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Interleaving Guidance in Evolutionary Multi-Objective Optimization

Journal of Computer Science & Technology 2008年第1期23卷 44-63页

作者： Lam Thu Bui Kalyanmoy Deb hussein a.abbass Daryl Essam The Artificial Life and Adaptive Robotics Laboratory School of ITEEADFAUniversity of New South Wales Canberra Mechanical Engineering Department Indian Institute of TechnologyKanpur

In this paper, we propose a framework that uses localization for multi-objective optimization to simultaneously guide an evolutionary algorithm in both the decision and objective spaces. The localization is built using a limited number of adaptive spheres （local models） in the decision space. These spheres axe usually guided, using some direction information, in the decision space towards the areas with non-dominated solutions. We use a second mechanism to adjust the spheres to specialize on different parts of the Paxeto front by using a guided dominance technique in the objective space. Through this interleaved guidance in both spaces, the spheres will be guided towards different parts of the Paxeto front while also exploring the decision space efficiently. The experimental results showed good performance for the local models using this dual guidance, in comparison with their original version.

关键词： evolutionary multi-objective optimization guided dominance local models

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Embedding-based Detection and Extraction of Research Topics from Academic Documents Using Deep Clustering

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Journal of Data and Information Science 2021年第3期6卷 99-122页

作者： Sahand Vahidnia Alireza Abbasi hussein a.abbass University of New South Wales Canberra2612ACTAustralia

Purpose:Detection of research fields or topics and understanding the dynamics help the scientific community in their decisions regarding the establishment of scientific *** also helps in having a better collaboration with governments and *** study aims to investigate the development of research fields over time,translating it into a topic detection ***/methodology/approach:To achieve the objectives,we propose a modified deep clustering method to detect research trends from the abstracts and titles of academic *** embedding approaches are utilized to transform documents into vector-based *** proposed method is evaluated by comparing it with a combination of different embedding and clustering approaches and the classical topic modeling algorithms(***)against a benchmark dataset.A case study is also conducted exploring the evolution of Artificial Intelligence(AI)detecting the research topics or sub-fields in related AI ***:Evaluating the performance of the proposed method using clustering performance indicators reflects that our proposed method outperforms similar approaches against the benchmark *** the proposed method,we also show how the topics have evolved in the period of the recent 30 years,taking advantage of a keyword extraction method for cluster tagging and labeling,demonstrating the context of the *** limitations:We noticed that it is not possible to generalize one solution for all downstream ***,it is required to fine-tune or optimize the solutions for each task and even *** addition,interpretation of cluster labels can be subjective and vary based on the readers’*** is also very difficult to evaluate the labeling techniques,rendering the explanation of the clusters further *** implications:As demonstrated in the case study,we show that in a real-world example,how the proposed method would enable the researchers and

关键词： Dynamics of science Science mapping Document clustering Artificial intelligence Deep learning

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Human-Swarm-Teaming Transparency and Trust Architecture

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IEEE/CAA Journal of Automatica Sinica 2021年第7期8卷 1281-1295页

作者： Adam J.Hepworth Daniel P.Baxter Aya hussein Kate J.Yaxley Essam Debie hussein a.abbass the School of Engineering and Information Technology University of New South WalesCanberraNew South Wales 2612Australia

Transparency is a widely used but poorly defined term within the explainable artificial intelligence *** is due,in part,to the lack of an agreed definition and the overlap between the connected—sometimes used synonymously—concepts of interpretability and *** assert that transparency is the overarching concept,with the tenets of interpretability,explainability,and predictability *** draw on a portfolio of definitions for each of these distinct concepts to propose a human-swarm-teaming transparency and trust architecture(HST3-Architecture).The architecture reinforces transparency as a key contributor towards situation awareness,and consequently as an enabler for effective trustworthy human-swarm teaming(HST).

关键词： Artificial intelligence explainability human-swarm teaming(HST) interpretability predictability swarm shepherding transparency.

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