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1. A Primitive-Based Approach to Good Seamanship Path Planning for Autonomous Surface Vessels

   Stankiewicz, Paul; Kobilarov, Marin (June 2021, IEEE International Conference on Robotics and Automation)

   null (Ed.)

   This paper offers a multi-layer planning approachfor autonomous surface vessels (ASVs) that must adhere togood seamanship practices and the International Regulationsfor Prevention of Collisions at Sea (COLREGS) [1]. The ap-proach combines novel situational awareness logic with motionprimitive-based planners in a receding horizon framework.Further, ship domain and ship arena concepts are used todevelop risk metrics that capture COLREGS compliance andthe notion of good seamanship. By relying on metrics-drivenmotion planning as opposed to rule-based conditions, theproposed framework scales naturally to non-trivial single-vessel and multi-vessel situations. The planner is evaluatedusing adaptive, simulation-based testing to statistically comparethe performance to other standard methods. Finally, proof-of-concept field experiments are presented on a subscale platform 

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2. Decision Support Frameworks in Solid Waste Management: A Systematic Review of Multi-Criteria Decision-Making with Sustainability and Social Indicators

   https://doi.org/10.3390/su151813316  

   Gutierrez-Lopez, Jenny; McGarvey, Ronald G; Costello, Christine; Hall, Damon M (September 2023, Sustainability)

   Waste management is a critical sector that needs to co-ordinate its activities with outcomes that impact society. Multi-criteria decision-making methods for waste management have been widely considered using environmental and economic criteria. With the development of new social regulations and concerns, sustainable waste management needs to additionally target socially acceptable practices. Despite the need to aid solid waste management decision-makers in contemplating the three pillars of sustainability, a limited inclusion of social impact has been found in the multi-objective decision-making literature. This study presents a systematic literature review of multi-criteria decision-making methods in solid waste management. The purpose of this study is threefold. (1) Emphasize the application of multi-objective decision-making methods, summarizing the models that have been used and their applications; (2) provide insights into the quantification of social aspects and their inclusion in decision-making methods, providing a list of social indicators collected from the reviewed studies; (3) offer an analysis of stakeholders’ involvement in waste management. From the articles investigated, one can observe the importance of understanding the local context in which the waste management system is located and the necessity of community consultation to recognize the potential challenges and improvements to solid waste management systems. Consequently, the involvement of stakeholders is crucial during the quantification process of social indicators. In alignment with the findings and needs raised by this review, a methodological approach is suggested for integrating optimization, social aspects, and stakeholders under a waste management context. 

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3. Motion Attribute-based Clustering and Collision Avoidance of Multiple In-water Obstacles by Autonomous Surface Vehicle

   https://doi.org/10.1109/IROS47612.2022.9981226  

   Jeong, Mingi; Quattrini Li, Alberto (October 2022, 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS))

   Navigation and obstacle avoidance in aquatic en-vironments for autonomous surface vehicles (ASVs) in high-traffic maritime scenarios is still an open challenge, as the Convention on the International Regulations for Preventing Collisions at Sea (COLREGs) is not defined for multi-encounter situations. Current state-of-the-art methods resolve single-to-single encounters with sequential actions and assume that other obstacles follow COLREGs. Our work proposes a novel real-time non-myopic obstacle avoidance method, allowing an ASV that has only partial knowledge of the surroundings within the sensor radius to navigate in high-traffic maritime scenarios. Specifically, we achieve a holistic view of the feasible ASV action space able to avoid deadlock scenarios, by proposing (1) a clustering method based on motion attributes of other obstacles, (2) a geometric framework for identifying the feasible action space, and (3) a multi-objective optimization to determine the best action. Theoretical analysis and extensive realistic experiments in simulation considering real-world traffic scenarios demonstrate that our proposed real-time obstacle avoidance method is able to achieve safer trajectories than other state-of-the-art methods and that is robust to uncertainty present in the current information available to the ASV. 

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4. An Explainable Artificial Intelligence Approach for Multi-Criteria ABC Item Classification

   https://doi.org/10.3390/jtaer18020044  

   Qaffas, Alaa Asim; Ben HajKacem, Mohamed-Aymen; Ben Ncir, Chiheb-Eddine; Nasraoui, Olfa (June 2023, Journal of Theoretical and Applied Electronic Commerce Research)

   Multi-criteria ABC classification is a useful model for automatic inventory management and optimization. This model enables a rapid classification of inventory items into three groups, having varying managerial levels. Several methods, based on different criteria and principles, were proposed to build the ABC classes. However, existing ABC classification methods operate as black-box AI processes that only provide assignments of the items to the different ABC classes without providing further managerial explanations. The multi-criteria nature of the inventory classification problem makes the utilization and the interpretation of item classes difficult, without further information. Decision makers usually need additional information regarding important characteristics that were crucial in determining the managerial classes of the items because such information can help managers better understand the inventory groups and make inventory management decisions more transparent. To address this issue, we propose a two-phased explainable approach based on eXplainable Artificial Intelligence (XAI) capabilities. The proposed approach provides both local and global explanations of the built ABC classes at the item and class levels, respectively. Application of the proposed approach in inventory classification of a firm, specialized in retail sales, demonstrated its effectiveness in generating accurate and interpretable ABC classes. Assignments of the items to the different ABC classes were well-explained based on the item’s criteria. The results in this particular application have shown a significant impact of the sales, profit, and customer priority as criteria that had an impact on determining the item classes. 

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5. Multi-Armed Bandits for Human-Machine Decision Making

   https://doi.org/10.1109/ICASSP.2018.8461843  

   Reverdy, Paul; Srivastava, Vaibhav (April 2018, 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP))

   Building an integrated human-machine decision-making system requires developing effective interfaces between the human and the machine. We develop such an interface by studying the multi-armed bandit problem, a simple sequential decision-making paradigm that can model a variety of tasks. We construct Bayesian algorithms for the multi-armed ban- dit problem, prove conditions under which these algorithms achieve good performance, and empirically show that, with appropriate priors, these algorithms effectively model human choice behavior; the priors then form a principled interface from human to machine. We take a signal processing perspective on the prior estimation problem and develop methods to estimate the priors given human choice data. 

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