This article presents a narrative review of Multi-Agent Systems and Emergent Cooperation in the context of Artificial Intelligence. The literature on this topic has expanded substantially over recent decades, yet it remains fragmented across subfields, methods, and national research traditions. Drawing on an interpretive synthesis of representative contributions, the review reconstructs the historical development of the area, examines the conceptual foundations and definitional disputes that organize its debates, and maps the contemporary landscape of research, including the methodological shift toward data-intensive approaches and the institutional pressures that shape publication practice. Particular attention is given to the role of multi-agent and cooperation as organizing themes, and to the conditions under which findings from different research traditions can be brought into productive comparison. The review identifies three synthetic conclusions: the literature is cumulatively strong but organizationally weak; methodological pluralism is better understood as a resource than as a defect; and the growing practical salience of the topic raises the stakes of its unresolved conceptual questions. An agenda for future work is proposed, emphasizing integrative research designs, transparent synthesis practices, and the protection of definitional and infrastructural work on which cumulative progress depends. The article is intended as both a reference map for newcomers and a provocation for specialists in Artificial Intelligence.
Zen Revista, 10 IA· Zenodo (CERN European Organi...· 0 citations
This article presents a narrative review of Multi-Agent Systems and Emergent Cooperation in the context of Artificial Intelligence. The literature on this topic has expanded substantially over recent decades, yet it remains fragmented across subfields, methods, and national research traditions. Drawing on an interpretive synthesis of representative contributions, the review reconstructs the historical development of the area, examines the conceptual foundations and definitional disputes that organize its debates, and maps the contemporary landscape of research, including the methodological shift toward data-intensive approaches and the institutional pressures that shape publication practice. Particular attention is given to the role of multi-agent and cooperation as organizing themes, and to the conditions under which findings from different research traditions can be brought into productive comparison. The review identifies three synthetic conclusions: the literature is cumulatively strong but organizationally weak; methodological pluralism is better understood as a resource than as a defect; and the growing practical salience of the topic raises the stakes of its unresolved conceptual questions. An agenda for future work is proposed, emphasizing integrative research designs, transparent synthesis practices, and the protection of definitional and infrastructural work on which cumulative progress depends. The article is intended as both a reference map for newcomers and a provocation for specialists in Artificial Intelligence.
Zen Revista, 10 IA· Zenodo (CERN European Organi...· 0 citations
Abstract Objective Researchers are increasingly using generative artificial intelligence (GenAI) to support tasks such as literature review, academic writing, programming, data interpretation, and knowledge synthesis. We developed a Rapid Assessment Method for Responsible Use of Generative AI in Scientific Research (RAM-GenAI) to provide a practical approach for evaluating responsible practices in low- and middle-income settings like Uganda. Results description Rather than presenting long checklists of many individual requirements, RAM-Gen AI have five major areas of assessment. These are groups related to the principles of responsible GenAI use. They includes, transparency of AI use, verification of AI-generated outputs, data responsibility, human oversight, and reproducibility of AI-assisted research workflows. Each domain contains assessment criteria designed to identify strengths, risks, and areas requiring improvement. RAM-GenAI developed provides researchers and institutions with a practical approach for evaluating responsible GenAI practices before, during, or after AI-assisted research activities.
Global land degradation affects approximately 2 billion hectares, threatening food security, biodiversity, and climate stability while undermining the United Nations Sustainable Development Goals (SDGs). The concurrent urgency to decarbonize the energy system and mobilize green finance for sustainable transitions has created a rare policy window in which AI-optimized biofuel production on degraded lands can simultaneously serve multiple imperatives. This study presents a comprehensive secondary data analysis of AI-based optimization frameworks for deploying biofuel production systems on degraded lands, integrating an explicit green finance dimension that has been largely absent from prior synthesis literature. Drawing on 152 peer-reviewed studies and authoritative datasets from FAO, IEA, IRENA, UNCCD, the Green Climate Fund (GCF), and the World Bank, we analyze machine learning, deep learning, reinforcement learning, and hybrid AI architectures applied to feedstock selection, soil remediation, yield prediction, supply-chain logistics, and green finance risk-return optimization. Our findings reveal that AI-optimized biofuel systems on degraded lands recover 75-94% of prime-land bioenergy yields, sequester 8.3-10.5 t CO2e ha-1 over 30 years, reduce lifecycle GHG emissions by 55-88%, and generate internal rates of return of 9-22% when green finance instruments are systematically integrated. Green bonds, Article 6 carbon credits, GCF concessional finance, and blended finance structures are identified as the most impactful instruments, collectively capable of reducing project risk scores by 30-45% and expanding the investable universe of degraded-land biofuel projects by an estimated 340%. We develop the AI-Biofuel-Land Restoration (ABLR) conceptual framework with explicit green finance routing pathways and identify critical policy enablers for global deployment. This study advances the evidence base for policy-makers, investors, researchers, and development practitioners working at the intersection of artificial intelligence, bioenergy, green finance, and sustainable land management.
ANJALI CHAUDHARY, Hebah Shalhoob, Kholoud Y. Bajunaied et al.· Processes· 0 citations
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As organizations that are explicitly values-driven, public libraries play a critical role in building and maintaining a democratic, equitable, and sustainable information environment. With the growing potential of artificial intelligence (AI) to reshape library collections, services, and workflows, public libraries must determine how to engage with these technologies while maintaining longstanding library values. Despite widespread discussion of AI’s impact on public libraries, to our knowledge there exists no published analysis of American and Canadian public library AI policies to date. In this paper, we address this gap first through an environmental scan of public library websites to identify publicly available AI policy statements. We then analyze these policy statements according to how they include library values. In our scan of over 200 library websites, we identified just 16 publicly available AI policies. ese policies all govern internal or staff usage rather than patron usage. All policies reference at least two library values, with privacy and security the most frequently cited.
Kathryn FitzGerald, Benjamin Charles Germain Lee· Public Library Quarterly· 0 citations
Abstract Large-scale artificial intelligence (AI) models are fundamentally transforming industries and redefining the paradigm of human–machine collaboration. While the technological revolution signals a new era of machine intelligence, the continued scaling of these models has exposed significant limitations in contemporary hardware architectures, manifesting as constraints on computational efficiency, interconnection bandwidth, and memory capacity. These three dimensions are inseparably intertwined, such that advances along any single axis often exacerbate bottlenecks in the others, rendering isolated optimizations increasingly ineffective. Achieving an optimal balance among them to maximize system efficiency therefore remains a central challenge in the design of scalable AI systems. To address this challenge, we introduce Computation-Bandwidth-Memory Trade-offs, termed the AI Trinity, a unified paradigm that positions computation , bandwidth , and memory as coequal pillars for next-generation AI infrastructure. Inspired by the device-edge-cloud collaboration principle from the AI Flow framework, we formulate AI Trinity as a resource-theoretic view of the computation-bandwidth-memory bottlenecks in distributed AI systems. Within this framework, AI Trinity identifies three fundamental trade-offs: (1) More Computation $$\rightarrow$$ → Less Bandwidth, wherein computational resources are exploited to reduce data transmission under limited bandwidth conditions, (2) More Bandwidth $$\rightarrow$$ → Less Memory, which exploits abundant communication capacity to populate or refresh memory when local storage resources are constrained, and (3) More Memory $$\rightarrow$$ → Less Computation, whereby storage capacity are utilized to mitigate redundant computation when computational costs are prohibitive. We illustrate its effectiveness through representative system designs spanning edge–cloud communication, large-scale distributed training, and model inference. The innovations embodied in AI Trinity advance a new paradigm for scalable AI infrastructure, providing both a conceptual foundation and practical guidance for a broad range of application scenarios.
Our students and classrooms have changed significantly in the last few years. Technology has reshaped how we teach and learn. Artificial intelligence is changing how students engage with information. At the same time, higher education classrooms continue to bring together learners with different experiences, expectations and approaches to learning. So, has the way we assess learning kept pace? This session takes a closer look at what assessment can look like in today’s higher education classroom, with a focus on authentic assessment and intentional course design. We’ll consider how assessment can move beyond simply measuring what students remember and instead create opportunities for students to apply what they know in meaningful ways. The session will also explore the opportunities and challenges AI brings to assessment along with practical considerations for designing courses that support a multigenerational classroom and encourage student engagement. Whether you are rethinking an existing assessment or starting from scratch, this session offers ideas and strategies for taking a more intentional approach to assessment in the 21st century classroom.
Ashton Hays· Scholar Works at Harding (Ha...· 0 citations
Artificial Intelligence (AI) is increasingly embedded in organisational life, and this dissertation explores employees’ experience of AI in everyday work. It focuses on broad-application AI, commonly introduced through organisational initiatives led by HR, Learning & Development, or IT with the aim of supporting employees. Rather than approaching AI as a discrete tool with fixed effects, the dissertation examines unfolding relations in specific contexts and ask what matters in AI-inclusive work. This dissertation shows that AI applications are not merely sets of functionalities, but are inseparable from the situated context, shaping and being shaped by employees, work practices, and the organisational environment. It finds that conversational AI, using natural language instead of a menu-based interface for employee self-service, can contribute to a motivation and well-being supportive organisational environment by fostering greater autonomy, competence and relatedness. It also examines how employees interact with AI systems internal and external to their organisations (including unendorsed “shadow AI”), such as contextual search, content recommendations and generative AI in knowledge work. These interactions, shaped by past habits, present constraints and imagined futures, gradually reshape the boundaries of tasks, relationships and the meaning of work. Finally, the dissertation argues that different types of AI matter in different ways because they elicit different forms of engagement and different workplace experiences. Discriminative AI is positioned as a tool for the task, foregrounding efficiency and effectiveness, while also potentially giving rise to possible negative long-term experiences and raising questions about meaningful work. Generative AI is positioned as a medium for creative expression, foregrounding exploration, innovation, and creative actions, thereby fostering more creative and positive experiences at work. Overall, this dissertation offers insights for scholars and practitioners into emerging relations in AI-inclusive work, showing that the value and effects of AI do not reside in technology alone, but emerge through the relations among employees, AI characteristics and organisational context. In doing so, it offers a perspective that moves beyond short-term gains and highlights the longer-term value of creating work environments in which AI is experienced as useful, meaningful and supportive.
Dijana; id_orcid 0009-0005-2046-9468 Aleksić· EUR Research Repository (Era...· 0 citations
The increasing adoption of generative artificial intelligence (AI) in software development introduces a structural asymmetry: implementation generation now occurs at machine speed, while behavioral governance remains constrained by institutional deliberation. These processes are not merely mismatched in velocity; they are orthogonal in function. Generation produces executable artifacts. Governance establishes permissible behavior. This paper formalizes the generation-governance impedance mismatch as a structural property of AI-accelerated systems. We argue that conventional governance mechanisms—code review, testing, and audit processes—operate at the implementation layer and therefore cannot scale to match machine-speed generation without structural reform.
Bhash Ganti, Bhash Ganti· Zenodo (CERN European Organi...· 0 citations
The increasing adoption of generative artificial intelligence (AI) in software development introduces a structural asymmetry: implementation generation now occurs at machine speed, while behavioral governance remains constrained by institutional deliberation. These processes are not merely mismatched in velocity; they are orthogonal in function. Generation produces executable artifacts. Governance establishes permissible behavior. This paper formalizes the generation-governance impedance mismatch as a structural property of AI-accelerated systems. We argue that conventional governance mechanisms—code review, testing, and audit processes—operate at the implementation layer and therefore cannot scale to match machine-speed generation without structural reform.
Bhash Ganti, Bhash Ganti· Zenodo (CERN European Organi...· 0 citations
Autonomous flight in unknown cluttered environments is hindered by the computation-quality-memory trilemma of onboard trajectory generation. In this paper, we propose an efficient end-to-end local planner via imitation learning. A lightweight offline-primitive-based dataset collection framework is designed to produce safe and high-quality trajectory primitives in non-convex environments. A compact neural network directly maps sensory inputs to polynomial coefficients that inherently encode higher-order dynamical information. The learned policy generates smooth, empirically collision-free and dynamically feasible trajectories in real time without back-end solving. It achieves ultra-fast computation (below 1ms on a standard desktop and average 3.68ms during onboard flight), while maintaining low onboard memory requirements (less than 1.5MiB). Extensive simulation benchmarks demonstrate superiority in both planning latency and target-reaching progress quality. Zero-shot deployment in real-world experiments further validates the robust sim-to-real transfer capability of the proposed method.
Zhitao Liu, Guangtong Xu, Zihan Wang et al.· 0 citations
Conditional segmentation models may be trained and evaluated with auxiliary signals cleaner than those available at deployment. We study this protocol-level manifestation of shortcut learning and auxiliary-variable shift in phase-conditioned echocardiographic segmentation. The complementary gap pair measures loss on the deployable oracle-estimated pathway and probes sensitivity on the oracle-random pathway. On held-out CAMUS data, one strong-cyclic, oracle-selected run fails severely with estimated phase, while sensitivity to incorrect phase persists across three runs. On EchoNet-Dynamic, the current estimator remains usable, but random-phase testing reveals strong latent sensitivity. Deployment-aware checkpoint selection and phase perturbation reduce both gaps with little change in mean Dice. Exploratory subgroup analyses quantify variation across measured strata, and a downstream ejection fraction (EF) audit shows that recovering segmentation does not necessarily recover EF error or signed bias. Together, the gaps test whether oracle-conditioned performance survives the inference pathway actually available at deployment.
Dang P. M. Cao, Hieu D. Pham, Hieu Pham· 0 citations
