Different Types Of Agents Research Articles

Prevalence of Accidental Injuries among Building Construction Workers in Bangladesh

The construction sector is the biggest and most unsafe venture compared to other sectors of the world. The workers in this sector are more prone to accidental injuries. There are different types of agents and lacking for this type of injuries. The objectives of this study were to investigate the prevalence of accidental injuries among building construction workers in Bangladesh. The agents and lacking behind these injuries also investigated. A survey on 390 building construction workers was conducted through a structured questionnaire. The survey data was built to find out the prevalence of accidental injuries among the construction workers. The Statistical Packages for the Social Science (SPSS) version 25 was used to analysis the data. The result indicates that overall 63.80% of the participants’ sufferred from at least one-body part injuries during their work time. Most of them (24.70%) injured due to falling from the height. The workplace safety facilities (41.70%) were identified as the main lacking of the construction management that led to major accidents. The proper implementation of workplace safety facilities and design or redesign the work process may reduce or eliminate this type of injuries.

Event-Triggered Optimal Dynamic Formation of Heterogeneous Affine Nonlinear Multiagent Systems

This article studies an optimal dynamic formation problem for heterogeneous affine nonlinear systems. The nonidenticality in agents and the requirement for dynamic spatial reconfiguration make it a challenging task to coordinate different types of agents to maintain an optimized formation shape. In an architecture of event-triggered decision and control, this article investigates how to fulfill dynamic formation by distributively optimizing a team cost function. The basic idea is to design a decision unit for each agent to generate an implicit trajectory as a servo signal, based on which a control unit is designed with a displacement-gradient-based law to achieve the desired local solution. Typical heterogeneous characteristics including different nonlinearities and nonidentical dimensions are dealt with in a unified framework. It is shown that with the proposed triggering mechanisms, the optimal dynamic formation problem can be solved by a distributed control law with only intermittent communication. In theory, the properties of convergence of trajectory tracking errors, optimality of the team solution, and Zeno-freeness of event-triggered mechanisms are proved. Two simulation examples are given to verify the proposed method.

Indoor 3D reconstruction from point clouds for optimal routing in complex buildings to support disaster management

During an emergency inside large buildings such as hospitals and shopping malls, the availability of up-to-date information is critical. One common source of information is the 2D layout of buildings and emergency exits. For most buildings, this information is represented as tangled floor plans, which in most cases are outdated. One solution to update the data of buildings after each renovation is to recreate 3D models of buildings in a quick and automatic approach. These 3D models provide proactively crucial building information in a digital format for first responders to be used in emergency cases. Thanks to advances in remote sensing, laser scanners can be used to generate an accurate spatial representation of buildings quickly. However, such devices provide point clouds, which are unstructured data. In this paper, we introduce a complete workflow that allows to generate 3D models from point clouds of buildings and extract fine-grained indoor navigation networks from those models, to support advanced path planning for disaster management and navigation of different types of agents. The process extracts structural elements of buildings such as walls, slabs, ceiling and openings, and reconstruct their volumetric shapes. Additionally, the furnishing elements in the input point clouds are identified and reconstructed as the obstacles. Stairs are also reconstructed to allow multistory navigation path planning. Our algorithm is fully 3D and can handle vertical and slanted structures. We test it on several real datasets, compared it to the state-of-the-art approaches and provide a process to check the consistency of the reconstruction, which allows in return to further improve its result.

Optimal Contracts for Agents with Adverse Selection

Due to information asymmetry, adverse selection exists largely in the multiagent market. Aiming at these problems, we develop two models: pure adverse selection model and mixed adverse selection and moral hazard model. We make the assumption that a type of agent is discrete and effort level is continuous in the models. With these models, we investigate the characters that make an optimal contract as well as the conditions under which the utility of a principal and agents can be optimized. As a result, we show that, in the pure adverse selection model, the conditions to reach the optimal utility of a principal and individual agents are that a principal needs to design different contracts for different types of agents, and an individual agent chooses the corresponding type of contracts. For the mixed model, we show that incentive constraint for agents plays a very important role. In fact, we find that whether a principal provides high-type contract or a separating equilibrium contract depends on the probability of existence of low-type agents in the market. In general, if a separating equilibrium contract is issued, then information asymmetry will cause the utility of the high-type agents to be less than that of the case in full information.

A Receding Horizon Scheduling Approach for Search & Rescue Scenarios

Many applications involving complex multi-task problems such as disaster relief, logistics and manufacturing necessitate the deployment and coordination of heterogeneous multi-agent systems due to the sheer number of tasks that must be executed simultaneously. A fundamental requirement for the successful coordination of such systems is leveraging the specialization of each agent within the team. This work presents a Receding Horizon Planning (RHP) framework aimed at scheduling tasks for heterogeneous multi-agent teams in a robust manner. In order to allow for the modular addition and removal of different types of agents to the team, the proposed framework accounts for the capabilities that each agent exhibits (e.g. quadrotors are agile and agnostic to rough terrain but are not suited to transport heavy payloads). An instantiation of the proposed RHP is developed and tested for a search and rescue scenario. Moreover, we present an abstracted search and rescue simulation environment, where a heterogeneous team of agents is deployed to simultaneously explore the environment, find and rescue trapped victims, and extinguish spreading fires as quickly as possible. We validate the effectiveness of our approach through extensive simulations comparing the presented framework with various planning horizons to a greedy task allocation scheme.

Dynamics of extended Schelling models

We explore extensions of Schelling’s model of social dynamics, in which two types of agents live on a checkerboard lattice and move in order to optimize their own satisfaction, which depends on how many agents among their neighbors are of their same type. For each number n of same-type nearest neighbors we independently assign a binary satisfaction variable sk which is equal to one only if the agent is satisfied with that condition, and is equal to zero otherwise. This defines 32 different satisfaction rules, which we investigate in detail, focusing on pattern formation and measuring segregation with the help of an ‘energy’ function which is related to the number of neighboring agents of different types and plays no role in the dynamics. We consider the checkerboard lattice to be fully occupied and the dynamics consist of switching the locations of randomly selected unsatisfied agents of opposite types. We show that, starting from a random distribution of agents, only a small number of rules lead to (nearly) fully segregated patterns in the long run, with many rules leading to chaotic steady-state behavior. Nevertheless, other interesting patterns may also be dynamically generated, such as ‘anti-segregated’ patterns as well as patterns resembling sponges.

Game-theoretical interpretation of abelian logic A

In the present paper we introduce a variation of Giles's game that captures the semantics of Slaney and Meyer's Abelian logic. This is a variation of the game earlier proposed for the Łukasiewicz infinitely-valued logic. We discuss two possible interpretations of this game. One of the interpretations involves a reference to different types of agents. We also give a brief description of the Abelian logic which as well corresponds to one of the comparative logics proposed by Casari. By different types of agents, we understand agents with diverse cognitive presumptions and capabilities. This reflects the idea that different agents can be encoded by a game (dialogue) semantics and truth (and validity) can be seen as a product of different types of communications between agents, establishing the relation between various types of moves available to the players and the resulting type of rationality. However, the main focus of the paper is concentrated on the technical result concerning the game proposed in the paper. In a separate section, we prove that this game is adequate to the Abelian logic. The game can be extended to the one allowing for the disjunctive strategies. As immediate future research, we suggest proving that Proponent’s winning strategies for some formula $F$ in the game for Abelian logic $\textbf{A}$ with disjunctive strategies correspond to a derivation of the formula $F$ in the hypersequent calculus $\textbf{GA}$.

Autonomous Self-Healing in Smart Distribution Grids Using agent Systems

When a fault occurs on a distribution system, it should be isolated. After isolation, the distribution system can be reconfigured to restore the out-of-service area. The distribution system configuration can be changed by performing switching actions on the feeders. This paper proposes an agent-based approach for service restoration in smart distribution systems. The proposed multiagent approach has three different types of agents, namely, feeder agents, zone agents, and switch agents. The agents can communicate and cooperate with each other in order to supply services to out-of-service customers. The restoration plan is built on the basis of local data considering system conditions, operational constraints, and the fault location. Different loading conditions have been considered under different scenarios, and the results of the proposed multiagent approach for each scenario have been compared. Simulation results show the efficiency of the proposed agent architecture.

The Value of Precontract Information About an Agent's Ability in the Presence of Moral Hazard and Adverse Selection

ABSTRACTWe analyze the expected value of information about an agent's type in the presence of moral hazard and adverse selection. Information about the agent's type enables the principal to sort/screen agents of different types. The value of the information decreases in the variability of output and the agent's risk aversion, two factors that are typically associated with the severity of the moral hazard problem. However, the value of the information about agent type first increases but ultimately decreases in the severity of adverse selection. The decrease comes about because the means available to the principal to induce effort—namely, the pay–performance sensitivity—must also be used to sort/screen agents, and these two goals conflict. This decline in value occurs despite the monotonically increasing importance of the information in determining the principal's expected profits. Further, we show that the peak value of information occurs at a predictable level of adverse selection. These results imply that over some range, the importance of the information will be increasing, and the value of the information will be simultaneously decreasing, in the severity of adverse selection.

Absence of speculation in the European sovereign debt markets

European sovereign debt markets have been under scrutiny since the sovereign debt crisis of 2009. In this paper, we study to what extent the extreme dynamics were driven by fundamentals or speculation. We do so by decomposing bond and CDS spreads into fundamental and non-fundamental parts using a heterogeneous agent model. We find that bond markets are driven for 80% by liquidity trading, 13% by credit news, and only 5.4% by speculation. The CDS market is for 49% driven by credit news, 45% liquidity trading, and 5.5% speculation. The relative importance of the different types of agents varies over time, though.

Topical Host-Modulating Therapy for Periodontal Regeneration: A Systematic Review and Meta-Analysis.

Background: Over the past decades, locally delivered host-modulating pharmacological agents have been extensively investigated to treat periodontitis. Although a small category of agents has been tested in clinical trials, most of them were reported in the animal experiments. This systematic review evaluates the efficacy of local application of currently available host-modulating agents, with or without mechanical removal of plaque, in animal models with periodontitis or periodontal defect. Methods: PubMed and Embase were searched on February 11, 2019. The inclusion criteria were as follows: (1) experiments were performed in healthy animals (all ages, sexes, and species) with periodontitis, and (2) outcome data for the local application of host-modulating approaches were presented for bone quantity, bone loss, and attachment loss and compared with a vehicle control group. Study characteristics and outcome data were extracted and internal validity was assessed. The efficacy of host-modulating agents was analyzed in a meta-analysis. A standardized mean difference (SMD) and its 95% confidence intervals for each individual comparison were calculated to estimate the overall effect. Subgroup analyses were conducted on animal species and different types of agents. Results: Forty-eight articles were included in the review, of which 42 were included in meta-analysis on bone quantity, bone loss, and attachment loss. The results showed that host-modulating therapy significantly increased bone formation (SMD: 2.200 [1.560-2.840], n = 24), and decreased bone loss (SMD: -1.659 [-1.969 to -1.348], n = 51) and attachment loss (SMD: -1.572 [-2.211 to -0.933], n = 17). No significant subgroup effects were identified, indicating that the effects are similar across species and drug types. Conclusions: The current scientific evidence supports the use of local host-modulation therapy in the treatment of periodontitis in experimental animals. Our findings seem robust for various animal models and designs included in this review, which increases our confidence of the translational value of the results to the clinical situation. Impact Statement Over the past decades, locally delivered host-modulating pharmacological agents have been extensively investigated for periodontal regeneration. Although a small category of agents has been tested in clinical trials, most of them were reported in the animal experiments. Considering the increasing amount of preclinical evidence and the need for future clinical trials, a systematic review and meta-analysis of all preclinical data was performed to determine the translational value of host-modulating drugs for human application. The results showed that host-modulating therapy significantly increased bone formation and decreased bone and attachment loss. This study contributes to researchers working on the periodontal translational research.

The Value of Pre-contract Information about an Agent's Ability in the Presence of Moral Hazard and Adverse Selection

We analyze the expected value of information about an agent's type in the presence of moral hazard and adverse selection. Information about the agent's type enables the principal to sort/screen agents of different types. The value of the information decreases in the variability of output and the agent's risk aversion, two factors that are typically associated with the severity of the moral hazard problem. However, the value of the information about agent type first increases but ultimately decreases in the severity of adverse selection. The decrease comes about because the means available to the principal to induce effort — namely, the pay-performance sensitivity — must also be used to sort/screen agents, and these two goals conflict. This decline in value occurs despite the monotonically increasing importance of the information in determining the principal's expected profits. Further, we show that the peak value of information occurs at a predictable level of adverse selection. These results imply that over some range, the importance of the information will be increasing, while the value of the information will be simultaneously decreasing, in the severity of adverse selection.

Consensus Vibration Control for Large Flexible Structures of Spacecraft With Modified Positive Position Feedback Control

In this brief, consensus modified positive position feedback (CMPPF) is proposed to depress the vibration of large flexible structure in the spacecraft. CMPPF is formulated by decentralizing and distributing the modified positive position feedback (MPPF) to a network of control agents. Variables are appropriately selected and the consensus algorithm is employed to make the multiagents consensus with each other. CMPPF convergence conditions are derived and the control parameters are optimized. Amplitude-frequency response analysis and simulations demonstrate that CMPPF suppresses vibrations faster and more evidently than MPPF. Results also indicate how consensus between agents is realized and how agents’ disagreement is eliminated. Effects of different consensus variables and different communication topologies are studied. Meanwhile, results for different types of agents’ failure are presented. CMPPF is robust to topology and agents’ failure and achieves satisfactory performances.

Alpha Blocker and 5-Alpha Reductase Inhibitor Prescribing Habits among Urologists and Primary Care Physicians.

While primary care physicians often prescribe medical therapy for voiding symptoms attributed to benign prostate enlargement, it is not clear to what extent they use novel or varied agents. We describe alpha blocker and 5-alpha reductase inhibitor prescribing habits of primary care physicians and compare them with those of urologists. Within Medicare Part D we identified providers who prescribed alpha blockers and 5-alpha reductase inhibitors in 2015. We determined the proportions that prescribed 1 versus multiple agents, different types of agents and both types of medications, and compared them between providers. Overall 94% (9,327) of urologists, 50% (54,253) of internal medicine physicians and 61% (60,063) of family medicine physicians prescribed an alpha blocker in 2015. Urologists were more likely to prescribe multiple alpha blockers. However, they were also more likely to predominantly use a single agent. A higher percentage of urologists prescribed newer agents (alfuzosin, silodosin) while a higher percentage of primary care physicians prescribed older agents (terazosin, doxazosin). For 5-alpha reductase inhibitors 87.5% (8,692) of urologists, 32.0% (34,598) of internal medicine physicians and 34.4% (33,720) of family medicine physicians issued prescriptions. Urologists were more likely to prescribe a single 5-alpha reductase inhibitor predominantly and prescribe multiple 5-alpha reductase inhibitors. More primary care physicians prescribed alpha blockers without also prescribing 5-alpha reductase inhibitors. Most primary care physicians prescribed alpha blockers to Medicare beneficiaries. Urologists were more likely to use diverse as well as newer agents, signaling greater awareness of medical options, although also more complex cases. Urologists were more likely to habitually prescribe single medications. As primary care physicians are involved in the initial treatment of these patients, further education regarding medical options and appropriate indications should be considered.

Does a Target Platform’s Reverse Resource Transfer Always Mean a Loss? The Perspective of User-Generated Content Volume

We have witnessed tremendous growth in platform firms over the past several years. Given the “winner take all” principle of two-sided markets, platform firms usually engage in aggressive strategies to compete, increasing the likelihood of mergers and acquisitions (M&As). The extant literature, however, concentrates on competition strategies while neglecting cooperation strategies in the context of platform M&A. Thus, this study focuses on the implementation of a reverse resource transfer policy during the post-M&A integration of two transaction platforms. Using data obtained from the target platform of the M&A event, we perform regression discontinuity design to analyze the causal effect of the policy change on user-generated content (UGC) volume of merchants in a target platform. The results indicate that the policy change leads to a positive discontinuity jump for the UGC volume of merchants around the cutoff point. Specifically, policy change increases the volume of reviews with pictures and the number of interactions within reviews in both the short and long run. These findings help to deepen our understanding of the impact of reverse resource sharing of the target platform firm on the generation of UGC and clarify how different types of agents can benefit from the strategy.

A NOTE ON ASYMMETRIES IN FRIENDSHIP NETWORKS

We generalize the analysis of Brueckner’s [(2006). Friendship networks. Journal of Regional Science, 847–865] friendship networks under different types of asymmetries. We first study cost asymmetries by allowing for cost-magnetic agents. These are players whose linking costs are the lowest. Next we focus on network-based asymmetries by allowing for knows-everyone agents. We identify conditions under which Nash networks are interlinked stars in both these cases. We also find that for asymmetric situations (whether in costs, values, or architectures), the equilibrium effort choices regarding the links between different types of agents are robust across the various friendship network models.

Robust switching control for service restoration in smart grids considering switching malfunction by MAS

This study proposes an agent-based approach to have a reliable service restoration scheme, which is vital to restore as many loads as possible after a permanent fault. The proposed multi-agent system (MAS) has four different types of agents: feeder agents, zone agents, switch agents and DG agents. The agents can communicate and cooperate with each other in order to supply services to out-of-service customers. An artificial neural network has been considered to handle DG uncertainties. The restoration plan is built in a distributed manner, based on local data considering system conditions, operational constraints and fault location. The result of the proposed MAS is a switching sequence. After defining this sequence, the command for opening will be received by circuit breaker agents. In case of one switch failure, the plan cannot be implemented, and hence the restoration plan will fail. To have a reliable restoration plan, the main feeder agent has to handle malfunction in order to operate the system at an acceptable range without violating system constraints. Different loading conditions have been considered under different scenarios and the results of the proposed approach for each scenario have been compared. Simulation results show the efficiency of the proposed agent architecture.

Market selection with idiosyncratic uncertainty

I analyze the survival probabilities of different types of agents in a general equilibrium model with disagreement over idiosyncratic uncertainties. I find that such biases create a separation between individual and group level survival: even when the survival probability of a single irrational agent tends to zero, these agents may still succeed as a whole. Effectively the irrational agent population can survive due to a vanishingly small group of increasingly rich agents. Disagreement over idiosyncratic uncertainties distorts savings decisions and interest rates, but idiosyncratic risks are not priced. Simulations confirm that the limiting results are relevant when the population of irrational agents is large.

Market Selection with Idiosyncratic Uncertainty

I analyze the survival probabilities of different types of agents in a general equilibrium model with disagreement over idiosyncratic uncertainties. I find that such biases create a separation between individual and group level survival: even when the survival probability of a single irrational agent tends to zero, these agents may still succeed as a whole. Effectively the irrational agent population can survive due to a vanishingly small group of increasingly rich agents. Disagreement over idiosyncratic uncertainties distorts savings decisions and interest rates, but idiosyncratic risks are not priced. Simulations confirm that the limiting results are relevant when the population of irrational agents is large.

A multiagent design for self-healing in electric power distribution systems

The out-of-service area after fault should berestored by changing the distribution system configuration by means of switching actions on the feeders. This paper proposes an agent-based approach for service restoration in smart distribution systems with distributed generations (DG). The proposed multi agent has four different types of agents: feeder agents, zone agents, switch agents and DG agents. The agents can communicate and cooperate with each other in order to supply services to out-of-service customers. An artificial neural network has been considered within DG agent to predict the DG generation. The restoration plan is built based on local data considering system conditions, operational constraints and fault location. Different loading conditions have been considered under different scenarios and the result of proposed multi agent approach for each scenario with and without DG have been compared. Simulation results show the efficiency of proposed agent architecture.