Human Problem Solving Research Articles

HUMAN RESOURCE MANAGEMENT AND ARTIFICIAL INTELLIGENCE: TRANSFORMATIVE EFFECTS AND FUTURE PROSPECTS

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.

Efficiency Divide: Comparative Analysis of Human & Neural Network Algorithm Development

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.

Adaptation Through Communication: Assessing Human–Artificial Intelligence Partnership for the Design of Complex Engineering Systems

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.

DegreEmbed: Incorporating entity embedding into logic rule learning for knowledge graph reasoning

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 in Pharmacy

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.

Peningkatan Technical Skill, Pengembangan SDM dan Problem Solving Pada Toko SS

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.

The Computational Challenges of Means Selection Problems: Network Structure of Goal Systems Predicts Human Performance.

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.

Material selection using knowledge-based expert system for racing bicycle forks

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.

FOCUS AND MODALITY: DEFINING A ROADMAP TO FUTURE AI-HUMAN TEAMING IN DESIGN

AbstractThe 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.

Roles and Scenario of Partly Qualified Actuaries in Insurance Companies of Nepal

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.

Foundations of human spatial problem solving

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.

On the social nature of abstraction: cognitive implications of interaction and diversity.

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'.

Artificial Intelligence in the Management of Rotator Cuff Tears

Technological innovation is a key component of orthopedic surgery. Artificial intelligence (AI), which describes the ability of computers to process massive data and "learn" from it to produce outputs that mirror human cognition and problem solving, may become an important tool for orthopedic surgeons in the future. AI may be able to improve decision making, both clinically and surgically, via integrating additional data-driven problem solving into practice. The aim of this article will be to review the current applications of AI in the management of rotator cuff tears. The article will discuss various stages of the clinical course: predictive models and prognosis, diagnosis, intraoperative applications, and postoperative care and rehabilitation. Throughout the article, which is a review in terms of study design, we will introduce the concept of AI in rotator cuff tears and provide examples of how these tools can impact clinical practice and patient care. Though many advancements in AI have been made regarding evaluating rotator cuff tears-particularly in the realm of diagnostic imaging-further advancements are required before they become a regular facet of daily clinical practice.

Reconstructing the Einstellung Effect

The Einstellung effect was first described by Abraham Luchins in his doctoral thesis published in 1942. The effect occurs when a repeated solution to old problems is applied to a new problem even though a more appropriate response is available. In Luchins’ so-called water jar task, participants had to measure a specific amount of water using three jars of different capacities. Luchins found that subjects kept using methods they had applied in previous trials, even if a more efficient solution for the current trial was available: an Einstellung effect. Moreover, Luchins studied the different conditions that could possibly mediate this effect, including telling participants to pay more attention, changing the number of tasks, alternating between different types of tasks, as well as putting participants under time pressure. In the current work, we reconstruct and reanalyze the data of the various experimental conditions published in Luchins’ thesis. We furthermore show that a model of resource-rational decision-making can explain all of the observed effects. This model assumes that people transform prior preferences into a posterior policy to maximize rewards under time constraints. Taken together, our reconstructive and modeling results put the Einstellung effect under the lens of modern-day psychology and show how resource-rational models can explain effects that have historically been seen as deficiencies of human problem-solving.

A Bayesian Hierarchical Model for Extracting Individuals’ Theory-Based Causal Knowledge

Abstract Extracting an individual’s scientific knowledge is essential for improving educational assessment and understanding cognitive tasks in engineering activities such as reasoning and decision-making. However, knowledge extraction is an almost impossible endeavor if the domain of knowledge and the available observational data are unrestricted. The objective of this paper is to quantify individuals’ theory-based causal knowledge from their responses to given questions. Our approach uses directed-acyclic graphs (DAGs) to represent causal knowledge for a given theory and a graph-based logistic model that maps individuals’ question-specific subgraphs to question responses. We follow a hierarchical Bayesian approach to estimate individuals’ DAGs from observations. The method is illustrated using 205 engineering students’ responses to questions on fatigue analysis in mechanical parts. In our results, we demonstrate how the developed methodology provides estimates of population-level DAG and DAGs for individual students. This dual representation is essential for remediation since it allows us to identify parts of a theory that a population or individual struggles with and parts they have already mastered. An addendum of the method is that it enables predictions about individuals’ responses to new questions based on the inferred individual-specific DAGs. The latter has implications for the descriptive modeling of human problem-solving, a critical ingredient in sociotechnical systems modeling.

Incorporation of prior knowledge and habits while solving anagrams.

Games and puzzles provide a valuable context for examining human problemsolving behavior. We recorded and analyzed the sequence of letters viewed by the participants of our study while they were solving anagram puzzles. The goal was to examine and understand how people's linguistic habits and prior knowledge influenced their eye movements. The main findings of this study are: (1) People's stereotypical habit of scanning (e.g., adjacent or top viewing) strongly influences their solution-seeking behavior. (2) People tend to incorporate their prior knowledge of letter statistics in a reasonable way, such as looking less frequently at letter combinations that are uncommon in the English language.

Human error in complex problem solving and dynamic decision making: A taxonomy of 24 errors and a theory

The current study extends existing research on human error by investigating human error in complex, dynamic, and uncertain microworlds. The main goal was to develop a taxonomy of errors for such situations. Several tasks with differing characteristics and demands on the problem solver were used: the simulation of a chocolate producing company (CHOCO FINE), the simulation of a tribe of semi-nomads in the Sahel zone (MORO), and the simulation of wildfires engaging fire-fighting units (WINFIRE). Observing participants and teams working on these simulations, we refer to specific cases and describe 24 errors related to the steps of human problem solving and dynamic decision making. A theory is then presented which attempts to explain the causes of these errors occurring in microworld contexts. Whereas most existing theories focus on cognitive explanations, the theory presented explains human error as a result of the interaction of emotions, motivations, and cognition. The current study not only extends the taxonomy and list of human errors, but especially addresses the lack of theoretical explanations in the literature on human error. The findings provide a starting point for further theory development and empirical testing and could be applied in training programs; hopefully contributing to sensitivity and awareness of the demands of complex, dynamic, and uncertain problems, and ultimately contributing to fewer human errors.

Engineering design optimisation using reinforcement learning with episodic controllers

Engineers solving engineering design problems can be regarded as a gradual optimisation process that involves strategising. The process can be modelled as a reinforcement learning (RL) framework. This article presents an RL model with episodic controllers to solve engineering problems. Episodic controllers provide a mechanism for using the short-term and long-term memories to improve the efficiency of searching for engineering problem solutions. This work demonstrates that the two kinds of models of memories can be incorporated into the existing RL framework. Finally, an optimised design problem of a crane girder is illustrated by RL with episodic controllers. The work presented in this study leverages the RL model that has been shown to mimic human problem solving in engineering optimised design problems.

The third AI summer: AAAI Robert S. Engelmore Memorial Lecture

The third AI summer: AAAI Robert S. Engelmore Memorial Lecture

Affordance Based Framework of Human Problem Solving: a Nonrepresentational Alternative

Problem solving is a crucial higher-order thinking ability of humans. Humans' ability to solve problems is a critical higher-order thinking ability. Mathematical problem solving, analogical problem solving, complex problem solving, situated problem solving, and so on are all examples of problem solving. Furthermore, distinct types of research analysis, models, and theories are based on the mechanisms and elements involved in diverse problem-solving types. The conventional approach to understanding human problem solving is a representation-laden description, which is similar to most cognitive explanations of psychological processes. On the other hand, the paper goes beyond representational theories and models to investigate nonrepresentational and situated aspects of human problem solving. Problem solving is a crucial higher-order thinking ability of humans. The paper is a rudimentary attempt to present a nonrepresentational, Affordance-Situation-Attunement (ASA) framework of human problem solving. The aim is to invoke ASA as an alternative framework, in contrast with the dominant representational explanation of human problem solving. The aim is not to disparage the representational theories and models of problem solving but to contribute a nonrepresentational working framework and elements for highlighting the situated nature of human problem solving. Keywords: Problem solving, affordances, embodied cognition, situated cognition, ecological psychology