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  • 1.
    Anjomshoae, Sule
    et al.
    Umeå University, Faculty of Science and Technology, Department of Computing Science. Umeå University.
    Främling, Kary
    Umeå University, Faculty of Science and Technology, Department of Computing Science. Umeå University.
    Najjar, Amro
    Umeå University, Faculty of Science and Technology, Department of Computing Science. Umeå University.
    Explanations of black-box model predictions by contextual importance and utility2019In: Explainable, transparent autonomous agents and multi-agent systems: first international workshop, EXTRAAMAS 2019, Montreal, QC, Canada, May 13–14, 2019, revised selected papers / [ed] Davide Calvaresi, Amro Najjar, Michael Schumacher, Kary Främling, Springer, 2019, p. 95-109Chapter in book (Refereed)
    Abstract [en]

    The significant advances in autonomous systems together with an immensely wider application domain have increased the need for trustable intelligent systems. Explainable artificial intelligence is gaining considerable attention among researchers and developers to address this requirement. Although there is an increasing number of works on interpretable and trans- parent machine learning algorithms, they are mostly intended for the technical users. Explanations for the end-user have been neglected in many usable and practical applications. In this work, we present the Contextual Importance (CI) and Contextual Utility (CU) concepts to extract explanations that are easily understandable by experts as well as novice users. This method explains the prediction results without transforming the model into an interpretable one. We present an example of providing explanations for linear and non-linear models to demonstrate the generalizability of the method. CI and CU are numerical values that can be represented to the user in visuals and natural language form to justify actions and explain reasoning for individual instances, situations, and contexts. We show the utility of explanations in car selection example and Iris flower classification by presenting complete (i.e. the causes of an individual prediction) and contrastive explanation (i.e. contrasting instance against the instance of interest). The experimental results show the feasibility and validity of the provided explanation methods.

  • 2.
    Anjomshoae, Sule
    et al.
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Najjar, Amro
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Calvaresi, Davide
    Främling, Kary
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Explainable Agents and Robots: Results from a Systematic Literature Review2019In: Proceedings of the 18th International Conference on Autonomous Agents and MultiAgent Systems / [ed] N. Agmon, M. E. Taylor, E. Elkind, M. Veloso, International Foundation for Autonomous Agents and MultiAgent Systems , 2019, p. 1078-1088Conference paper (Refereed)
    Abstract [en]

    Humans are increasingly relying on complex systems that heavily adopts Artificial Intelligence (AI) techniques. Such systems are employed in a growing number of domains, and making them explainable is an impelling priority. Recently, the domain of eXplainable Artificial Intelligence (XAI) emerged with the aims of fostering transparency and trustworthiness. Several reviews have been conducted. Nevertheless, most of them deal with data-driven XAI to overcome the opaqueness of black-box algorithms. Contributions addressing goal-driven XAI (e.g., explainable agency for robots and agents) are still missing. This paper aims at filling this gap, proposing a Systematic Literature Review. The main findings are (i) a considerable portion of the papers propose conceptual studies, or lack evaluations or tackle relatively simple scenarios; (ii) almost all of the studied papers deal with robots/agents explaining their behaviors to the human users, and very few works addressed inter-robot (inter-agent) explainability. Finally, (iii) while providing explanations to non-expert users has been outlined as a necessity, only a few works addressed the issues of personalization and context-awareness

  • 3.
    Kampik, Timotheus
    et al.
    Umeå University, Faculty of Science and Technology, Department of Computing Science. Signavio GmbH, Berlin, Germany.
    Najjar, Amro
    Umeå University, Faculty of Science and Technology, Department of Computing Science. AI-Robolab/ICR, Computer Science and Communications, University of Luxembourg, Esch-sur-Alzette, Luxembourg.
    Integrating Multi-agent Simulations into Enterprise Application Landscapes2019In: Highlights of Practical Applications of Survivable Agents and Multi-Agent Systems: The PAAMS Collection. PAAMS 2019 / [ed] De La Prieta F. et al., 2019, p. 100-111Conference paper (Refereed)
    Abstract [en]

    To cope with increasingly complex business, political, and economic environments, agent-based simulations (ABS) have been proposed for modeling complex systems such as human societies, transport systems, and markets. ABS enable experts to assess the influence of exogenous parameters (e.g., climate changes or stock market prices), as well as the impact of policies and their long-term consequences. Despite some successes, the use of ABS is hindered by a set of interrelated factors. First, ABS are mainly created and used by researchers and experts in academia and specialized consulting firms. Second, the results of ABS are typically not automatically integrated into the corresponding business process. Instead, the integration is undertaken by human users who are responsible for adjusting the implemented policy to take into account the results of the ABS. These limitations are exacerbated when the results of the ABS affect multi-party agreements (e.g., contracts) since this requires all involved actors to agree on the validity of the simulation, on how and when to take its results into account, and on how to split the losses/gains caused by these changes. To address these challenges, this paper explores the integration of ABS into enterprise application landscapes. In particular, we present an architecture that integrates ABS into cross-organizational enterprise resource planning (ERP) processes. As part of this, we propose a multi-agent systems simulator for the Hyperledger blockchain and describe an example supply chain management scenario type to illustrate the approach.

  • 4.
    Kampik, Timotheus
    et al.
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Najjar, Amro
    Umeå University, Faculty of Science and Technology, Department of Computing Science. AI-Robolab/ICR Computer Science and Communications University of Luxembourg Esch-sur-Alzette, Luxembourg.
    Technology-facilitated Societal Consensus2019In: UMAP'19 Adjunct: Adjunct Publication of the 27th Conference on User Modeling, Adaptation and Personalization, Larnaca, Cyprus: ACM Digital Library, 2019, p. 3-7Conference paper (Refereed)
    Abstract [en]

    The spread of radical opinions, facilitated by homophilic Internet communities (echo chambers), has become a threat to the stability of societies around the globe. The concept of choice architecture-the design of choice information for consumers with the goal of facilitating societally beneficial decisions-provides a promising (although not uncontroversial) general concept to address this problem. The choice architecture approach is reflected in recent proposals advocating for recommender systems that consider the societal impact of their recommendations and not only strive to optimize revenue streams. However, the precise nature of the goal state such systems should work towards remains an open question. In this paper, we suggest that this goal state can be defined by considering target opinion spread in a society on different topics of interest as a multivariate normal distribution; i.e., while there is a diversity of opinions, most people have similar opinions on most topics. We explain why this approach is promising, and list a set of cross-disciplinary research challenges that need to be solved to advance the idea.

  • 5. Mualla, Yazan
    et al.
    Najjar, Amro
    Umeå University.
    Boissier, Olivier
    EMSE Saint-Etienne.
    Galland, Stéphane
    UTBM.
    Tchappi Haman, Igor
    University of Ngaoundere.
    Vanet, Robin
    IMT Saint-Etienne.
    A Cyber-Physical System for Semi-autonomous Oil & Gas Drilling Operations2019In: 2019 Third IEEE International Conference on Robotic Computing (IRC), IEEE Computer Society, 2019, p. 514-519Conference paper (Refereed)
    Abstract [en]

    In Oil&Gas drilling operations and after reaching deep drilled depths, high temperature increases significantly enough to damage the down-hole drilling tools, and the existing mitigation process is insufficient. In this paper, we propose a Cyber-Physical System (CPS) where agents are used to represent the collaborating entities in Oil\&Gas fields both up-hole and down-hole. With the proposed CPS, down-hole tools respond to high temperature autonomously with a decentralized collective voting based on the tools' internal decision model while waiting for the cooling performed up-hole by the field engineer. This decision model, driven by the tools' specifications, aims to withstand high temperature. The proposed CPS is implemented using a multiagent simulation environment, and the results show that it mitigates high temperature properly with both the voting and the cooling mechanisms.

  • 6. Mualla, Yazan
    et al.
    Najjar, Amro
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Galland, Stephane
    Nicolle, Christophe
    Tchappi, Igor Haman
    Yasar, Ansar-Ul-Haque
    Främling, Kary
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Between the Megalopolis and the Deep Blue Sky: Challenges of Transport with UAVs in Future Smart Cities2019In: AAMAS '19: PROCEEDINGS OF THE 18TH INTERNATIONAL CONFERENCE ON AUTONOMOUS AGENTS AND MULTIAGENT SYSTEMS, ASSOC COMPUTING MACHINERY , 2019, p. 1649-1653Conference paper (Refereed)
    Abstract [en]

    With the rapid increase of the world's urban population, the infrastructure of the constantly expanding metropolitan areas is undergoing an immense pressure. To meet the growing demands of sustainable urban environments and improve the quality of life for citizens, municipalities will increasingly rely on novel transport solutions. In particular, Unmanned Aerial Vehicles (UAVs) are expected to have a crucial role in the future smart cities thanks to their interesting features such as autonomy, flexibility, mobility, adaptive altitude, and small dimensions. However, densely populated megalopolises of the future are administrated by several municipals, governmental and civil society actors, where vivid economic activities involving a multitude of individual stakeholders take place. In such megalopolises, the use of agents for UAVs is gaining more interest especially in complex application scenarios where coordination and cooperation are necessary. This paper sketches a visionary view of the UAVs' role in the transport domain of future smart cities. Additionally, four challenging research directions are highlighted including problems related to autonomy, explainability, security and validation & verification of the UAVs behavior.

  • 7. Mualla, Yazan
    et al.
    Najjar, Amro
    Vanet, Robin
    Boissier, Olivier
    Galland, Stéphane
    Towards a Real-time Mitigation of High Temperature while Drilling using a Multi-agent System2018In: 1st International Workshop on Real-Time Compliant Multi-Agent Systems, RTcMAS 2018, 2018, p. 77-92Conference paper (Refereed)
  • 8. Mualla, Yazan
    et al.
    Vanet, Robin
    Najjar, Amro
    Boissier, Olivier
    Galland, Stéphane
    AgentOil: a multiagent-based simulation of the drilling process in oilfields2018In: International Conference on Practical Applications of Agents and Multi-Agent Systems, 2018, p. 339-343Conference paper (Refereed)
  • 9. Najjar, Amro
    et al.
    Mualla, Yazan
    Picard, Gauthier
    Singh, Kamal Deep
    Négociation multi-agent "un-à-plusieurs" et mécanismes de coordination pour la gestion de la satisfaction des utilisateurs d’un service2018In: 26èmes Journées Francophones sur les Systèmes Multi-Agents (JFSMA 2018), 2018Conference paper (Refereed)
  • 10. Najjar, Amro
    et al.
    Mualla, Yazan
    Singh, Kamal
    Picard, Gauthier
    One-to-Many Multi-agent Negotiation and Coordination Mechanisms to Manage User Satisfaction2018In: the 11th International Workshop on Agent-based Complex Automated Negotiations (ACAN2018), 2018Conference paper (Refereed)
  • 11.
    Najjar, Amro
    et al.
    Laboratoire Hubert Curien UMR CNRS 5516, Institut Henri Fayol, Mines Saint-Etienne, Saint-Etienne, France.
    Picard, Gauthier
    Boissier, Olivier
    Négociation multi-agents résistante aux pics de charge pour améliorer l’acceptabilité des services d’un fournisseur SaaS ouvert2018In: Revue d'intelligence artificielle: Revue des Sciences et Technologies de l'Information, ISSN 0992-499X, E-ISSN 1958-5748, Vol. 32, no 5-6, p. 603-625Article in journal (Refereed)
    Abstract [en]

    Service acceptability rate and user satisfaction are becoming key factors to avoid client churn and secure the success of any Software as a Service (SaaS) provider. Nevertheless, the provider must also accommodate fluctuating workloads and minimize the cost it pays to rent resources from the cloud. To address these contradicting concerns, most of existing works carry out resource management unilaterally by the provider. Consequently, end-user preferences and her subjective acceptability of the service are mostly ignored. In order to assess user satisfaction and service acceptability recent studies in the domain of Quality of Experience (QoE) recommend providers to use quantiles and percentile to gauge user service acceptability precisely. In this article we propose an elastic, load-spike proof, and adaptive one-to-many negotiation mechanism to improve the service acceptability of an open SaaS provider. Based on quantile estimation of service acceptability rate and a learned model of the user negotiation strategy, this mechanism adjusts the provider negotiation process in order to guarantee the desired service acceptability rate while meeting the budget limits of the provider and accommodating workload fluctuations. The proposed mechanism is implemented and its results are examined and analyzed.

1 - 11 of 11
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  • en-US
  • fi-FI
  • nn-NO
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