Rule-based Task Research Articles

Neural representational geometries reflect behavioral differences in monkeys and recurrent neural networks

Animals likely use a variety of strategies to solve laboratory tasks. Traditionally, combined analysis of behavioral and neural recording data across subjects employing different strategies may obscure important signals and give confusing results. Hence, it is essential to develop techniques that can infer strategy at the single-subject level. We analyzed an experiment in which two male monkeys performed a visually cued rule-based task. The analysis of their performance shows no indication that they used a different strategy. However, when we examined the geometry of stimulus representations in the state space of the neural activities recorded in dorsolateral prefrontal cortex, we found striking differences between the two monkeys. Our purely neural results induced us to reanalyze the behavior. The new analysis showed that the differences in representational geometry are associated with differences in the reaction times, revealing behavioral differences we were unaware of. All these analyses suggest that the monkeys are using different strategies. Finally, using recurrent neural network models trained to perform the same task, we show that these strategies correlate with the amount of training, suggesting a possible explanation for the observed neural and behavioral differences.

Robotic Process Automation in Banking for Better Customer Experience

Robotic Process Automation (RPA) has emerged as a transformative technology in the banking sector, offering opportunities to streamline operations, reduce costs, and enhance customer experiences. This explores the impact of RPA implementation on customer experience within the banking industry. By automating repetitive, rule-based tasks such as account maintenance, loan processing, and customer inquiries, banks can significantly improve operational efficiency and accuracy while freeing up human resources to focus on higher-value activities. Furthermore, we discuss the challenges and considerations associated with RPA adoption in banking, including data security, regulatory compliance, and employee upskilling. Ultimately, this underscores the pivotal role of RPA in reshaping the banking landscape, driving innovation, and delivering superior customer experiences in an increasingly digital world.

Dissociable roles of the dorsolateral and ventromedial prefrontal cortex in human categorization.

Models of human categorization predict the prefrontal cortex (PFC) serves a central role in category learning. The dorsolateral prefrontal cortex (dlPFC) and ventromedial prefrontal cortex (vmPFC) have been implicated in categorization; however, it is unclear whether both are critical for categorization and whether they support unique functions. We administered three categorization tasks to patients with PFC lesions (mean age = 69.6 years; 5 men, 5 women) to examine how prefrontal subregions contribute to categorization. These included a rule-based (RB) task that was solved via a unidimensional rule, an information integration (II) task that was solved by combining information from two stimulus dimensions, and a deterministic/probabilistic (DP) task with stimulus features that had varying amounts of category-predictive information. Compared to healthy comparison participants, both patient groups had impaired performance. Impairments in the dlPFC patients were largest during the RB task, whereas impairments in the vmPFC patients were largest during the DP task. A hierarchical model was fit to the participants' data to assess learning deficits in the patient groups. PFC damage was correlated with a regularization term that limited updates to attention after each trial. Our results suggest that the PFC, as a whole, is important for learning to orient attention to relevant stimulus information. The dlPFC may be especially important for rule-based learning, whereas the vmPFC may be important for focusing attention on deterministic (highly diagnostic) features and ignoring less predictive features. These results support overarching functions of the dlPFC in executive functioning and the vmPFC in value-based decision making.Significance Statement Category learning creates flexible memory representations that easily generalize to novel situations. Although it is generally established that the prefrontal cortex is central to categorization, it is unclear how different prefrontal subregions contribute to learning. Separate literatures have implicated both the dorsolateral prefrontal cortex (dlPFC) and the ventromedial prefrontal cortex (vmPFC) in categorization, but there has been little effort to bridge these literatures. The current study is the first to examine categorization in patients with lesions centered in the dlPFC and vmPFC. We found that, as a whole, the PFC orients attention to relevant stimulus information. The dlPFC is important for rule-based learning, whereas the vmPFC is important for focusing attention on highly diagnostic features and ignoring less predictive features.

Relevance of RPA in Banking & CRM

This journal explores the relevance of Artificial Intelligence (AI) in the field of banking and Customer Relationship Management (CRM). It delves into how AI technologies are reshaping traditional banking operations and enhancing customer experiences and optimizing CRM strategies. In banking, AI is being used to improve customer services, strengthen security, and provide personalized financial services. AI technologies, such as machine learning, natural language processing, and predictive analytics, have revolutionized various aspects of banking operations. These technologies delegates’ banks to automate routine tasks enhance fraud detection capabilities, personalized customer experiences. The study also highlights the challenges and opportunities associated with AI adoption in CRM strategies, so here we are using Robotic Process Automation (RPA) concepts to automate repetitive and rule-based tasks in banking and CRM processes. RPA is reshaping business processes, workforce dynamics, and strategic decision making. Additionally, it discusses key considerations for successful RPA implementations. Ultimately, this paper highlights the importance of understanding and harnessing the power of RPA to drive innovation and competitiveness in the digital age.

Revolutionizing Finance: Insights on the Impact of Automation

Abstract This article delves into the transformative impact of integrating SAP and Robotic Process Automation (RPA) in the realm of Finance. As organizations strive for greater efficiency, accuracy, and agility in their financial operations, solutions such as SAP and RPA emerge as a powerful solution. The article provides an in-depth analysis of how these technologies complement each other, offering a set of different perspectives provided by 13 Subject Matter Experts during an interview. The study begins by outlining the importance of using Artificial Intelligence in Finance, with a focus on the demonstrated benefits and potential areas that are suitable for this technology. It is also highlighted what Financial areas could be considered candidates for automation of processes, with a focus on five main topics. Subsequently, the article investigates the role of Robotic Process Automation and the key features of SAP, in automating repetitive, rule-based tasks within financial processes. Furthermore, through real-world case studies and examples provided by people with work experience in this field, the article provides insights into the practical applications of SAP and RPA, witnessing the improvements in operational efficiency, data accuracy, and compliance. The discussion explores the evolving landscape of finance in the face of digital transformation, emphasizing the following topics: understanding the need of automation, prerequisites needed in Financial processes, tools required, data security, and future prospects.

Latest Innovation in Robotics

Robotics is the science of creating and assembling tangible robots to enhance automation and creativity. Engineering and computer science are combined in the field of robotics, which deals with the creation, manufacturing, and use of robots. Robotics is undergoing a fast evolution with ground-breaking discoveries that are changing entire sectors and societal contexts. Recent advances in AI have accelerated robotics' progress toward more flexibility and autonomy. There are many different types of robotics. A robot could be an artificial intelligence (AI) device that looks like a person, or it could be a robotic application like robotic process automation, which resembles how people interact with software to carry out repetitive, rule-based tasks. The continued convergence of robotics with artificial intelligence, materials science, and other interdisciplinary domains holds the potential to open up new avenues for automation and human-robot cooperation. This study offers an overview of the most recent advancements in robotics, including significant technological innovations, cutting-edge applications, and developing trends

ChatGPT vs Chatbots Unleashing the Power of Conversational AI

This article explores the distinctions between ChatGPT and chatbots, two prominent technologies in the field of conversational AI. ChatGPT, powered by OpenAI's GPT-3.5 architecture, represents a state-of-the-art natural language processing model that learns from vast text data, enabling it to generate contextually relevant responses. In contrast, chatbots operate within predefined rules and offer structured interactions. The article discusses the strengths and weaknesses of each approach and their respective applications. While ChatGPT excels in natural language understanding and adaptability, chatbots are well-suited for rule-based tasks. The future of conversational AI may involve hybrid systems that combine these strengths. Ethical considerations surrounding bias and privacy in AI conversations are also addressed. Ultimately, the choice between ChatGPT and chatbots depends on specific needs and goals, but both technologies play a pivotal role in shaping the future of human-computer interactions

Security Automation in Application Development Using Robotic Process Automation (RPA)

In the contemporary digital landscape, the rapid advancement of application development necessitates a parallel evolution in security measures. Traditional security practices often struggle to keep pace with the dynamic nature of modern software development. To address these challenges, Robotic Process Automation (RPA) emerges as a transformative tool, offering substantial potential to automate security tasks within the application development lifecycle. This paper explores the integration of RPA into security automation, focusing on its capabilities to enhance security protocols, minimize human error, and streamline processes across various stages of development. Security automation through RPA involves the deployment of software robots to perform repetitive, rule-based tasks that are essential to maintaining and enforcing security standards. These tasks include automated code scanning, vulnerability assessments, compliance checks, and incident response, all of which are crucial to the development and deployment of secure applications. By automating these processes, organizations can significantly reduce the time and resources required to secure applications, ensuring that security measures are not compromised by the speed and agility demanded by modern development practices.

Prototyping and implementing Robotic Process Automation in accounting firms: Benefits, challenges and opportunities to audit automation

Despite the advancements in technology, auditors still spend a significant amount of time performing repetitive and rule-based tasks. Our paper examines various audit scenarios within four accounting firms and discusses the potential for robotic process automation (RPA) to improve the efficiency and accuracy of these tasks. We propose and implement RPA-enabled solutions to make various procedures more efficient and effective. These anecdotes were based on our consultancy experiences with auditors from a group of Big 4 and mid-sized accounting firms. We present four practical business process scenarios and the prototyped RPA solutions. In each scenario, we explain our automation strategies and the duration of development. We also describe the implementation benefits and challenges, as well as analyze how we have helped the accounting firms automate their real-world audit tasks by providing the as-is and to-be Business Process Model and Notation that one can extend for future RPA development. Finally, we discuss the future challenges and opportunities regarding the broader implementation of RPA in accounting and audit tasks.

Robotic process automation deployments: a step-by-step method to investment appraisal

PurposeRobotic process automation (RPA) has recently emerged as a technology focusing on the automation of repetitive, frequent, voluminous and rule-based tasks. Despite a few practical examples that document successful RPA deployments in organizations, evidence of its economic benefits has been mostly anecdotal. The purpose of this paper is to present a step-by-step method to RPA investment appraisal and a business case demonstrating how the steps can be applied to practice.Design/methodology/approachThe methodology relies on design science research (DSR). The step-by-step method is a design artefact that builds on the mapping of processes and modelling of the associated costs. Due to the longitudinal nature of capital investments, modelling uses discounted cashflow and present value methods. Empirical grounding characteristic to DSR is achieved by field testing the artefact.FindingsThe step-by-step method is comprised of a preparatory step, three modelling steps and a concluding step. The modelling consists of compounding the interest rate, discounting the investment costs and establishing measures for comparison. These steps were applied to seven business processes to be automated by the case company, Estate Blend. The decision to deploy RPA was found to be trivial, not only based on the initial case data, but also based on multiple sensitivity analyses that showed how resistant RPA investments are to changing circumstances.Practical implicationsBy following the provided step-by-step method, executives and managers can quantify the costs and benefits of RPA. The developed method enables any organization to directly compare investment alternatives against each other and against the probable status quo where many tasks in organizations are still carried out manually with little to no automation.Originality/valueThe paper addresses a growing new domain in the field of business process management by capitalizing on DSR and modelling-based approaches to RPA investment appraisal.

A method for selecting processes for automation with AHP and TOPSIS

Organizations are more frequently turning towards robotic process automation (RPA) as a solution for employees to focus on higher complexity and more valuable tasks while delegating routine, monotonous and rule-based tasks to their digital colleagues. These software robots can handle various rule-based, digital, repetitive tasks. However, currently available process identification methods must be qualified to select suitable automation processes accurately. Wrong process selection and failed attempts are often the origin of process automation's bad reputation within organizations and often result in the avoidance of this technology. As a result, in this research, a method for selecting processes for automation combining two multi-criteria decision-making techniques, 'Analytic Hierarchy Process (AHP) and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), will be proposed, demonstrated, and evaluated. This study follows the Design Science Research Methodology (DSRM) and applies the proposed method for selecting processes for automation to a real-life scenario. The result will be a method to support the proper selection of business processes for automation, increasing the success of implementing RPA tools in an organization.

Using diverging predictions from classical and quantum models to dissociate between categorization systems

Quantum probability theory has successfully provided accurate descriptions of behavior in the areas of judgment and decision making, and here we apply the same principles to two category learning tasks, one task using information-integration categories and the other using rule-based categories. Since information-integration categories lack verbalizable descriptions, unlike rule-based ones, we assert that an information-integration categorization decision results from an intuitive probabilistic reasoning system characterized by quantum probability theory, whereas a rule-based categorization decision results from a logical, rational probabilistic reasoning system characterized classical probability theory. In our experiment, participants learn to categorize simple, visual stimuli as members of either category S or category K during an acquisition phase, and then rate the likelihood on a scale of 0 to 5 that a stimulus belongs to one category and subsequently perform the same likelihood rating for the other category during a transfer phase. Following the principle of complementarity in quantum theory, we expect the category likelihood ratings to exhibit order effects in the information-integration task, but not in the rule-based task. In the information-integration task, we found definitive order effects in the likelihood ratings. But, in the rule-based task, we found that the order effects in the likelihood ratings are not significant.

Robotic Process Automation with Selenium

RPA is defined as the practice of using software robots to interface with Software-as-a-Service applications and IT systems in order to automate rule-based manual tasks related to repetitive and transactional procedures. The robot mimics a user's behaviors while interacting with a system's user interface. RPA services allow data security, improved business efficiency, and effectiveness across a number of business applications without changing the infrastructure or systems that are already in place. Robotic process automation (RPA) is a form of technology utilized in areas including artificial intelligence, machine learning, and automation engineering. Instead of using the most valuable human resources on repetitive activities, it might be considered a low-risk approach to automating business operations.

Robotic Process Automation (RPA) Adoption: A Systematic Literature Review

Abstract Robotic process automation (RPA) is a recent technology that has recently become increasingly adopted by companies as a solution for employees to focus on higher complexity and more valuable tasks while delegating routine, monotonous and rule-based tasks to their digital colleagues. The increased interest, reflected in the increasing number of articles regarding approaches and test cases, has triggered the necessity for a summary that could extract the more generalisable ideas and concepts about these software robots. This paper used a Systematic Literature Review (SLR) approach to find and synthesise information from articles obtained on this subject. This research identified the most general implementation approaches of successful RPA adoption cases, observed benefits, challenges commonly faced by organisations, characteristics that make processes more suitable for RPA, and research gaps in the current literature. The findings presented in this paper have two purposes. The first is to provide a way for companies and organisations to become more familiar with good practices regarding the adoption of robotic process automation. The second is to foster further research on the subject by complementing the current knowledge and proposing new paths for research.

Sex-related differences in visuomotor skill recovery following concussion in working-aged adults

BackgroundThe ability to perform visually-guided motor tasks requires the transformation of visual information into programmed motor outputs. When the guiding visual information does not align spatially with the motor output, the brain processes rules to integrate somatosensory information into an appropriate motor response. Performance on such rule-based, “cognitive-motor integration” tasks is affected in concussion. Here, we investigate the relationship between visuomotor skill performance, concussion history, and sex during the course of a post-concussion management program.MethodsFifteen acutely concussed working-aged adults, 11 adults with a history of concussion, and 17 healthy controls all completed a recovery program over the course of 4 weeks. Prior to, mid-way, and following the program, all participants were tested on their visuomotor skills.ResultsWe observed an overall change in visuomotor behaviour in all groups, as participants completed the tasks faster and more accurately. Specifically, we observed significant visuomotor skill improvement between the first and final sessions in participants with a concussion history compared to no-concussion-history controls. Notably, we observed a stronger recovery of these skills in females.ConclusionsOur findings indicate that (1) concussion impairs visuomotor skill performance, (2) the performance of complex, rule-based tasks showed improvement over the course of a recovery program, and (3) stronger recovery in females suggests sex-related differences in the brain networks controlling skilled performance, and the effect of injury on these networks.

Selective effects of psychosocial stress on plan based movement selection

Efficient movement selection is crucial in everyday activities. Whether this function is governed by our stress system is so far unknown. In the current study, data from thirty-six young male adults were analyzed. They performed rule- and plan-based movement selection tasks before (session 1) and after (session 2) a psychosocial stressor, or after a control condition without additional social stressor. Results showed that the rule-based efficiency advantage which was observed prior to the psychosocial stressor was significantly reduced afterwards in the whole sample, as well as in the stress group. Regression analyses revealed that this effect was due to a modulation of the plan-based approach. Especially variations—both increase and decrease—in the parasympathetic activity (reflected by the heart rate variability measure RMSSD) appeared to be disadvantageous for plan-based movement selection improvement. In contrast, performance in the rule-based movement selection tasks appeared to be rather invariant to external influences. The current results suggest that autonomic nervous system activity might modulate motor-cognitive performance. This modulatory capability might be selective for plan-based approaches, hence the applied strategy to movement selection could be decisive when it comes to the vulnerability of motor-cognitive processes towards psychosocial stress.

The Lords of the Rings: People and pigeons take different paths mastering the concentric-rings categorization task

COVIS (COmpetition between Verbal and Implicit Systems; Ashby, Alfonso-Reese, & Waldron, 1998) is a prominent model of categorization which hypothesizes that humans have two independent categorization systems – one declarative, one associative – that can be recruited to solve category learning tasks. To date, most COVIS-related research has focused on just two experimental tasks: linear rule-based (RB) tasks, which purportedly encourage declarative rule use, and linear information-integration (II) tasks, which purportedly require associative learning mechanisms. We introduce and investigate a novel alternative: the concentric-rings task, a nonlinear category structure that both humans and pigeons can successfully learn and transfer to untrained exemplars. Yet, despite their broad behavioral similarities, humans and pigeons achieve their successful learning through decidedly different means. As predicted by COVIS, pigeons appear to rely solely on associative learning mechanisms, whereas humans appear to initially test but subsequently reject unidimensional rules. We discuss how variants of our concentric-rings task might yield further insights into which category-learning mechanisms are shared across species, as well as how categorization strategies might change throughout training.

RPA Vendor Evaluation and Selection using AHP and Kano Model

Robotic Process Automation (RPA) as a technology has gained momentum due to the onset of Industry 4.0. RPA is considered to be the simplest forms of automation in which, typically, the human actions are mimicked without taking into account the complex judgements associated with it. Hence, RPA is deemed fit for the rule-based tasks which are highly repetitive, bulky and are error-prone. Traditionally, organizations looking to increase its operations efficiency improve workforce utilisation, turn to RPA. With the shift in working pattern enforced by COVID-19, RPA adoption is no more a luxury, but a necessity. In a short span of time, the number of RPA vendors, that is the developers and providers of RPA software have significantly increased and due to current demands of RPA, it is expected to grow further. This increase in choices makes the vendor selection part of any RPA Implementation Project highly complex and confusing. Keeping in mind the significance of the vendor selection process, various methods of RPA Vendor Selection have been previously proposed but it is still evolving along with the changing needs of the Businesses. This Research paper aims to propose a standard model using Kano Model and Analytic Hierarch Process (AHP) such that it can be customized during each new RPA implementation project without compromising on the basic structure and method for evaluation.

Pigeons exhibit flexibility but not rule formation in dimensional learning, stimulus generalization, and task switching.

A prominent model of categorization (Ashby, Alfonso-Reese, Turken, & Waldron, 1998) posits that 2 separate mechanisms-one declarative, one associative-can be recruited in category learning. These 2 systems can effectively be distinguished by 2 task structures: rule-based (RB) tasks are unidimensional and encourage analytic processing, whereas information-integration (II) tasks are bidimensional and encourage nonanalytic associative learning. Humans and nonhuman primates have been reported to learn RB tasks more quickly than II tasks; however, pigeons and rats have shown no learning speed differences are thus believed to lack the declarative system. In the present trio of experiments, we further explored pigeons' dimensional category learning. We replicated the finding that pigeons learn RB and II tasks at equal speeds. Further, we found that stimulus generalization performance was equivalent on both tasks. We also explored the effect of switching from one task to another. Task switches between phases of training as well as within individual training sessions posed little difficulty for pigeons; they quickly and flexibly switched their categorization responses with no cost in choice speed or accuracy. Together, our data indicate that, although pigeons may lack the capacity to form explicit dimensional rules, their associative learning system is both powerful and flexible. Further exploration of this associative system would help us better appreciate possible contributions of the declarative system. (PsycInfo Database Record (c) 2020 APA, all rights reserved).

High Cognitive Flexibility Learners Perform Better in Probabilistic Rule Learning.

Cognitive flexibility reflects the ability to switch quickly between tasks or stimulus sets, which is an important feature of human intelligence. Researchers have confirmed that this ability is related to the learners’ academic achievement, cognitive ability, and creativity development. The number-letter switching task is an effective tool for measuring cognitive flexibility. Previous studies have found that high flexibility individuals perform better in rule-based tasks such as the Iowa Gambling Task. It is not clear whether highly flexible learners have learning advantages when the rule tasks involve probabilistic cues. Using an inter-individual differences approach, we examined whether cognitive flexibility, as assessed by the number-letter task, is associated with the learning process of a probabilistic rule task. The results showed that the high flexibility group reached a higher level of rule acquisition, and the accuracy during the post-learning stage was significantly higher than the low flexibility group. These findings demonstrate that cognitive flexibility is associated with the performance after the rule acquisition during the probabilistic rule task. Future research should explore the internal process of learning differences between high and low flexibility learners by using other technologies across multiple modes.