Agent-based Computer Research Articles

Agent-Based simulation reveals localized isolation key to saving lives and resources

In the realm of pandemic dynamics, understanding the intricate interplay between disease transmission, interventions, and immunity is pivotal for effective control strategies. Through a rigorous agent-based computer simulation, we embarked on a comprehensive exploration, traversing unmitigated spread, lockdown scenarios, and the transformative potential of vaccination. we unveil that while quarantine unquestionably delays the pandemic peak, it does not act as an impenetrable barrier to halt the progression of infectious diseases. Vaccination factor revealed a potent weapon against outbreaks — higher vaccination percentage not only delayed infection peaks but also substantially curtailed their impact. Our investigation into bond dilution below the percolation threshold presents an additional dimension to pandemic control. We observed that localized isolation through bond dilution offers a more resource-efficient targeted control strategy than blanket lockdowns or large-scale vaccination campaigns.

Diverse morphology and motility induced emergent order in bacterial collectives.

Microbial communities exhibit complex behaviors driven by species interactions and individual characteristics. In this study, we delve into the dynamics of a mixed bacterial population comprising two distinct species with different morphology and motility aspects. Employing agent-based modeling and computer simulations, we analyze the impacts of size ratios and packing fractions on dispersal patterns, aggregate formation, clustering, and spatial ordering. Notably, we find that motility and anisotropy of elongated bacteria significantly influence the distribution and spatial organization of nonmotile spherical species. Passive spherical cells display a superdiffusive behavior, particularly at larger size ratios in the ballistic regime. As the size ratio increases, clustering of passive cells is observed, accompanied by enhanced alignment and closer packing of active cells in the presence of higher passive cell area fractions. In addition, we identify the pivotal role of passive cell area fraction in influencing the response of active cells toward nematicity, with its dependence on size ratio. These findings shed light on the significance of morphology and motility in shaping the collective behavior of microbial communities, providing valuable insights into complex microbial behaviors with implications for ecology, biotechnology, and bioengineering.

Highly idealized models of scientific inquiry as conceptual systems

The social epistemology of science has adopted agent-based computer simulations as one of its core methods for investigating the dynamics of scientific inquiry. The epistemic status of these highly idealized models is currently under active debate in which they are often associated either with predictive or the argumentative functions. These two functions roughly correspond to interpreting simulations as virtual experiments or formalized thought experiments, respectively. This paper advances the argumentative account of modeling by proposing that models serve as a means to (re)conceptualize the macro-level dynamics of complex social epistemic interactions. I apply results from the epistemology of scientific modeling and the psychology of mental simulation to the ongoing debate in the social epistemology of science. Instead of considering simulation models as predictive devices, I view them as artifacts that exemplify abstract hypothetical properties of complex social epistemic processes in order to advance scientific understanding, hypothesis formation, and communication. Models need not be accurate representations to serve these purposes. They should be regarded as pragmatic cognitive tools that engender rather than replace intuitions in philosophical reasoning and argumentation. Furthermore, I aim to explain why the community tends to converge around few model templates: Since models have the potential to transform our intuitive comprehension of the subject of inquiry, successful models may literally capture the imagination of the modeling community.

How to Reduce the Number of Inspectors: Operation Mode and Modeling Simulation of Centralized Image Recognition in Railway Passenger Security Inspection

Security inspection is an essential step for passengers entering railway stations in China. Under the existing security inspection operation mode, each X-ray machine needs an image inspector to recognize the X-ray image, which poses the issue of resource optimization for inspectors. Based on the networking of security inspection X-ray machines, an innovative centralized image recognition (CIR) operation mode for railway security inspections is proposed, and it is worth noting that network and device failures are risk factors for this mode. The method for assigning image recognition personnel is designed to reduce labor force needed for security inspection. Using a combination of agent-based modeling method and computer simulation technology, the four elements of passengers, security inspection X-ray machines, X-ray image inspectors, and secondary baggage inspection personnel are modeled, and CIR indicators to study the effectiveness of the operation are designed. Based on the real departing passenger data of the Zhangjiakou section of the Beijing Zhangjiakou Line in China, simulation is conducted in two scenarios: large passenger flow and small passenger flow. The research is conducted from three aspects: operational efficiency, travel experience, and system complexity. Experiments have shown that, with a slight increase in passenger waiting time, the average number of X-ray image inspectors is reduced by more than 70% compared with the existing operation mode. The best pick-up location for passengers is related to passenger flow. The proposed model can analyze the effectiveness of CIR operation mode and provide decisive support for the application of CIR operation mode.

Resource limitation and population fluctuation drive spatiotemporal order in microbial communities.

Microbial communities display complex spatiotemporal behaviors leading to spatially-structured and ordered organization driven by species interactions and environmental factors. Resource availability plays a pivotal role in shaping the dynamics of bacterial colonies. In this study, we delve into the intricate interplay between resource limitation and the emergent properties of a growing colony of two visually distinct bacterial strains having similar growth and mechanical properties. Employing an agent-based modeling and computer simulations, we analyze the resource-driven effect on segregation and sectoring, cell length regulation and nematic ordering within a growing colony. We introduce a dimensionless parameter referred to as the active layer thickness, derived from nutrient diffusion equations, indicating effective population participation due to local resource availability. Our results reveal that lower values of active layer thickness arising from decreased resource abundance lead to rougher colony fronts, fostering heightened population fluctuations within the colony and faster spatial genetic diversity loss. Our temporal analyses unveil the dynamics of mean cell length and fluctuations, showcasing how initial disturbances evolve as colonies are exposed to nutrients and subsequently settle. Furthermore, examining microscopic details, we find that lower resource levels yield diverse cell lengths and enhanced nematic ordering, driven by the increased prevalence of longer rod-shaped cells. Our investigation sheds light on the multifaceted relationship between resource constraints and bacterial colony dynamics, revealing insights into their spatiotemporal organization.

Three New Design Patterns for Scalable Agent-Based Computing and Simulation

Three New Design Patterns for Scalable Agent-Based Computing and Simulation

An Analysis of Multi Agent Systems Agent Based Programming

The effectiveness of agent-based modeling as a simulation modeling methodology has resulted in its application in diverse settings, including the resolution of pragmatic business challenges, in recent times. The domain of symbolic artificial intelligence, which investigates intelligent and self-governing entities, is preoccupied with the mechanisms by which these entities arrive at determinations regarding their conduct in reaction to, or in expectation of, stimuli from the external environment. The scope of the methods employed encompasses a diverse array of techniques, spanning from negotiations to agent simulations, as well as multi-agent argumentation and planning. The present article scrutinizes the utilization of agent-based computing in multi-agent systems and provides an all-encompassing analysis of the relevant literature. This study delves into the examination of both traditional and contemporary agent programming languages, including their respective extensions, comparative analyses, and instances of their application in published literature.

Computer Simulations of Ethics: The Applicability of Agent-Based Modeling for Ethical Theories

Abstract I consider the applicability of Agent-Based Modeling (ABM) and computer simulations for ethical theories. Though agent-based modeling is already well established in the social sciences, it has not yet found acceptance in the field of philosophical ethics. Currently, there are only a few works explicitly connecting ethics with agent-based modeling. In this paper, I show that it is possible to build computer simulations of ethical theories and that there are also potential benefits in doing so: (1) the opportunity for virtual ethical experiments that are impossible to do in real life, and (2) an increased understanding and appreciation of an ethical theory either through the programming implementation or through the visual simulation. In the first part of the paper, I mention some social science simulations with ethical import that could encourage ethicists to work with ABM. Second, I list the few pioneering works that attempt to combine computer simulation with philosophical ethics, the most prominent being Evolving Ethics: The New Science of Good and Evil (2010) by Mascaro et al. Third, I give pointers for the computer simulation of the most prominent ethical theories: deontological ethics, utilitarianism, feminist care ethics, and virtue ethics. In the final part, I consider the potential of using an existing reference model for the simulation of human behavior, the PECS model, as the foundation for a computer simulation of virtue ethics.

Less can be more: Pruning street networks for sustainable city-making

Current trends in urban planning aim at the reduction of space for private vehicles to promote alternative mobility, more diverse activities on streets and reduced pollution for healthier cities. Simultaneously, “tactical urbanism”, embedded within participatory processes, helps to articulate and enrich these policies. In this connection, our study evaluates a number of “what-if scenarios” of “city pruning” by means of realistic, agent-based computer simulations. They regard traffic restrictions for Barcelona with the purpose to identify the impact on travel performance and the environment. Positive counterintuitive effects related to Braess and Daganzo paradoxes are found in new alternatives to the street repurposing plans developed by the City of Barcelona. These result in a reduction of emissions (−8% of main pollutants) and traffic congestion (−14% of travel time) solely by closing some streets to motor vehicles. These findings indicate a formerly unnoticed potential to further improve the quality of urban life. Hence, the use of interactive urban models can be a powerful tool to explore and co-create policies within participatory approaches in spatial planning. Positive counterintuitive effects of street repurposing and network dismantling provide opportunities for participatory and sustainable city-making beyond the ongoing public debate.

Analysis on Intelligent Agent based Approach for Software Engineering

A broad area of research, known as "Agent-Based Computing", focuses on developing "agent-based" intelligent software using agent-based techniques. However, there is a scarcity of research focusing on providing enough evidence of the superiority of agent-based techniques in creating complex software systems. This article has attempted to provide evidence for why agent-based techniques are superior to traditional methods for creating complex software systems, such as control systems. A case of a distinct agent-based control system (the power transportation management system used by Iber-26 drola) is used to illustrate these broader principles. This line of reasoning allows advocates of complex software engineering paradigms to accurately assert that their method can replicate the essential ideas behind agent-based computing. When broken down to their most basic components, agent-based systems are just computer programmes, and every programme has the same set of computable functionalities. The value of a paradigm lies in the way of thinking and the tools it teaches to programmers. As such, agent-based ideas and approaches are not just an extension of those now accessible within existing paradigms, but also well suited to the development of large, networked systems.

Supporting Three-dimensional Learning on Ecosystems Using an Agent-Based Computer Model

Supporting Three-dimensional Learning on Ecosystems Using an Agent-Based Computer Model

Agent-Based Modeling of Autosomal Recessive Deafness 1A (DFNB1A) Prevalence with Regard to Intensity of Selection Pressure in Isolated Human Population

Simple SummaryIn this study, we developed a simple simulation model to illustrate the effects of different mating patterns on the spread of autosomal recessive deafness 1A (DFNB1A) in an isolated human population with regard to the intensity of selection pressure. The modeling results have revealed that the prevalence of DFNB1A in an isolated population can be dramatically increased under frequent assortative marriages in a relatively short time period under the pressure of “relaxed” selection. However, under current conditions, the proportion of recessive homozygotes quickly reaches a short plateau and then continuously decreases. Moreover, in the long term, the studied effect can be leveled by growing social equality for deaf people, as evidenced by the results of neutral selection modeling.An increase in the prevalence of autosomal recessive deafness 1A (DFNB1A) in populations of European descent was shown to be promoted by assortative marriages among deaf people. Assortative marriages became possible with the widespread introduction of sign language, resulting in increased genetic fitness of deaf individuals and, thereby, relaxing selection against deafness. However, the effect of this phenomenon was not previously studied in populations with different genetic structures. We developed an agent-based computer model for the analysis of the spread of DFNB1A. Using this model, we tested the impact of different intensities of selection pressure against deafness in an isolated human population over 400 years. Modeling of the “purifying” selection pressure on deafness (“No deaf mating” scenario) resulted in a decrease in the proportion of deaf individuals and the pathogenic allele frequency. Modeling of the “relaxed” selection (“Assortative mating” scenario) resulted in an increase in the proportion of deaf individuals in the first four generations, which then quickly plateaued with a subsequent decline and a decrease in the pathogenic allele frequency. The results of neutral selection pressure modeling (“Random mating” scenario) showed no significant changes in the proportion of deaf individuals or the pathogenic allele frequency after 400 years.

Chronotherapy of Early Colon Cancer: Advantage of Morning Dose Schedules.

Colon adenomas with proliferating mutant cells may progress to invasive carcinomas. Proliferation of cells in human colorectal tissue is circadian, greater in the interval 4 to 12 hours after midnight than 16 to 24 hours after midnight. We have tested the hypothesis that chemotherapy administered during the time of greater cell proliferation will be more effective than chemotherapy administered during the time of lesser proliferation. An agent-based computer model of cell proliferation in colon crypts was calibrated with measurements of cell numbers in human biopsy specimens. It was used to simulate cytotoxic chemotherapy of an early stage of colon cancer, adenomas with about 20% of mutant cells. Chemotherapy doses were scheduled at different 4-hour intervals during the 24-hour day, and repeated at weekly intervals. Chemotherapy administered at 4 to 8 hours after midnight cured mutant cells in 100% of 50 trials with an average time to cure of 7.82 days (s.e.m. = 0.99). In contrast, chemotherapy administered at 20 to 24 hours after midnight cured only 18% of 50 trials, with the average time to cure of 23.51 days (s.e.m. = 2.42). These simulation results suggest that clinical chemotherapy of early colon cancer may be more effective when given in the morning than later in the day.

Agent-Oriented Software Engineering Methodologies: Analysis and Future Directions

The Internet of Things (IoT) facilitates in building cyber-physical systems, which are significant for Industry 4.0. Agent-based computing represents effective modeling, programming, and simulation paradigm to develop IoT systems. Agent concepts, techniques, methods, and tools are being used in evolving IoT systems. Over the last years, in particular, there has been an increasing number of agent approaches proposed along with an ever-growing interest in their various implementations. Yet a comprehensive and full-fledged agent approach for developing related projects is still lacking despite the presence of agent-oriented software engineering (AOSE) methodologies. One of the moves towards compensating for this issue is to compile various available methodologies, ones that are comparable to the evolution of the unified modeling language (UML) in the domain of object-oriented analysis and design. These have become de facto standards in software development. In line with this objective, the present research attempts to comprehend the relationship among seven main AOSE methodologies. More specifically, we intend to assess and compare these seven approaches by conducting a feature analysis through examining the advantages and limitations of each competing process, structural analysis, and a case study evaluation method. This effort is made to address the significant characteristics of AOSE approaches. The main objective of this study is to conduct a comprehensive analysis of selected AOSE methodologies and provide a proposal of a draft unified approach that drives strengths (best) of these methodologies towards advancement in this area.

Energy redistribution in autonomous hybridization of agent-based computing

Evolutionary multi-agent systems (EMAS) are very good at dealing with difficult, multi-dimensional problems. Currently, research is underway to improve this algorithm, giving even more freedom to agents not only in solving the problem but also in making decisions on the behavior of the algorithm. One way is to hybridize this algorithm with other existing algorithms creating Hybrid Evolutionary Multi Agent-System (HEMAS). Unfortunately, such connections generate problems in the form of an unbalanced energy level of agents who have made the decision to use such an improvement. One of the solutions is the mechanism of redistributing the agents' energy in the form of an operator. The article presents several proposals of redistribution operators along with numerous experimental results.

A bioinspired navigation strategy that uses magnetic signatures to navigate without GPS in a linearized northern Atlantic ocean: a simulation study

Certain animal species use the Earth’s magnetic field (i.e. magnetoreception) in conjunction with other sensory modalities to navigate long distances. It is hypothesized that several animals use combinations of magnetic inclination and intensity as unique signatures for localization, enabling migration without a pre-surveyed map. However, it is unknown how animals use magnetic signatures to generate guidance commands, and the extent to which species-specific capabilities and environmental factors affect a given strategy’s efficacy or deterioration. Understanding animal magnetoreception can aid in developing better engineered navigation systems that are less reliant on satellites, which are expensive and can become unreliable or unavailable under a variety of circumstances. Building on previous studies, we implement an agent-based computer simulation that uses two variants of a magnetic signature-based navigation strategy. The strategy can successfully migrate to eight specified goal points in an environment that resembles the northern Atlantic ocean. In particular, one variant reaches all goal points with faster ocean current velocities, while the other variant reaches all goal points with slower ocean current velocities. We also employ dynamic systems tools to examine the stability of the strategy as a proxy for whether it is guaranteed to succeed. The findings demonstrate the efficacy of the strategy and can help in the development of new navigation technologies that are less reliant on satellites and pre-surveyed maps.

Field Perimeter Trapping to Manage Rhagoletis mendax (Diptera: Tephritidae) in Wild Blueberry.

Rhagoletis mendax Curran (the blueberry maggot fly) is a major pest of wild blueberry. It is a direct pest of the fruit. Females lay eggs in fruit resulting in infestations of larvae unacceptable to most consumers. Three field perimeter interception tactics were tested for control of R. mendax in wild blueberry, Vaccinium angustifolium Aiton (Ericales: Ericaceae), between 2000 and 2010. We investigated field perimeter deployment of baited, insecticide-treated, green spheres (2000-2005), baited, yellow Trécé PHEROCON AM traps (2005 and 2006), and baited, Hopper Finder, sticky barrier tape (2008-2010). Only the Hopper Finder tape provided significant reduction in R. mendax adults and fruit infestation over the 3-yr field study. However, the reduction in fruit infestation compared with control plots was only 48.2 ± 7.3%, a level of reduction in damage that would be unsuitable for many commercial wild blueberry growers as a stand-alone tactic, but could be an important reduction as part of a multiple tactic IPM strategy. In addition, we constructed an agent-based computer simulation model to assess optimal trap placement between three patterns: 1) a single row of traps along field perimeter; 2) a double row of traps along the field perimeter at half the density of the single row; and 3) a grid of traps spread throughout the field but with the largest distance between traps. We found that the single row deployment pattern of traps was the best for reducing immigration of R. mendax adults into simulated fields.

Inequalities, chance and success in sport competitions: Simulations vs empirical data

We present a new way of estimation of the role of chance in achieving success, by comparing the empirical data from 100-m dash competitions (one of the sports disciplines with the most stringent controls of external randomness), with the results of an agent-based computer model, which assumes that success depends jointly on the intrinsic talent of the agent and on unpredictable luck. We find a small, but non-zero contribution of random luck to the performance of the best sprinters, which may serve as a lower bound for the randomness role in other, less stringently controlled competitive domains. Additionally we discuss the perception of the payoff differences among the top participants, and the role of random luck in the resulting inequality.

Verifiable Self-Aware Agent-Based Autonomous Systems

In this article, we describe an approach to autonomous system construction that not only supports self-awareness but also formal verification. This is based on modular construction where the key autonomous decision making is captured within a symbolically described “agent.” So, this article leads us from traditional systems architectures, via agent-based computing, to explainability, reconfigurability, and verifiability, and on to applications in robotics, autonomous vehicles, and machine ethics. Fundamentally, we consider self-awareness from an agent-based perspective. Agents are an important abstraction capturing autonomy, and we are particularly concerned with intentional, or rational, agents that expose the “intentions” of the autonomous system. Beyond being a useful abstract concept, agents also provide a practical engineering approach for building the core software in autonomous systems such as robots and vehicles. In a modular autonomous system architecture, agents of this form capture important decision making elements. Furthermore, this ability to transparently capture such decision making processes, and especially being able to expose their intentions, within an agent allows us to apply strong (formal) agent verification techniques to these systems.

An Intelligent Approach to Allocating Resources within an Agent-Based Cloud Computing Platform

The cloud computing paradigm has the ability to adapt to new technologies and provide consistent cloud services. These features have led to the widespread use of the paradigm, making it necessary for the underlying computer infrastructure to cope with the increased demand and the high number of end users. Platforms often use classical mathematical models for this purpose, helping assign computational resources to the services provided to the final user. Although this kind of model is valid and widespread, it can be refined through intelligent techniques. Therefore, this research presents a novel system consisting of a multi-agent system, which integrates a case-based reasoning system. The resulting system dynamically allocates resources within a cloud computing platform. This approach, which is distributed and scalable, can learn from previous experiences and produce better results in each resource allocation. A model of the system has been implemented and tested on a real cloud platform with successful results.