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        <title>Frontiers in Artificial Intelligence | Logic and Reasoning in AI section | New and Recent Articles</title>
        <link>https://www.frontiersin.org/journals/artificial-intelligence/sections/logic-and-reasoning-in-ai</link>
        <description>RSS Feed for Logic and Reasoning in AI section in the Frontiers in Artificial Intelligence journal | New and Recent Articles</description>
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        <pubDate>2026-05-01T13:37:40.458+00:00</pubDate>
        <ttl>60</ttl>
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        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frai.2026.1768696</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frai.2026.1768696</link>
        <title><![CDATA[Ethics and bias in emotional AI]]></title>
        <pubdate>2026-03-05T00:00:00Z</pubdate>
        <category>Perspective</category>
        <author>Smrithy G. S</author><author>Balaji Chandrasekaran</author><author>Omana J</author>
        <description><![CDATA[Emotional Artificial Intelligence (Emotional AI) is a branch of artificial intelligence that combines machine learning, natural language processing, and computer vision to perceive and react to human feelings. Emotional AI will enhance more intuitive and personal human-machine interactions by analyzing facial expressions, speech patterns, physiological factors, and behavioural expressions, and find applications in healthcare, education, customer service, and other fields. Although this field is promising, it comes with serious ethical issues especially on privacy, transparency, accountability and fairness. The nature of human emotions is intricate, context-specific and culturally biassed, thus the perceptions of emotions are challenging and subject to biasness in the perception. Besides, emotional data is sensitive, thus, causing concerns over its abuse, surveillance, and infringement of individual rights. The problems of algorithms bias, the representativeness of data, and even fairness also make the implementation of the Emotional AI more problematic since biassed systems can serve to strengthen stereotypes and inequalities in society. This perspective explores the ethical issues of Emotional AI, which brings out the need to develop ethically, establish good governance, and work together internationally to ensure that Emotional AI is used in a way that benefits humanity without denting human dignity, security, or social justice.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frai.2026.1808388</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frai.2026.1808388</link>
        <title><![CDATA[Correction: Fault tree analysis-adapted knowledge structuring: a case study of sustainable international security cooperation]]></title>
        <pubdate>2026-02-23T00:00:00Z</pubdate>
        <category>Correction</category>
        <author>Frontiers Production Office </author>
        <description></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frai.2026.1723198</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frai.2026.1723198</link>
        <title><![CDATA[Fault tree analysis-adapted knowledge structuring: a case study of sustainable international security cooperation]]></title>
        <pubdate>2026-02-06T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Yudai Wada</author><author>Koki Ijuin</author><author>Chiaki Oshiyama</author><author>Takuichi Nishimura</author>
        <description><![CDATA[Sustainable knowledge transfer in Japan’s international security cooperation for research and development (R&D) and procurement is challenging due to institutional and security constraints. Critical know-how is often tacit and dispersed among experts; continuity is undermined by frequent personnel rotations, temporal–spatial gaps between projects, and institutional and cultural differences across international partners, leading to knowledge loss. In this context, traditional on-the-job training is difficult to sustain, making durable knowledge transfer difficult to achieve. To overcome this problem, this study proposes a knowledge engineering method that externalizes practitioner expertise. Procedure-based knowledge (what/how) and purpose-based knowledge (why: purposes and decision rules) are structured as two auditable linked, machine-interpretable graphs—the procedure- and purpose-based knowledge graphs. This method uses the Convincing Human Action Rationalized Model (CHARM) as a notation for knowledge structuring. To articulate implicit causal reasoning, we integrate Fault Tree Analysis (FTA) as a qualitative, deductive elicitation and articulation notation. Starting from observed outcomes (“top events”), FTA deductively elicits avoidance purposes and candidate actions. These purpose-action pairs are then recorded under Reference Cases (RC) and embedded as RC-tagged links in the two graphs with FTA-derived annotations, thereby refining causal logic and facilitating knowledge externalization. We empirically assess the method’s effectiveness through qualitative and quantitative analyses of data from semi-structured interviews, a facilitated workshop, and FTA-guided follow-up interviews. These activities increased both the volume and granularity of externalized knowledge, yielding 42 RCs and 133 case-attached actions as provenance-bearing purpose-action units. Our approach yields reusable, machine-interpretable assets for human-AI collaboration and may support continuity during constrained handovers, which may help mitigate repeated errors and improve negotiation preparedness. These findings suggest that the FTA-adapted CHARM approach can foster more sustainable knowledge transfer for Japan’s international security cooperation.]]></description>
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        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frai.2025.1710410</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frai.2025.1710410</link>
        <title><![CDATA[Comparing AI and human moral reasoning: context-sensitive patterns beyond utilitarian bias]]></title>
        <pubdate>2026-01-12T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Elyas Barabadi</author><author>Zahra Fotuhabadi</author><author>Amanollah Arghavan</author><author>James R Booth</author>
        <description><![CDATA[IntroductionDecision-making supported by intelligent systems is being increasingly deployed in ethically sensitive domains. As a result, it is of considerable importance to understand the patterns of moral judgments generated by large language models (LLMs).MethodsTo this end, the current research systematically investigates how two prominent LLMs (i.e., ChatGPT and Claude Sonnet) respond to 12 moral scenarios previously administered to human participants (first language and second language users). The primary purpose was to examine whether the responses generated by LLMs align with either deontological or utilitarian orientations. Our secondary aim was to compare response patterns of these two models to those of human respondents in previous studies.ResultsContrary to prevailing assumptions regarding the utilitarian tendency of LLMs, the findings revealed subtle response distributions of moral choice that are context-sensitive. Specifically, both models alternated between deontological and utilitarian judgments, depending on the scenario-specific features.DiscussionThese output patterns reflect complex moral trade-offs and may play a significant role in shaping societal trust and acceptance of AI systems in morally sensitive domains.]]></description>
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        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frai.2025.1728738</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frai.2025.1728738</link>
        <title><![CDATA[From the logic of coordination to goal-directed reasoning: the agentic turn in artificial intelligence]]></title>
        <pubdate>2026-01-12T00:00:00Z</pubdate>
        <category>Hypothesis and Theory</category>
        <author>Tsehaye Haidemariam</author>
        <description><![CDATA[The rise of agentic artificial intelligence (Agentic AI) marks a transition from systems that optimize externally specified objectives to systems capable of representing, evaluating, and revising their own goals. Whereas earlier AI architectures executed fixed task specifications, agentic systems maintain recursive loops of perception, evaluation, goal-updating, and action, allowing them to sustain and adapt purposive activity across temporal and organizational scales. This paper argues that Agentic AI is not an incremental extension of large language models (LLMs) or autonomous agents in the sense we know it from classical AI and multi-agent systems, but a reconstitution of agency itself within computational substrates. Building on the logic of coordination, delegation, and self-regulation developed in early agent-based process management systems, we propose a general theory of synthetic purposiveness, where agency emerges as a distributed and self-maintaining property of artificial systems operating in open-ended environments. We develop the concept of synthetic teleology—the engineered capacity of artificial systems to generate and regulate goals through ongoing self-evaluation—and we formalize its dynamics through a recursive goal-maintenance equation. We further outline design patterns, computational semantics, and measurable indicators of purposiveness (e.g., teleological coherence, adaptive recovery, and reflective efficiency), providing a foundation for the systematic design and empirical investigation of agentic behaviour. By reclaiming agency as a first-class construct in artificial intelligence, we argue for a paradigm shift from algorithmic optimization toward goal-directed reasoning and purposive orchestration—one with far-reaching epistemic, societal, and institutional consequences.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frai.2025.1724493</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frai.2025.1724493</link>
        <title><![CDATA[Tracing strategic divergence: archetypal and counterfactual analysis of StarCraft II gameplay trajectories]]></title>
        <pubdate>2026-01-06T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Jie Zhang</author><author>Weilong Yang</author>
        <description><![CDATA[IntroductionTo address the challenges of data heterogeneity, strategic diversity, and process opacity in interpreting multi-agent decision-making within complex competitive environments, we have developed TRACE, an end-to-end analytical framework for StarCraft II gameplay.MethodsThis framework standardizes raw replay data into aligned state trajectories, extracts “typical strategic progressions” using a Conditional Recurrent Variational Autoencoder (C-RVAE), and quantifies the deviation of individual games from these archetypes via counterfactual alignment. Its core innovation is the introduction of a dimensionless deviation metric, |Δ|, which achieves process-level interpretability. This metric reveals “which elements are important” by ranking time-averaged feature contributions across aggregated categories (Economy, Military, Technology) and shows “when deviations occur” through temporal heatmaps, forging a verifiable evidence chain..ResultsQuantitative evaluation on professional tournament datasets demonstrates the framework’s robustness, revealing that strategic deviations often crystallize in the early game (averaging 8.4% of match duration) and are frequently driven by critical technology timing gaps. The counterfactual generation module effectively restores strategic alignment, achieving an average similarity improvement of over 90% by correcting identified divergences. Furthermore, expert human evaluation confirms the practical utility of the system, awarding high scores for Factual Fidelity (4.6/5.0) and Causal Coherence (4.3/5.0) to the automatically generated narratives.DiscussionBy providing openaccess code and reproducible datasets, TRACE lowers the barrier to large-scale replay analysis, offering an operational quantitative basis for macro-strategy understanding, coaching reviews, and AI model evaluation.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frai.2025.1614894</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frai.2025.1614894</link>
        <title><![CDATA[Minimal reduct for propositional circumscription]]></title>
        <pubdate>2025-12-10T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Zhongtao Xie</author><author>Yisong Wang</author><author>Lei Yang</author><author>Renyan Feng</author>
        <description><![CDATA[Circumscription is an important logic framework for representing and reasoning common-sense knowledge. With efficient implementations for circumscription, including circ2dlp and aspino, it has been widely used in model-based diagnosis and other domains. We propose a notion of minimal reduct for propositional circumscription and prove a characterization theorem, i.e., that the models of a circumscription can be obtained from the minimal reduct of the circumscription. With the help of the minimal reduct, a new method circ-reduct for computing models of circumscription is presented. It iteratively computes smaller models under set inclusion (if possible), and the minimal reduct is used to simplify the circumscription in each iteration. The algorithm is proved to be correct. Extensive experiments are conducted on circuit diagnosis ISCAS85, random CNF instances, and some industrial SAT instances for the international SAT competition. These results demonstrate that the minimal reduct is effective in computing circumscription models. Compared to the widely used circumscription solver circ2dlp using the state-of-the-art answer set programming solver clingo, our algorithm circ-reduct achieves significantly shorter CPU time. Compared with aspino using glucose as the internal SAT solver and unsatisfiable core analysis technique, our algorithm achieves better CPU time for random and industrial CNF benchmarks, while it is comparable for circuit diagnosis benchmarks.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frai.2025.1677528</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frai.2025.1677528</link>
        <title><![CDATA[Epistemic limits of local interpretability in self-modulating cognitive architectures]]></title>
        <pubdate>2025-12-01T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Abdelaali Mahrouk</author>
        <description><![CDATA[IntroductionLocal interpretability methods such as LIME and SHAP are widely used to explain model decisions. However, they rely on assumptions of local continuity that often fail in recursive, self-modulating cognitive architectures.MethodsWe analyze the limitations of local proxy models through formal reasoning, simulation experiments, and epistemological framing. We introduce constructs such as Modular Cognitive Attention (MCA), the Cognitive Leap Operator (Ψ), and the Internal Narrative Generator (ING).ResultsOur findings show that local perturbations yield divergent interpretive outcomes depending on internal cognitive states. Narrative coherence emerges from recursive policy dynamics, and traditional attribution methods fail to capture bifurcation points in decision space.DiscussionWe argue for a shift from post-hoc local approximations to embedded narrative-based interpretability. This reframing supports epistemic transparency in future AGI systems and aligns with cognitive theories of understanding.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frai.2025.1635691</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frai.2025.1635691</link>
        <title><![CDATA[Epistemic responsibility: toward a community standard for human-AI collaborations]]></title>
        <pubdate>2025-07-04T00:00:00Z</pubdate>
        <category>Opinion</category>
        <author>Dan Lloyd</author>
        <description></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frai.2025.1535845</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frai.2025.1535845</link>
        <title><![CDATA[Navigating AI ethics: ANN and ANFIS for transparent and accountable project evaluation amidst contesting AI practices and technologies]]></title>
        <pubdate>2025-04-30T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Sandeep Wankhade</author><author>Manoj Sahni</author><author>Ernesto León-Castro</author><author>Maricruz Olazabal-Lugo</author>
        <description><![CDATA[IntroductionThe rapid evolution of Artificial Intelligence (AI) necessitates robust ethical frameworks to ensure responsible project deployment. This study addresses the challenge of quantifying ethical criteria in AI projects amidst contesting communicative practices, organizational structures, and enabling technologies, which shape AI’s societal implications.MethodsWe propose a novel framework integrating Artificial Neural Networks (ANN) and Adaptive Neuro-Fuzzy Inference Systems (ANFIS) to evaluate AI project performance and model ethical uncertainties using Fuzzy logic. A Fuzzy weighted average approach quantifies critical ethical dimensions: transparency, fairness, accountability, privacy, security, explainability, human involvement, and societal impact.ResultsThe framework enables a structured assessment of AI projects, enhancing transparency and accountability by mapping ethical criteria to project outcomes. ANN evaluates performance metrics, while ANFIS models uncertainties, providing a comprehensive ethical evaluation under complex conditions.DiscussionBy combining ANN and ANFIS, this study advances the understanding of AI’s ethical dimensions, offering a scalable approach for accountable AI systems. It reframes organizational communication and decision-making, embedding ethics within AI’s technological and structural contexts. This work contributes to responsible AI innovation, fostering trust and societal alignment in AI deployments.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frai.2025.1523390</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frai.2025.1523390</link>
        <title><![CDATA[Adaptive Neuro-Fuzzy Inference System guided objective function parameter optimization for inverse treatment planning]]></title>
        <pubdate>2025-02-12T00:00:00Z</pubdate>
        <category>Methods</category>
        <author>Eduardo Cisternas Jiménez</author><author>Fang-Fang Yin</author>
        <description><![CDATA[Intensity-Modulated Radiation Therapy requires the manual adjustment to numerous treatment plan parameters (TPPs) through a trial-and-error process to deliver precise radiation doses to the target while minimizing exposure to surrounding healthy tissues. The goal is to achieve a dose distribution that adheres to a prescribed plan tailored to each patient. Developing an automated approach to optimize patient-specific prescriptions is valuable in scenarios where trade-off selection is uncertain and varies among patients. This study presents a proof-of-concept artificial intelligence (AI) system based on an Adaptive Neuro-Fuzzy Inference System (ANFIS) to guide IMRT planning and achieve optimal, patient-specific prescriptions in aligned with a radiation oncologist's treatment objectives. We developed an in-house ANFIS-AI system utilizing Prescription Dose (PD) constraints to guide the optimization process toward achievable prescriptions. Mimicking human planning behavior, the AI system adjusts TPPs, represented as dose-volume constraints, to meet the prescribed dose goals. This process is informed by a Fuzzy Inference System (FIS) that incorporates prior knowledge from experienced planners, captured through “if-then” rules based on routine planning adjustments. The innovative aspect of our research lies in employing ANFIS's adaptive network to fine-tune the FIS components (membership functions and rule strengths), thereby enhancing the accuracy of the system. Once calibrated, the AI system modifies TPPs for each patient, progressing through acceptable prescription levels, from restrictive to clinically allowable. The system evaluates dosimetric parameters and compares dose distributions, dose-volume histograms, and dosimetric statistics between the conventional FIS and ANFIS. Results demonstrate that ANFIS consistently met dosimetric goals, outperforming FIS with a 0.7% improvement in mean dose conformity for the planning target volume (PTV) and a 28% reduction in mean dose exposure for organs at risk (OARs) in a C-Shape phantom. In a mock prostate phantom, ANFIS reduced the mean dose by 17.4% for the rectum and by 14.1% for the bladder. These findings highlight ANFIS's potential for efficient, accurate IMRT planning and its integration into clinical workflows.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frai.2024.1496689</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frai.2024.1496689</link>
        <title><![CDATA[An overview of pink eye infection to evaluate its medications: group decision-making approach with 2-tuple linguistic T-spherical fuzzy WASPAS method]]></title>
        <pubdate>2025-01-21T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>M. Waheed Rasheed</author><author>Hind Y. Saleh</author><author>Areen A. Salih</author><author>Jahangeer Karamat</author><author>Muhammad Bilal</author>
        <description><![CDATA[An infectious eye illness known as pink eye results in ocular redness, irritation, and mucus. Schools are an especially vulnerable region for dissemination because they can propagate that contagious disease quickly via direct or indirect interactions. Choosing the right medication to treat pink eye infection is typically thought of as an intricate multi-attribute group decision-making concern. The goal of this research is to construct a multi-attribute group decision-making framework that assesses six pink eye treatment medications, including Bleph-10, Moxeza, Zymar, Romycin, Polytrim, and Bacticin. The constructed multi-attribute group decision-making framework includes the following scenario: (1) In contrast to other types of fuzzy sets, the 2-tuple linguistic T-spherical fuzzy set (2TLT-SFS) looks to be a potent tool for dealing with informational inconsistencies in decision-making scenarios; (2) in order to render the 2TLT-SF accumulation details processing more flexible, the addition, multiplication, scalar multiplication, and exponential laws that are predicated on the Schweizer-Sklar collection of t-conorms and t-norms are described; (3) the Schweizer-Sklar weighted average and Schweizer-Sklar weighted geometric operators are then put forward employing the aforementioned operations to combine the data; (4) subsequently, using newly developed operators (referred to as 2TLT-SF Schweizer-Sklar weighted average and 2TLT-SF Schweizer-Sklar weighted geometric), this work enhances the conventional weighted aggregated sum product assessment (WASPAS) approach. The computation procedure for this methodology is thoroughly given to rank the alternatives; (5) to confirm the viability of the suggested approach, thorough computational and simulation assessments are conducted. An examination of the developed and existing research is compared to demonstrate the benefits of the suggested analysis.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frai.2024.1402719</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frai.2024.1402719</link>
        <title><![CDATA[Enhancing breast cancer treatment selection through 2TLIVq-ROFS-based multi-attribute group decision making]]></title>
        <pubdate>2024-06-03T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Muhammad Waheed Rasheed</author><author>Abid Mahboob</author><author>Anfal Nabeel Mustafa</author><author>Israa Badi</author><author>Zainab Abdulkhaleq Ahmed Ali</author><author>Zainb H. Feza</author>
        <description><![CDATA[IntroductionBreast cancer is an extremely common and potentially fatal illness that impacts millions of women worldwide. Multiple criteria and inclinations must be taken into account when selecting the optimal treatment option for each patient.MethodsThe selection of breast cancer treatments can be modeled as a multi-attribute group decision-making (MAGDM) problem, in which a group of experts evaluate and rank alternative treatments based on multiple attributes. MAGDM methods can aid in enhancing the quality and efficacy of breast cancer treatment selection decisions. For this purpose, we introduce the concept of a 2-tuple linguistic interval-valued q-rung orthopair fuzzy set (2TLIVq-ROFS), a new development in fuzzy set theory that incorporates the characteristics of interval-valued q-rung orthopair fuzzy set (IVq-ROFS) and 2-tuple linguistic terms. It can express the quantitative and qualitative aspects of uncertain information, as well as the decision-makers' level of satisfaction and dissatisfaction.ResultsThen, the 2TLIVq-ROF weighted average (2TLIVq-ROFWA) operator and the 2TLIVq-ROF weighted geometric (2TLIVq-ROFWJ) operator are introduced as two new aggregation operators. In addition, the multi-attribute border approximation area comparison (MABAC) method is extended to solve the MAGDM problem with 2TLIVq-ROF information.DiscussionTo demonstrate the efficacy and applicability of the suggested model, a case study of selecting the optimal breast cancer treatment is presented. The results of the computations show that the suggested MAGDM model is able to handle imprecision and subjectivity in complicated decision-making scenarios and opens new research scenarios for scholars.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frai.2024.1347626</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frai.2024.1347626</link>
        <title><![CDATA[A MAGDM approach for evaluating the impact of artificial intelligence on education using 2-tuple linguistic q-rung orthopair fuzzy sets and Schweizer-Sklar weighted power average operator]]></title>
        <pubdate>2024-03-14T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Abid Mahboob</author><author>Zafar Ullah</author><author>Ali Ovais</author><author>Muhammad Waheed Rasheed</author><author>S. A. Edalatpanah</author><author>Kainat Yasin</author>
        <description><![CDATA[The impact of artificial intelligence (AI) in education can be viewed as a multi-attribute group decision-making (MAGDM) problem, in which several stakeholders evaluate the advantages and disadvantages of AI applications in educational settings according to distinct preferences and criteria. A MAGDM framework can assist in providing transparent and logical recommendations for implementing AI in education by methodically analyzing the trade-offs and conflicts among many components, including ethical, social, pedagogical, and technical concerns. A novel development in fuzzy set theory is the 2-tuple linguistic q-rung orthopair fuzzy set (2TLq-ROFS), which is not only a generalized form but also can integrate decision-makers quantitative evaluation ideas and qualitative evaluation information. The 2TLq-ROF Schweizer-Sklar weighted power average operator (2TLq-ROFSSWPA) and the 2TLq-ROF Schweizer-Sklar weighted power geometric (2TLq-ROFSSWPG) operator are two of the aggregation operators we create in this article. We also investigate some of the unique instances and features of the proposed operators. Next, a new Entropy model is built based on 2TLq-ROFS, which may exploit the preferences of the decision-makers to obtain the ideal objective weights for attributes. Next, we extend the VIseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) technique to the 2TLq-ROF version, which provides decision-makers with a greater space to represent their decisions, while also accounting for the uncertainty inherent in human cognition. Finally, a case study of how artificial intelligence has impacted education is given to show the applicability and value of the established methodology. A comparative study is carried out to examine the benefits and improvements of the developed approach.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frai.2021.638951</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frai.2021.638951</link>
        <title><![CDATA[Improving the Robustness of Object Detection Through a Multi-Camera–Based Fusion Algorithm Using Fuzzy Logic]]></title>
        <pubdate>2021-05-26T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Md Nazmuzzaman Khan</author><author>Mohammad Al Hasan</author><author>Sohel Anwar</author>
        <description><![CDATA[A single camera creates a bounding box (BB) for the detected object with certain accuracy through a convolutional neural network (CNN). However, a single RGB camera may not be able to capture the actual object within the BB even if the CNN detector accuracy is high for the object. In this research, we present a solution to this limitation through the usage of multiple cameras, projective transformation, and a fuzzy logic–based fusion. The proposed algorithm generates a “confidence score” for each frame to check the trustworthiness of the BB generated by the CNN detector. As a first step toward this solution, we created a two-camera setup to detect objects. Agricultural weed is used as objects to be detected. A CNN detector generates BB for each camera when weed is present. Then a projective transformation is used to project one camera’s image plane to another camera’s image plane. The intersect over union (IOU) overlap of the BB is computed when objects are detected correctly. Four different scenarios are generated based on how far the object is from the multi-camera setup, and IOU overlap is calculated for each scenario (ground truth). When objects are detected correctly and bounding boxes are at correct distance, the IOU overlap value should be close to the ground truth IOU overlap value. On the other hand, the IOU overlap value should differ if BBs are at incorrect positions. Mamdani fuzzy rules are generated using this reasoning, and three different confidence scores (“high,” “ok,” and “low”) are given to each frame based on accuracy and position of BBs. The proposed algorithm was then tested under different conditions to check its validity. The confidence score of the proposed fuzzy system for three different scenarios supports the hypothesis that the multi-camera–based fusion algorithm improved the overall robustness of the detection system.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frai.2020.614853</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frai.2020.614853</link>
        <title><![CDATA[Control Sequence Ranking for Critical System Based on Health of Equipment Thanks to Choquet Integral]]></title>
        <pubdate>2021-03-24T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Mohammed-Farouk Bouaziz</author><author>Pascale Marange</author><author>Alexandre Voisin</author><author>Jean-Francois Petin</author>
        <description><![CDATA[This paper presents a ranking method of operating sequences based on the actual condition of complex systems. This objective is achieved using the health checkup concept and the multiattribute utility theory. Our contribution is the proposal of sequences ranking process using data and experts’ judgments. The ranking results in a decision-making element; it allows experts to have an objective and concise overall ranking to be used for decision making. A case study is presented based on an experimental platform; it allows us to compare two aggregation operators: the weighted mean and the Choquet integral.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frai.2020.00050</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frai.2020.00050</link>
        <title><![CDATA[Intelligent Multirobot Navigation and Arrival-Time Control Using a Scalable PSO-Optimized Hierarchical Controller]]></title>
        <pubdate>2020-08-07T00:00:00Z</pubdate>
        <category>Methods</category>
        <author>Yu-Cheng Chang</author><author>Anna Dostovalova</author><author>Chin-Teng Lin</author><author>Jijoong Kim</author>
        <description><![CDATA[We present a hierarchical fuzzy logic system for precision coordination of multiple mobile agents such that they achieve simultaneous arrival at their destination positions in a cluttered urban environment. We assume that each agent is equipped with a 2D scanning Lidar to make movement decisions based on local distance and bearing information. Two solution approaches are considered and compared. Both of them are structured around a hierarchical arrangement of control modules to enable synchronization of the agents' arrival times while avoiding collision with obstacles. The proposed control module controls both moving speeds and directions of the robots to achieve the simultaneous target-reaching task. The control system consists of two levels: the lower-level individual navigation control for obstacle avoidance and the higher-level coordination control to ensure the same time of arrival for all robots at their target. The first approach is based on cascading fuzzy logic controllers, and the second approach considers the use of a Long Short-Term Memory recurrent neural network module alongside fuzzy logic controllers. The parameters of all the controllers are optimized using the particle swarm optimization algorithm. To increase the scalability of the proposed control modules, an interpolation method is introduced to determine the velocity scaling factors and the searching directions of the robots. A physics-based simulator, Webots, is used as a training and testing environment for the two learning models to facilitate the deployment of codes to hardware, which will be conducted in the next phase of our research.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frai.2020.00001</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frai.2020.00001</link>
        <title><![CDATA[Fuzzy Leaky Bucket System for Intelligent Management of Consumer Electricity Elastic Load in Smart Grids]]></title>
        <pubdate>2020-01-31T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Miltiadis Alamaniotis</author>
        <description><![CDATA[This paper frames itself in an informational rich smart electricity grid where consumers have access to various streams of information and make decisions over their daily consumption pattern. In particular, a new intelligent management system to accommodate possible optimal decisions for elastic load consumption is discussed. The energy management system implements a fuzzy driven leaky bucket that manages the elastic load of a consumer by controlling the token rate buffer via a set of four fuzzy variables (among them the electricity price). The goal of this innovative system is to allow loads that are identified as elastic to be scheduled only when it is potentially beneficial to the consumer. To that end, a fuzzy algorithm comprised of a set of rules is developed to manage the token rate of the leaky bucket and through that the decisions over the fate of elastic loads. The developed system is applied on a set of real-world electricity consumption data taken from a residential consumer, and benchmarked against a full scheduling method, where the elastic load is fully scheduled offline. Results exhibit that the proposed fuzzy logic method outperforms the full scheduling method in the vast majority of the cases, i.e., over 79% of the cases with respect to consumption cost. Furthermore, they validate its ability to conduct real time decision making with no human in the loop.]]></description>
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