Simple Agents Research Articles

Wealth distribution models with regulations: Dynamics and equilibria

Simple agent based exchange models are a commonplace in the study of wealth distribution in an artificial economy. Generally, in a system that is composed of many agents characterized by their wealth and risk-aversion factor, two agents are selected sequentially and randomly to exchange wealth, allowing its redistribution. Here we analyze how the effect of social protection policies, which favor agents of lower wealth during the exchange, influences the stability and some relevant economic indicators of the system. On the other hand, we study how periods of interruption of these policies produce, in the short and long term, changes in the system. In all cases, a steady state is reached, but with varying relaxation times. We conclude that regulations may improve economic mobility and reduce inequality. Moreover, our results indicate that the removal of social protection entails a high cost associated with the hysteresis of the distribution of wealth. Economic inequalities increase during the period without social protection and decrease when they are restored. But recovery to the initial equilibrium may take longer than it took to disturb it. In some extreme cases, inequality is irreversible.

Agent-based dynamic scheduling model for product-driven production

Goal: This research provides specific solution for dynamic scheduling of product-driven production with unique level of detail and original architecture.
 Design / Methodology / Approach: Design process of scheduling problem-solving MAS is divided into three steps: agent encapsulation of entities participating in scheduling, including concept of agents and responsibilities they assume, system architecture and topology of the agents network, detailed design of decision scheme of individual agents.
 Results: Production processes take place in dynamic environment and have to react to numerous real-time events, hence reschedule the production by a new design and implement agent-based model in order to solve dynamic flexible job shop scheduling problem in product-driven production environment.
 Limitations of the investigation: Designed model counts with simple agents behaving on condition-action rules. These agents could be replaced by more sophisticated types of agents such as utility-based or learning agents. Also the implemented coordination mechanism ensuring global view on the scheduling problem is rather simple.
 Practical implications: Via simulations of realistic production scenarios it was expected to prove an applicability of specific solution of how bringing the intelligence to the lower levels of control system may be used in dynamic scheduling. Based on elaboration of theoretical basis, principles were identified as suitable or widely used in design of such model.
 Originality / Value: This research provides specific real-time architecture for a multi-agents dynamic scheduling of product-driven production with unique level of detail and scenarios analysis.

Inspiration-wise swarm intelligence meta-heuristics for continuous optimisation: a survey - part I

In many science fields, such as engineering, administration, transportation, economics and biology, among many others, optimisation problem is a handy required tool. Global optimisation techniques aim to find the best solution in a set of feasible solutions to a problem. Currently, there are numerous optimisation techniques. In general, the problems to be optimised are complex, nonlinear and in some cases may be intractable. Meta-heuristics are general algorithmic frameworks adaptable to various optimisation problems and are generally applied to highly complex problems. Swarm intelligence represents intelligent models inspired by real-world social systems, based on interaction and organisation between simple agents to perform simple tasks. Nowadays, there are many swarm-based meta-heuristics. In this survey, we take advantage of the inspiration behind the strategies to contribute an inspiration-oriented novel taxonomy for the current state-of-the-art of swarm-oriented optimisation methods. The overall survey, which will be divided into three separate parts, provides a review of swarm-based meta-heuristic, which is commonly employed to solve complex continuous optimisations, aiming at building a inspiration-based taxonomy for such search strategies. In this part of the survey, we review meta-heuristics that are guided by some interesting human relation's characteristics and those that are inspired by physical system's properties.

Compartmental voter model

Numerous models in opinion dynamics focus on the temporal dynamics within a single electoral unit (e.g. country). The empirical observations, on the other hand, are often made across multiple electoral units (e.g. polling stations) at a single point in time (e.g. elections). Aggregates of these observations, while quite useful in many applications, neglect the underlying heterogeneity in opinions. To address this issue we build a simple agent–based model in which all agents have fixed opinions, but are able to change their electoral units. We demonstrate that this model is able to generate rank–size distributions consistent with the empirical data.

Benzoin Thiosemicarbazone Inhibits Growth and Triggers Apoptosis in Earlich Ascites Carcinoma Cells through an Intrinsic Pathway

Anticancer drug development is now an emerging field of research all over the world. In this study we attempt to synthesize a novel, simple, inexpensive and safe chemical agents Benzoin thiosemicarbazone (BTSC) and studied against Ehrlich Ascites Carcinoma (EAC) cells bearing Swiss Albino mice by monitoring in vivo tumor cell growth inhibition, survival time of tumor bearing swiss albino mice. MTT colorimetric assay was done to assess the in vitro effect of the test compound. The intrinsic apoptotic pathway induced by BTSC was evidenced by p53 or tumor protein, B-cell lymphoma 2 (BCL-2), B-cell lymphoma extra-large (BCL-xL), BCL-2 associated X protein (BAX), cleavage of caspase-9 and caspase-3 and poly-ADP ribose polymerase (PARP-1). Reactive oxygen species (ROS) generation after BTSC treatment was determined by 2´, 7´- dicholorodihydrofluorescein diacetate (DCFH-DA) staining. The compound was found to possess pronounced anticancer effect. Maximum cell growth inhibition, enhancement of life span was found 73.53% and 52.17% at the dose of 8 mg/kg (i.p) respectively. The induction of apoptosis by BTSC occurred through an ROS-dependent mitochondria-mediated intrinsic pathway rather than an extrinsic pathway, and was regulated by the BCL-2 protein family. The compound exerted a potent antitumor effect toward Ehrlich ascites carcinoma cells through the induction of apoptosis via an intrinsic pathway. Thus, this study provides evidence to carry out further researches in a way to formulate novel anticancer drugs.

Coevolution of the Asymmetric Morphology and the Behaviour of Simple Predator Agents in Predator-Prey Pursuit Problem.

Humanity has long strived to create microscopic machines for various purposes. Most prominent of them employ nanorobots for medical purposes and procedures, otherwise deemed hard or impossible to perform. However, the main advantage of this kind of machines is also their main drawback—their small size. The miniature scale, they work in, brings many problems, such as not having enough space for the computational power needed for their operation or the specifics of the laws of physics that govern their behaviour. In our study, we focus on the former challenge, by introducing a new standpoint to the well-studied predator-prey pursuit problem using an implementation of very simple predator agents. Intended to model the small-scale (micro and nano) robots, these agents are morphologically simple—they feature a single line-of-sight sensor. The behaviour of the predator agents is simple as well—the (few) perceived environmental variables are mapped directly into corresponding pairs of rotational velocities of the wheels' motors. We implemented genetic algorithm to evolve such a mapping that results in a successful capturing of the prey by the team of predator agents. However, as the preliminary results indicated, the predators that use a straightforward sensor could not resolve more than just few of the tested initial situations. Thus, to improve the generality of the evolved behaviour, we proposed an asymmetric sensory morphology of predators—an angular offset to the sensor relative to the longitudinal axis—and coevolved the amount of such an offset together with the behaviour of predators. The behaviours, coevolved with a sensor offset between 12° and 38°, resulted in both an efficient and consistent capture of the prey in all tested initial situations. Moreover, some of the behaviours, coevolved with sensor offset between 18° and 24°, demonstrated a good generality to the increased speed of the prey and a good robustness to perception noise. The obtained results could be seen as a step towards the engineering of asymmetric small-scale for delivery of medicine, locating and destroying cancer cells, microscopic imaging, etc.

Evolution, Robustness and Generality of a Team of Simple Agents with Asymmetric Morphology in Predator-Prey Pursuit Problem

One of the most desired features of autonomous robotic systems is their ability to accomplish complex tasks with a minimum amount of sensory information. Often, however, the limited amount of information (simplicity of sensors) should be compensated by more precise and complex control. An optimal tradeoff between the simplicity of sensors and control would result in robots featuring better robustness, higher throughput of production and lower production costs, reduced energy consumption, and the potential to be implemented at very small scales. In our work we focus on a society of very simple robots (modeled as agents in a multi-agent system) that feature an “extreme simplicity” of both sensors and control. The agents have a single line-of-sight sensor, two wheels in a differential drive configuration as effectors, and a controller that does not involve any computing, but rather—a direct mapping of the currently perceived environmental state into a pair of velocities of the two wheels. Also, we applied genetic algorithms to evolve a mapping that results in effective behavior of the team of predator agents, towards the goal of capturing the prey in the predator-prey pursuit problem (PPPP), and demonstrated that the simple agents featuring the canonical (straightforward) sensory morphology could hardly solve the PPPP. To enhance the performance of the evolved system of predator agents, we propose an asymmetric morphology featuring an angular offset of the sensor, relative to the longitudinal axis. The experimental results show that this change brings a considerable improvement of both the efficiency of evolution and the effectiveness of the evolved capturing behavior of agents. Finally, we verified that some of the best-evolved behaviors of predators with sensor offset of 20° are both (i) general in that they successfully resolve most of the additionally introduced, unforeseen initial situations, and (ii) robust to perception noise in that they show a limited degradation of the number of successfully solved initial situations.

Top-k Recommended Items: Applying Clustering Technique for Recommendation

This research proposes the Top-k Items Recommendation System which uses clustering techniques based on memory-based collaborative filtering technique. Currently, data sparsity and quantity of system are problems in memory-based collaborative filtering technique. We offer recommend or show some items set for user’s preference. In this research, we propose methods for recommended items set to user preference on data sparsity, movie lens datasets (1M) consisting of 671 users and 163,949 product items were used by determining the preference level between 1 and 5 and user satisfaction levels of all 98,903 items being build and test the models. Methods was divided into three parts included 1) Simple Agent Module 2) Neighbor Filtering and 3) Prediction and Recommend. Simple clustering was used to create a system to provide suggestions for sparsity data. Datasets obtained from clustering represented the sample agent of dataset to being create the recommendation system. Datasets were divided into two categories, 1) Traditional Data (TD) and 2) Statistic Data (SD), and each dataset clustered by k-means clustering. The experimental results demonstrated that the number of item types in the system were recommended in the TD and Euclidean (DIS). DIS was used to find the nearest value in TD for the item list recommendation to active users in the system with the a lot of number choice of recommendation system.

Distributed operation of collaborating unmanned aerial vehicles for time-sensitive oil spill mapping

Abstract Multiple simple agents working together to achieve a common complex goal embodies the underlying theme of swarm concepts, with decentralized decision-making serving as the new frontier for tackling challenges associated with scalability, fault tolerance, and communication constraints. This paper builds on this emerging paradigm to develop a distributed approach (called PSOil) for off-shore oil spill mapping using a team of unmanned aerial vehicles or UAVs. In-flight waypoint planning is achieved via a new particle swarm mechanics-inspired technique, employing a novel combination of anomaly detection for knowledge extraction, and a stochastic occupancy grid approach for timely processing and frugal sharing of knowledge (with net communications

Building, composing and experimenting complex spatial models with the GAMA platform

The agent-based modeling approach is now used in many domains such as geography, ecology, or economy, and more generally to study (spatially explicit) socio-environmental systems where the heterogeneity of the actors and the numerous feedback loops between them requires a modular and incremental approach to modeling. One major reason of this success, besides this conceptual facility, can be found in the support provided by the development of increasingly powerful software platforms, which now allow modelers without a strong background in computer science to easily and quickly develop their own models. Another trend observed in the latest years is the development of much more descriptive and detailed models able not only to better represent complex systems, but also answer more intricate questions. In that respect, if all agent-based modeling platforms support the design of small to mid-size models, i.e. models with little heterogeneity between agents, simple representation of the environment, simple agent decision-making processes, etc., very few are adapted to the design of large-scale models. GAMA is one of the latter. It has been designed with the aim of supporting the writing (and composing) of fairly complex models, with a strong support of the spatial dimension, while guaranteeing non-computer scientists an easy access to high-level, otherwise complex, operations. This paper presents GAMA 1.8, the latest revision to date of the platform, with a focus on its modeling language and its capabilities to manage the spatial dimension of models. The capabilities of GAMA are illustrated by the presentation of applications that take advantage of its new features.

Pay-off scarcity causes evolution of risk-aversion and extreme altruism

All organisms descend from populations with limited resources, so it is clear why evolution should select strategies that win resources at the expense of competitors. Less obvious is how altruistic behaviours evolve, whereby an individual helps others despite expense to itself. Modelling simple agents using evolutionary game theory, it is shown that steady states of extreme altruism can evolve when pay-offs are very rare compared with death. In these states, agents give away most of their wealth. A new theorem for general evolutionary models shows that, when pay-offs are rare, evolution no longer selects strategies to maximize income (average pay-off), but to minimize the risk of missing-out entirely on a rare resource. Principles revealed by the model are widely applicable, where the game represents rare life-changing events: disasters or gluts.

Recovery and cryopreservation of insulin amyloid using ionic liquids

Amyloid aggregates containing a high content of β-sheet structure are one of several water-insoluble protein aggregates. In this study, we assessed the use of aqueous ionic liquid (IL) solutions as part of a simple preservation/refolding technique for amyloid aggregates. We investigated the dissolution and refolding of cryopreserved bovine insulin amyloid in concentrated aqueous solutions (X = 20 mol%IL) of six different ionic liquids: three 1-butyl-3-methylimidazolium-based ILs ([bmim][X]; X = SCN, NO3, or Cl), 1-ethyl-3-methylimidazolium nitrate ([emim][NO3]), ethylammonium nitrate (EAN), and propylammonium nitrate (PAN). Dimethyl sulfoxide (DMSO) was used as the reference standard for spectroscopy. The six ILs all exhibited the ability to dissolve insulin amyloid, with imidazolium-based ILs exhibiting increased dissolution capacity compared with ammonium-based ILs or DMSO. Remarkably, >80% of the secondary structure of the folded insulin monomer was recovered from insulin amyloid dissolved in aqueous IL solutions following cryopreservation and IL removal by dialysis. These results indicate that concentrated aqueous IL solutions have potential as a new and simple agent for the dissolution, cryopreservation, and refolding of amyloid aggregates.

Indirect Reciprocity and the Evolution of Prejudicial Groups

Prejudicial attitudes are widely seen between human groups, with significant consequences. Actions taken in light of prejudice result in discrimination, and can contribute to societal division and hostile behaviours. We define a new class of group, the prejudicial group, with membership based on a common prejudicial attitude towards the out-group. It is assumed that prejudice acts as a phenotypic tag, enabling groups to form and identify themselves on this basis. Using computational simulation, we study the evolution of prejudicial groups, where members interact through indirect reciprocity. We observe how cooperation and prejudice coevolve, with cooperation being directed in-group. We also consider the co-evolution of these variables when out-group interaction and global learning are immutable, emulating the possible pluralism of a society. Diversity through three factors is found to be influential, namely out-group interaction, out-group learning and number of sub-populations. Additionally populations with greater in-group interaction promote both cooperation and prejudice, while global rather than local learning promotes cooperation and reduces prejudice. The results also demonstrate that prejudice is not dependent on sophisticated human cognition and is easily manifested in simple agents with limited intelligence, having potential implications for future autonomous systems and human-machine interaction.

Clinical value of single photon emission computed tomography imaging in differential diagnosis of neonatal obstructive jaundice

Objective To investigate and explore the clinical value of single-photon emission computed tomography(SPECT) imaging in the differential diagnosis of neonatal obstructive jaundice. Methods From January 2012 to April 2017, 130 cases of suspected neonatal obstructive jaundice in the First Hospital of Jiaxing were enrolled in the study.All patients were given SPECT hepatobiliary scintigraphy, were injected 99mTc-EHIDA imaging agent, 99mTc-MIBI imaging agent, diagnosis of hepatobiliary scintigraphy after the acquisition.The result of operative cholangiography was viewed as the gold standard, the diagnostic sensitivity, specificity, accuracy of simple 99mTc-EHIDA SPECT imaging examination, combined with SPECT imaging agent in the diagnosis of neonatal obstructive jaundice were calculated and compared.The coincidence rate of differential diagnosis of different etiologies of neonatal obstructive jaundice was also compared. Results The diagnostic sensitivity, specificity and accuracy of combined with SPECT imaging agent in the diagnosis of neonatal obstructive jaundice were 94.23%, 96.15%, 94.61%, respectively, which of simple 99mTc-EHIDA imaging agent SPECT were 83.65%, 76.92%, 82.31%, respectively, the differences were statistically significant(χ2=5.915, 4.127, 9.646, all P<0.05). The coincidence rates of extrahepatic biliary atresia, hepatitis syndrome of the two kinds of SPECT imaging agents combined examination were 96.05%, 96.43%, respectively, which were significantly higher than those of the 99mTc-EHIDA imaging agent SPECT examination (84.21%, 85.00%)(χ2=5.991, 5.250, all P<0.05). Conclusion SPECT imaging in the diagnosis of neonatal obstructive jaundice has high sensitivity and accuracy, especially in combination with 99mTc-EHIDA imaging agent, 99mTc-MIBI imaging agent can identify and distinguish different causes of jaundice, and provide a reliable basis for clinical treatment. Key words: Jaundice, neonatal; Jaundice, obstructive; Diagnosis; Single-photon emission computed tomography

Swarm Intelligence: A Primer

Swarm intelligence is the emergent collective intelligence of groups of simple agents. It belongs to the emerging field of bio-inspired soft computing. It is inspired from the biological entities such as birds, fish, ants, wasps, termites, and bees. Bio-inspired computation is a field of study that is closely related to artificial intelligence. This paper provides a brief introduction to swarm intelligence.

Single Viruses on the Fluorescence Microscope: Imaging Molecular Mobility, Interactions and Structure Sheds New Light on Viral Replication.

Viruses are simple agents exhibiting complex reproductive mechanisms. Decades of research have provided crucial basic insights, antiviral medication and moderately successful gene therapy trials. The most infectious viral particle is, however, not always the most abundant one in a population, questioning the utility of classic ensemble-averaging virology. Indeed, viral replication is often not particularly efficient, prone to errors or containing parallel routes. Here, we review different single-molecule sensitive fluorescence methods that we employ routinely to investigate viruses. We provide a brief overview of the microscopy hardware needed and discuss the different methods and their application. In particular, we review how we applied (i) single-molecule Förster resonance energy transfer (smFRET) to probe the subviral human immunodeficiency virus (HIV-1) integrase (IN) quaternary structure; (ii) single particle tracking to study interactions of the simian virus 40 with membranes; (iii) 3D confocal microscopy and smFRET to quantify the HIV-1 pre-integration complex content and quaternary structure; (iv) image correlation spectroscopy to quantify the cytosolic HIV-1 Gag assembly, and finally; (v) super-resolution microscopy to characterize the interaction of HIV-1 with tetherin during assembly. We hope this review is an incentive for setting up and applying similar single-virus imaging studies in daily virology practice.

LUCID PARTICLE SWARM OPTIMIZATION ALGORITHM FOR SOLVING OPTIMAL REACTIVE POWER PROBLEM

This paper presents, Lucid Particle Swarm Optimization (LPSO) algorithm for Solving Optimal Reactive Power Problem. Particle swarm method is typically made up of a population of simple agents intermingle locally with one another and with their surroundings, with the aim of locating the optima within the operational environment. In this paper, a robust and Lucid particle swarm optimization framework based on multi-agent system is presented, where learning capabilities are integrated into the particle agents to dynamically fiddle with their optimality behaviours. Self-Sufficiency is achieved by the use of communicators that separate an agent’s individual operation from that of the swarm, thereby making the system more robust. The proposed Lucid Particle Swarm Optimization (LPSO) algorithm has been tested on standard IEEE 118 &amp; practical 191 bus test systems and simulation results show clearly about the premium performance of the proposed algorithm in reducing the real power loss.

A NOVELMETAHEURISTIC ALGORITHM BASED ON WAVE FUNCTION

Aiming at the phenomenon of slow convergence rate and low accuracy of some metaheuristic algorithms , a novel metaheuristic algorithm based on wave function and swarm intelligence is proposed. Swarm intelligence is a collective intelligence of groups of simple agents deals with collective behaviors of decentralized and self-organized swarms, which result from the local interactions of individual components with one another and with their environment. Wave function is introduced to accelerate the convergence speed of proposed algorithm. Standard deviation can ensure the dispersion of the population to prevent premature convergence. The simulation results using Benchmark functions show that the proposed algorithm is effective.

Self-Organized Linguistic Systems: From traditional AI to bottom-up generative processes

This work seeks to explore the potential of bottom-up generative processes in the context of conlang production, aiming to describe the basis of a new field of research: Self-Organized Linguistic Systems or SOLS, specified under the perspective of both self-organized systems and constructed languages. SOLS approach provides a framework for the creation of self-generated artificial languages and may serve as a starting point for the development of context-dependent or domain-specific languages. It acknowledges that the development of conlangs can happen in artificial societies of simple agents, as the output of social interactions in computational simulations under the agent-based modelling paradigm.In the proposed initial SOLS model, automatic generation of lexicon takes place in the context of a digital environment with objects, actions and agents with embodied cognition through peer-to-peer interactions. Specifically, this paper exposes how SOLS can be developed with bi-dimensional games and simulations. An initial work has been done with the xmunch-atomspace and the SciArt simulator, which constitute the first implementations of both our knowledge representation toolbox and our bi-dimensional simulator of P2P Social Dynamics. Non-interactive agent-based SOLS can allow artificial agents to independently evolve emergent languages as part of their self-organizing or adaptation processes.

Visualization of Fluorescent Protein Expression in Whole-Mount Postimplantation-Stage Mouse Embryos.

Fluorescent protein (FP)-expressing transgenic mice are powerful genetic resources for marking both live and fixed cells and tissues. Expression in live embryos requires only dissection and visualization using an appropriate microscope. Fixation can compromise FP activity. A simple tissue-clearing agent called Scale preserves FP activity while rendering the embryo or organ optically transparent.