulos, Alessandro
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Urdiales, Cristina
    University of Malaga, Spain.
    Adaptive Autonomy in Wireless Sensor Networks2020Conference paper (Refereed)
    Abstract [en]

    Moving nodes in a Mobile Wireless Sensor Network (MWSN) typically have two maintenance objectives: (i) extend the coverage of the network as long as possible to a target area, and (ii) extend the longevity of the network as much as possible. As nodes move and also route traffic in the network, their battery levels deplete differently for each node. Dead nodes lead to loss of connectivity and even to disengaging full parts of the network. Several reactive and rule-based approaches have been proposed to solve this issue by adapting redeployment to depleted nodes. However, in large networks a cooperative approach may increase performance by taking the evolution of node battery and traffic into account. In this paper, we present a hybrid agent-based architecture that addresses the problem of depleting nodes during the maintenance phase of a MWSN. Agents, each assigned to a node, collaborate and adapt their behaviour to their battery levels. The collaborative behavior is modeled through the willingness to interact abstraction, which defines when agents ask and give help to one another. Thus, depleting nodes may ask to be replaced by healthier counterparts and move to areas with less traffic or to a collection point. At the lower level, negotiations trigger a reactive navigation behaviour based on Social Potential Fields (SPF). It is shown that the proposed method improves coverage and extends network longevity in an environment without obstacles as compared to SPF alone.

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  • 8.
    Frasheri, Mirgita
    et al.
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Curuklu, Baran
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Ekström, Mikael
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Analysis of perceived helpfulness in adaptive autonomous agent populations2018In: Transactions on Computational Collective Intelligence XXVIII, Springer Verlag , 2018, Vol. 10780, p. 221-252Conference paper (Refereed)
    Abstract [en]

    Adaptive autonomy allows agents to change their autonomy levels based on circumstances, e.g. when they decide to rely upon one another for completing tasks. In this paper, two configurations of agent models for adaptive autonomy are discussed. In the former configuration, the adaptive autonomous behavior is modeled through the willingness of an agent to assist others in the population. An agent that completes a high number of tasks, with respect to a predefined threshold, increases its willingness, and vice-versa. Results show that, agents complete more tasks when they are willing to give help, however the need for such help needs to be low. Agents configured to be helpful will perform well among alike agents. The second configuration extends the first by adding the willingness to ask for help. Furthermore, the perceived helpfulness of the population and of the agent asking for help are used as input in the calculation of the willingness to give help. Simulations were run for three different scenarios. (i) A helpful agent which operates among an unhelpful population, (ii) an unhelpful agent which operates in a helpful populations, and (iii) a population split in half between helpful and unhelpful agents. Results for all scenarios show that, by using such trait of the population in the calculation of willingness and given enough interactions, helpful agents can control the degree of exploitation by unhelpful agents. © Springer International Publishing AG, part of Springer Nature 2018.

  • 9.
    Frasheri, Mirgita
    et al.
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Curuklu, Baran
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Ekström, Mikael
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Comparison Between Static and Dynamic Willingness to Interact in Adaptive Autonomous Agents2018In: Proceedings of the 10th International Conference on Agents and Artificial Intelligence, 2018, Vol. 1, p. 258-267Conference paper (Refereed)
    Abstract [en]

    Adaptive autonomy (AA) is a behavior that allows agents to change their autonomy levels by reasoning on their circumstances. Previous work has modeled AA through the willingness to interact, composed of willingness to ask and give assistance. The aim of this paper is to investigate, through computer simulations, the behavior of agents given the proposed computational model with respect to different initial configurations, and level of dependencies between agents. Dependency refers to the need for help that one agent has. Such need can be fulfilled by deciding to depend on other agents. Results show that, firstly, agents whose willingness to interact changes during run-time perform better compared to those with static willingness parameters, i.e. willingness with fixed values. Secondly, two strategies for updating the willingness are compared, (i) the same fixed value is updated on each interaction, (ii) update is done on the previous calculated value. The maximum number of completed tasks which need assistance is achieved for (i), given specific initial configurations.

  • 10.
    Frasheri, Mirgita
    et al.
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Curuklu, Baran
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Ekström, Mikael
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Papadopoulos, Alessandro
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Adaptive Autonomy in a Search and Rescue Scenario2018In: International Conference on Self-Adaptive and Self-Organizing Systems, SASO, Volume 2018-September, 15 January 2019, 2018, p. 150-155Conference paper (Refereed)
    Abstract [en]

    Adaptive autonomy plays a major role in the design of multi-robots and multi-agent systems, where the need of collaboration for achieving a common goal is of primary importance. In particular, adaptation becomes necessary to deal with dynamic environments, and scarce available resources. In this paper, a mathematical framework for modelling the agents' willingness to interact and collaborate, and a dynamic adaptation strategy for controlling the agents' behavior, which accounts for factors such as progress toward a goal and available resources for completing a task among others, are proposed. The performance of the proposed strategy is evaluated through a fire rescue scenario, where a team of simulated mobile robots need to extinguish all the detected fires and save the individuals at risk, while having limited resources. The simulations are implemented as a ROS-based multi agent system, and results show that the proposed adaptation strategy provides a more stable performance than a static collaboration policy. 

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  • 11.
    Frasheri, Mirgita
    et al.
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Cürüklü, Baran
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Ekström, Mikael
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Towards Collaborative Adaptive Autonomous Agents2017In: ICAART: PROCEEDINGS OF THE 9TH INTERNATIONAL CONFERENCE ON AGENTS AND ARTIFICIAL INTELLIGENCE, VOL 1, 2017, p. 78-87Conference paper (Refereed)
    Abstract [en]

    Adaptive autonomy enables agents operating in an environment to change, or adapt, their autonomy levels by relying on tasks executed by others. Moreover, tasks could be delegated between agents, and as a result decision-making concerning them could also be delegated. In this work, adaptive autonomy is modeled through the willingness of agents to cooperate in order to complete abstract tasks, the latter with varying levels of dependencies between them. Furthermore, it is sustained that adaptive autonomy should be considered at an agent's architectural level. Thus the aim of this paper is two-fold. Firstly, the initial concept of an agent architecture is proposed and discussed from an agent interaction perspective. Secondly, the relations between static values of willingness to help, dependencies between tasks and overall usefulness of the agents' population are analysed. The results show that a unselfish population will complete more tasks than a selfish one for low dependency degrees. However, as the latter increases more tasks are dropped, and consequently the utility of the population degrades. Utility is measured by the number of tasks that the population completes during run-time. Finally, it is shown that agents are able to finish more tasks by dynamically changing their willingness to cooperate.

  • 12.
    Frasheri, Mirgita
    et al.
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Esterle, Lukas
    Aarhus Universitet, Aarhus, Denmark.
    Papadopoulos, Alessandro
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Modeling the willingness to interact in cooperative multi-robot systems2020In: ICAART 2020 - Proceedings of the 12th International Conference on Agents and Artificial Intelligence, SciTePress , 2020, Vol. 1, p. 62-72Conference paper (Refereed)
    Abstract [en]

    When multiple robots are required to collaborate in order to accomplish a specific task, they need to be coordinated in order to operate efficiently. To allow for scalability and robustness, we propose a novel distributed approach performed by autonomous robots based on their willingness to interact with each other. This willingness, based on their individual state, is used to inform a decision process of whether or not to interact with other robots within the environment. We study this new mechanism to form coalitions in the on-line multiobject κ-coverage problem, and compare it with six other methods from the literature. We investigate the trade-off between the number of robots available and the number of potential targets in the environment. We show that the proposed method is able to provide comparable performance to the best method in the case of static targets, and to achieve a higher level of coverage with respect to the other methods in the case of mobile targets.

  • 13.
    Frasheri, Mirgita
    et al.
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Miloradović, Branko
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Curuklu, Baran
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Ekström, Mikael
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Papadopoulos, Alessandro
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    GLocal: A Hybrid Approach to the Multi-Agent Mission Re-Planning Problem2020Report (Other academic)
    Abstract [en]

    Multi-robot systems can be prone to failures during plan execution, depending on the harshness of the environment they are deployed in. As a consequence, initially devised plans may no longer be feasible, and a re-planning process needs to take place to re-allocate any pending tasks. Two main approaches emerge as possible solutions, a global re-planning technique using a centralized planner that will redo the task allocation with the updated world state information, or a decentralized approach that will focus on the local plan reparation, i.e., the re-allocation of those tasks initially assigned to the failed robots.The former approach produces an overall better solution, while the latter is less computationally expensive.The goal of this paper is to exploit the benefits of both approaches, while minimizing their drawbacks. To this end, we propose a hybrid approach {that combines a centralized planner with decentralized multi-agent planning}. In case of an agent failure, the local plan reparation algorithm tries to repair the plan through agent negotiation. If it fails to re-allocate all of the pending tasks, the global re-planning algorithm is invoked, which re-allocates all unfinished tasks from all agents.The hybrid approach was compared to planner approach, and it was shown that it improves on the makespan of a mission in presence of different numbers of failures,as a consequence of the local plan reparation algorithm.

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  • 14.
    Frasheri, Mirgita
    et al.
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Trinh, LanAnh
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Curuklu, Baran
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Ekström, Mikael
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Failure Analysis for Adaptive Autonomous Agents using Petri Nets2017In: Proceedings of the 2017 Federated Conference on Computer Science and Information Systems, FedCSIS 2017, 2017, p. 293-297Conference paper (Refereed)
    Abstract [en]

    Adaptive autonomous (AA) agents are able to make their own decisions on when and with whom to share their autonomy based on their states. Whereas dependability gives evidence on whether a system, (e.g. an agent team), and its provided services are to be trusted. In this paper, an initial analysis on AA agents with respect to dependability is conducted. Firstly, AA is modeled through a pairwise relationship called willingness of agents to interact, i.e. to ask for and give assistance. Secondly, dependability is evaluated by considering solely the reliability attribute, which presents the continuity of correct services. The failure analysis is realized by modeling the agents through Petri Nets. Simulation results indicate that agents drop slightly more tasks when they are more willing to interact than otherwise, especially when the fail-rate of individual agents increases. Conclusively, the willingness should be tweaked such that there is compromise between performance and helpfulness.

  • 15.
    Miloradović, Branko
    et al.
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Frasheri, Mirgita
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Curuklu, Baran
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Ekström, Mikael
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Papadopoulos, Alessandro
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    TAMER: Task Allocation in Multi-robot Systems Through an Entity-Relationship Model2019In: PRIMA 2019: Principles and Practice of Multi-Agent Systems, 2019, p. 478-486Conference paper (Refereed)
    Abstract [en]

    Multi-robot task allocation (MRTA) problems have been studied extensively in the past decades. As a result, several classifications have been proposed in the literature targeting different aspects of MRTA, with often a few commonalities between them. The goal of this paper is twofold. First, a comprehensive overview of early work on existing MRTA taxonomies is provided, focusing on their differences and similarities. Second, the MRTA problem is modelled using an Entity-Relationship (ER) conceptual formalism to provide a structured representation of the most relevant aspects, including the ones proposed within previous taxonomies. Such representation has the advantage of (i) representing MRTA problems in a systematic way, (ii) providing a formalism that can be easily transformed into a software infrastructure, and (iii) setting the baseline for the definition of knowledge bases, that can be used for automated reasoning in MRTA problems.

  • 16.
    Rodríguez-Molina, J.
    et al.
    Centro de Investigación en Tecnologías Software y Sistemas Multimedia Para la Sostenibilidad—CITSEM, Madrid, Spain.
    Bilbao, S.
    TECNALIA, Derio, Bizkaia, Spain.
    Martínez, B.
    TECNALIA, Derio, Bizkaia, Spain.
    Frasheri, Mirgita
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Curuklu, Baran
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    An optimized, data distribution service-based solution for reliable data exchange among autonomous underwater vehicles2017In: Sensors, ISSN 1424-8220, E-ISSN 1424-8220, Vol. 17, no 8, article id 1802Article in journal (Refereed)
    Abstract [en]

    Major challenges are presented when managing a large number of heterogeneous vehicles that have to communicate underwater in order to complete a global mission in a cooperative manner. In this kind of application domain, sending data through the environment presents issues that surpass the ones found in other overwater, distributed, cyber-physical systems (i.e., low bandwidth, unreliable transport medium, data representation and hardware high heterogeneity). This manuscript presents a Publish/Subscribe-based semantic middleware solution for unreliable scenarios and vehicle interoperability across cooperative and heterogeneous autonomous vehicles. The middleware relies on different iterations of the Data Distribution Service (DDS) software standard and their combined work between autonomous maritime vehicles and a control entity. It also uses several components with different functionalities deemed as mandatory for a semantic middleware architecture oriented to maritime operations (device and service registration, context awareness, access to the application layer) where other technologies are also interweaved with middleware (wireless communications, acoustic networks). Implementation details and test results, both in a laboratory and a deployment scenario, have been provided as a way to assess the quality of the system and its satisfactory performance. 

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