Human Problem Solving Research Articles (Page 1)

E2TP: Element to tuple prompting improves aspect sentiment tuple prediction.

We humans are capable of solving challenging planning problems, but the range of adaptive strategies that we use to address them is not yet fully characterized. Here, we designed a series of problem-solving tasks that require planning at different depths. After systematically comparing the performance of participants and planning models, we found that when facing problems that require planning to a certain number of subgoals (from 1 to 8), participants make an adaptive use of their cognitive resources—namely, they tend to select an initial plan having the minimum required depth, rather than selecting the same depth for all problems. These results support the view of problem-solving as a bounded rational process, which adapts costly cognitive resources to task demands.

Zoological research drives progress in developing new business ideas. Business managers and zoological researchers lead to new biological discoveries that benefit different industries because they develop new medical technology, such as agriculture practices, robotic systems, and energy technologies. Combining animal studies with business operations leads to different types of positive outcomes. Businesses gain better knowledge of complex biological systems to make decisions that improve resource use and product development and promote sustainable business operations. Modern business leaders recognize the scientific breakthrough from zoological study as it produces robotic machines that help farming and produces energy breakthroughs. It has grown into a real business movement that leads companies in sustainability and innovation to make lasting strategic changes. Our society studies how nature handles problems when trying to solve human issues. Biomimicry is the practice of learning nature's strategies to solve human challenges. Moreover, zoological research can play a critical role in driving sustainable innovation. As companies seek to reduce their environmental footprint and improve their social responsibility, nature provides a wealth of inspiration. Biomimicry can help develop more efficient resource use, reduce waste, and promote eco-friendly practices throughout the supply chain Our approach to human problem-solving focuses on studying how nature performs these tasks. Biomimicry can help develop more efficient resource use, reduce waste, and promote eco-friendly practices throughout the supply chain. Humans took inspiration for Velcro by observing how burrs cling to animal fur and used robotic factories that act based on animal locomotion patterns. All companies follow normal business procedures that include applying zoological sciences. The technology that promotes vaccine creation is drawn from research on animal defense systems. The behavior of animals helps robotic engineers design drones and autonomous vehicle systems that assist with monitoring and cargo transport activities. Modern power technologies take flight inspiration from birds, and scientists use animal migration behavior to create their essential climate model for sustainability. As we look to the future, Zoology remains fundamental to creating new business ideas that enhance commercial success. Through natural intelligence, companies can discover better ways to grow, protect our environment, and successfully reach their business targets. In conclusion, the connection between zoological science and business development will help industries grow and create a sustainable future for every person. Our success depends on using nature's limitless power to reach breakthroughs in industry development.

Class incremental learning (CIL) is a specific scenario in incremental learning. It aims to continuously learn new classes from the data stream, which suffers from the challenge of catastrophic forgetting. Inspired by the human hippocampus, the CIL method for replaying episodic memory offers a promising solution. However, the limited buffer budget restricts the number of old class samples that can be stored, resulting in an imbalance between new and old class samples during each incremental learning stage. This imbalance adversely affects the mitigation of catastrophic forgetting. Therefore, we propose a novel CIL method based on multi-granularity balance strategy (MGBCIL), which is inspired by the three-way granular computing in human problem-solving. In order to mitigate the adverse effects of imbalances on catastrophic forgetting at fine-, medium-, and coarse-grained levels during training, MGBCIL introduces specific strategies across the batch, task, and decision stages. Specifically, a weighted cross-entropy loss function with a smoothing factor is proposed for batch processing. In the process of task updating and classification decision, contrastive learning with different anchor point settings is employed to promote local and global separation between new and old classes. Additionally, the knowledge distillation technology is used to preserve knowledge of the old classes. Experimental evaluations on CIFAR-10 and CIFAR-100 datasets show that MGBCIL outperforms other methods in most incremental settings. Specifically, when storing 3 exemplars on CIFAR-10 with Base2 Inc2 setting, the average accuracy is improved by up to 9.59% and the forgetting rate is reduced by up to 25.45%.

Artificial intelligence (AI) is a technology that enables computers and machines to simulate human learning, comprehension, problem solving, decision making, creativity and autonomy. AI software can perform a variety of tasks, including:Content generation, Data analysis, Prediction, Process automation, Decision-making, Learning, and Problem-solving. ML/AI can help vision system experts to identify and sort false ejects from large image data sets for oral, Injectables, solids, semi-solid Doses form products “ONLINE INSPECTION”.

Artificial intelligence (AI) is technology that enables computers and machines to simulate human learning, comprehension, problem solving, decision making, creativity and autonomy. Ancient civilizations contained tales of intelligent robots, such as Greek and Chinese cultures. AI as a modern phenomenon which took shape in the mid-20th century with the advent of digital computers. The first breakthrough, published in 1950, was Alan Turing's paper, ‘Computing Machinery and Intelligence. AI can make provide detailed solution to users and experts. They can act independently, replacing the need for human intelligence or intervention (a classic example being a self-driving car). Many sectors like finance, healthcare, to human resource management (HRM), rely on AI technologies. AI's integration in HRM has brought about great change in conventional HR practices used to measure efficiency, accuracy, strategic decision-making, etc. AI technologies help to provide solution to automation of repetitive tasks, analysis of sprawling volumes of data and insights facilitating decision-making. Through this research paper we try to understand the AI's effects on HR processes, in terms of recruitment, onboarding, performance appraisal, training and development, employee engagement, workforce planning; understanding the benefits and cons of implementing AI in HRM; ethical considerations and potential biases in AI algorithms relevant to HR practices; analysis of emergent AI trends with respect to HRM. Trends in AI in HR tend towards a most promising future, presenting several innovations. Accordingly, HR professionals will be expected to acquire competency with regards to new roles and responsibilities to avail AI in enhancing strategic planning, ethical oversight, and data-driven decision-making.

Dementia is a growing global health challenge due to the aging population and lifestyle changes. Early and accurate diagnosis is crucial but often difficult and costly. The Case-Based Reasoning (CBR) method in artificial intelligence offers a solution by mimicking human problem-solving based on past experiences. This study aims to develop and implement an efficient and reliable CBR-based dementia diagnosis system. The system is expected to analyze and compare patient symptoms and medical histories with documented cases to provide faster and more accurate diagnostic recommendations. The implementation of CBR in a web-based expert system using PHP and MySQL has proven effective, significantly contributing to the improvement of patient quality of life and healthcare system effectiveness.

Artificial intelligence in medicine: advantages and disadvantages for today and the future

Abstract This study extends on work investigating the increased application of mixed methods research in prehospital care. The complexity and diversity of the prehospital environment warrants flexibility and applicability to guide the research process, yet little attention has focused on the theoretical lens used in prehospital research when using mixed methods research. Pragmatism’s characteristics of human inquiry, problem solving, and action align with a clinical reasoning approach, supporting the prehospital researchers epistemological understanding of the world. Through further exploration of this alignment, this article proposes pragmatism as a compatible theoretical lens for mixed methods in prehospital research.

The paper delves into a comparative analysis between human and artificial intelligence (AI) capabilities in algorithm development, with a specific focus on the challenges presented in the "Advent of Code." The research thoroughly investigates the performance of Generative Pre-trained Transformers (GPTs), such as ChatGPT and Bard, in solving intricate algorithmic problems and benchmarks these results against those achieved by human participants. A sizeable portion of the study is dedicated to understanding the nuances of prompt engineering in AI and how it affects the problem-solving process, alongside exploring the choice of programming languages used by both AI and humans. The methodology of the research is extensive, involving the participation of both AI models and human subjects, who vary in their levels of programming expertise. This approach allows for a comprehensive evaluation of the correctness and efficiency of solutions, along with the time taken to resolve the given problems. The results from this study reveal intriguing insights. While AI models like GPTs demonstrate an impressive speed in problem resolution, they often fall short in accuracy when compared to human problem-solvers, particularly in tasks demanding deeper contextual understanding and creative reasoning. Furthermore, the study delves into the impact of time constraints on the effectiveness of problem-solving strategies employed by both AI and humans. It finds that under strict time constraints, AI models can quickly generate solutions, but these solutions may lack the depth and accuracy found in those devised by human participants. This aspect of the research highlights the trade-off between speed and precision in AI-driven problem solving. The research extends its implications beyond mere performance comparison. It suggests the potential for a synergistic approach where the computational efficiency and rapid problem-solving abilities of AI can be effectively combined with the nuanced understanding and creative problem-solving skills inherent in humans. This hybrid approach could redefine the future landscape of programming and algorithm development. The study not only provides a critical analysis of the capabilities of AI in the realm of algorithmic problem-solving but also paves the way for future exploration into the collaborative dynamics of human-AI interaction in programming. It highlights the evolving role of AI in programming and underscores the importance of balancing AI’s computational prowess with human creativity and adaptability in solving complex, real-world problems.

Abstract Exploring the opportunities for incorporating Artificial Intelligence (AI) to support team problem-solving has been the focus of intensive ongoing research. However, while the incorporation of such AI tools into human team problem-solving can improve team performance, it is still unclear what modality of AI integration will lead to a genuine human–AI partnership capable of mimicking the dynamic adaptability of humans. This work unites human designers with AI Partners as fellow team members who can both reactively and proactively collaborate in real-time toward solving a complex and evolving engineering problem. Team performance and problem-solving behaviors are examined using the HyForm collaborative research platform, which uses an online collaborative design environment that simulates a complex interdisciplinary design problem. The problem constraints are unexpectedly changed midway through problem-solving to simulate the nature of dynamically evolving engineering problems. This work shows that after the unexpected design constraints change, or shock, is introduced, human–AI hybrid teams perform similarly to human teams, demonstrating the capability of AI Partners to adapt to unexpected events. Nonetheless, hybrid teams do struggle more with coordination and communication after the shock is introduced. Overall, this work demonstrates that these AI design partners can participate as active partners within human teams during a large, complex task, showing promise for future integration in practice.

Knowledge graphs (KGs), as structured representations of real world facts, are intelligent databases incorporating human knowledge that can help machine imitate the way of human problem solving. However, KGs are usually huge and there are inevitably missing facts in KGs, thus undermining applications such as question answering and recommender systems that are based on knowledge graph reasoning. Link prediction for knowledge graphs is the task aiming to complete missing facts by reasoning based on the existing knowledge. Two main streams of research are widely studied: one learns low-dimensional embeddings for entities and relations that can explore latent patterns, and the other gains good interpretability by mining logical rules. Unfortunately, the heterogeneity of modern KGs that involve entities and relations of various types is not well considered in the previous studies. In this paper, we propose DegreEmbed, a model that combines embedding-based learning and logic rule mining for inferring on KGs. Specifically, we study the problem of predicting missing links in heterogeneous KGs from the perspective of the degree of nodes. Experimentally, we demonstrate that our DegreEmbed model outperforms the state-of-the-art methods on real world datasets and the rules mined by our model are of high quality and interpretability.

Artificial intelligence research tried and removed many of the different approaches since its founding, including simulating the brain, modeling human problem solving, learning, formal logic, large databases of knowledge and imitating animal behavior. Artificial intelligence in pharmaceutical industry shows no sign of slowing down. According to recent research, about 50% of global healthcare companies plan to implement artificial intelligence strategies broadly adopt the technology by 2025.

In the era of globalization, traditional retail stores face increasingly complex challenges, especially with the emergence of technology and changes in consumer behavior. One important aspect that traditional retail shop owners need to pay attention to is technical skills, human resource development and problem solving which aims to improve technical skills in the context of traditional retail shops which is the focus of this research. As technology advances, physical retail stores must understand and integrate various aspects of technology to ensure smooth operations and remain competitive in an ever-changing marketplace. SS Stores require increased technical skills, human resource development and problem solving in running their business. The method in this activity uses the Participatory Action Research (PAR) method which aims to determine the systematic process or steps used as a means to achieve certain goals effectively. The importance of developing technical skills, managing human resources (HR), and problem solving abilities is the key to success, therefore every retail business is certainly obliged to improve technical skills, develop human resources and solve problems in business.

We study human performance in two classical NP-hard optimization problems: Set Cover and Maximum Coverage. We suggest that Set Cover and Max Coverage are related to means selection problems that arise in human problem-solving and in pursuing multiple goals: The relationship between goals and means is expressed as a bipartite graph where edges between means and goals indicate which means can be used to achieve which goals. While these problems are believed to be computationally intractable in general, they become more tractable when the structure of the network resembles a tree. Thus, our main prediction is that people should perform better with goal systems that are more tree-like. We report three behavioral experiments which confirm this prediction. Our results suggest that combinatorial parameters that are instrumental to algorithm design can also be useful for understanding when and why people struggle to choose between multiple means to achieve multiple goals.

Materials today provide enormous prospects for innovation. However, progress is only possible if a mechanism for making a sensible choice from the materials exists. When choosing a material for a component, one should start with the full menu of materials as an option. When selecting a material for an engineering component, there is usually more than one option. However, the final choice will be a balance between the materials based on the benefits and drawbacks they provide. This paper focuses on material selection for racing bicycle forks utilizing knowledge-based expert systems based on design requirements. Knowledge-based expert systems are computer methods replicating human problem-solving through artificial intelligence to arrive at the best decisive action. A database of 67 materials with its own set of attributes was created. The material index and shape factor were evaluated for all the materials in the database based on the critical selection factors of mass and section shape. Material selection is determined by considering the material index, shape factor, and coupling various design requirements by framing simple IF-THEN rules using the python interface and Jupyter Notebook. According to this method, the final suitable materials for selecting racing bicycle forks are Aluminium alloys and Carbon Fiber Reinforced Plastics (CFRP). As the application demands lower weight by compromising on the cost, Carbon Fiber Reinforced Plastic (CFRP) is the most preferred choice for material selection of racing bicycle forks.

Abstract The evolution of Artificial Intelligence (AI) and Machine Learning (ML) enables new ways to envision how computer tools will aid, work with, and even guide human teams. This paper explores this new paradigm of design by considering emerging variations of AI-Human collaboration: AI used as a design tool versus AI employed as a guide to human problem solvers, and AI agents which only react to their human counterparts versus AI agents which proactively identify and address needs. The different combinations can be mapped onto a 2×2 AI-Human Teaming Matrix which isolates and highlights these different AI capabilities in teaming. The paper introduces the matrix and its quadrants, illustrating these different AI agents and their application and impact, and then provides a road map to researching and developing effective AI team collaborators.

The field of actuarial science uses mathematical and statistical techniques to evaluate financial risks in the insurance and finance industries. Actuaries are creative, curious and adaptable human resources and problem solvers, who need to possess different skills and knowledge to solve risk related problems. Partly Qualified Actuaries (PQA) is semi-professionals actuary. From analyzing the financial costs of risk and uncertainty to pricing and reserving, partly qualified actuaries are important in doing the basic to advanced actuarial tasks in these businesses. The research paper investigates on the role of partly qualified actuaries in the insurance industry in Nepal and analyzes the factors impacting the role of PQA in Nepalese insurance companies. The objective of this study is to look into the scenario and important role of Partly Qualified Actuary in the insurance companies. Descriptive and analytical research design was used in this study. The data collected through questionnaire from100 respondents was used for analysis from PQA, insurance companies' staffs, policy makers and some academicians of Nepal who are well informed about actuary. The research shows that the number of PQA is increasing in Nepal every year and they are working in different insurance companies.

Despite great strides in both machine learning and neuroscience, we do not know how the human brain solves problems in the general sense. We approach this question by drawing on the framework of engineering control theory. We demonstrate a computational neural model with only localist learning laws that is able to find solutions to arbitrary problems. The model and humans perform a multi-step task with arbitrary and changing starting and desired ending states. Using a combination of computational neural modeling, human fMRI, and representational similarity analysis, we show here that the roles of a number of brain regions can be reinterpreted as interacting mechanisms of a control theoretic system. The results suggest a new set of functional perspectives on the orbitofrontal cortex, hippocampus, basal ganglia, anterior temporal lobe, lateral prefrontal cortex, and visual cortex, as well as a new path toward artificial general intelligence.

The human capacity for abstraction is remarkable. We effortlessly form abstract representations from varied experiences, generalizing and flexibly transferring experiences and knowledge between contexts, which can facilitate reasoning, problem solving and learning across many domains. The cognitive process of abstraction, however, is often portrayed and investigated as an individual process. This paper addresses how cognitive processes of abstraction-together with other aspects of human reasoning and problem solving-are fundamentally shaped and modulated by online social interaction. Starting from a general distinction between convergent thinking, divergent thinking and processes of abstraction, we address how social interaction shapes information processing differently depending on cognitive demands, social coordination and task ecologies. In particular, we suggest that processes of abstraction are facilitated by the interactive sharing and integration of varied individual experiences. To this end, we also discuss how the dynamics of group interactions vary as a function of group composition; that is, in terms of the similarity and diversity between the group members. We conclude by outlining the role of cognitive diversity in interactive processes and consider the importance of group diversity in processes of abstraction. This article is part of the theme issue 'Concepts in interaction: social engagement and inner experiences'.