Behavior model of an intelligent agent based on a genetic algorithm

A solution of improving the behavior model of a non-player character as an intelligent agent by optimizing input parameters based on a genetic algorithm is presented. The proposed approach includes the development of a non-player character model: a skeleton, rigid bodies, the implementation of a dynamic model based on the Featherstone algorithm, and modeling of the character's behavior based on a genetic algorithm. The formation of a behavior model using a genetic algorithm that simulates the physical properties of a character, taking into account his actions, is proposed. The stages of the genetic algorithm include creating an initial population, fitness score, selection, crossing and mutation. Based on the results of the experiments, the input parameters of the non-player character behavior model were determined, maximizing the cumulative fitness score, which acts as an estimate of the reward, which can be used as initial values for further experiments. Keywords: non-player character, intelligent agent, simulation, genetic algorithm

Effectiveness of the COVID-19 Contact-Confirming Application (COCOA) Based on Multi-Agent Simulation

As of Aug. 2020, coronavirus disease 2019 (COVID-19) is still spreading in the world. In Japan, the Ministry of Health, Labour and Welfare developed “COVID-19 Contact-Confirming Application (COCOA),” which was released on June 19, 2020. By utilizing COCOA, users can know whether or not they had contact with infected persons. If those who had contact with infected individuals keep staying at home, they may not infect those outside. However, effectiveness decreasing the number of infected individuals depending on the app’s various usage parameters is not clear. If it is clear, we could set the objective value of the app’s usage parameters (e.g., the usage rate of the total populations) and call for installation of the app. Therefore, we develop a multi-agent simulator that can express COVID-19 spreading and usage of the apps, such as COCOA. In this study, we describe the simulator and the effectiveness of the app in various scenarios. The result obtained in this study supports those of previously conducted studies.

Transit Network Frequency Setting With Multi-Agent Simulation to Capture Activity-Based Mode Substitution

We propose a bilevel transit network frequency setting problem in which the upper level consists of analytical route cost functions and the lower level is an activity-based market equilibrium derived using MATSim-NYC. The use of MATSim in the lower-level problem incorporates sensitivity of the design process to competition from other modes, including ride-hail, and can support large-scale optimization. The proposed method is applied to the existing Brooklyn bus network, which includes 78 bus routes, 650,000 passengers per day, 550 route-km, and 4,696 bus stops. MATSim-NYC modeling of the existing bus network has a ridership-weighted average error per route of 21%. The proposed algorithm is applied to a benchmark network and confirms their predicted 20% growth in ridership using their benchmark design. Applying our proposed algorithm to their network with 78 routes and 24 periods, we have a problem with 3,744 decision variables. The algorithm converged within 10 iterations to a delta of 0.064%. Compared with the existing scenario, we increased ridership by 20% and reduced operating cost by 25%. We improved the farebox recovery ratio from the existing 0.22 to 0.35, 0.06 more than the benchmark design. Analysis of mode substitution effects suggest that 2.5% of trips would be drawn from ride-hail while 74% would come from driving.

Simulation and Analysis of Coal Mine Safety Escape Routes Based on a Multi-agent Model

In order to discuss the influencing factors of mine escape decision when underground disaster occurs, this paper uses the multi-agent modeling theory to construct the coal mine safety accident escape model. The real escape scenarios of different chamber positions and multiple exits are simulated. Dijkstra algorithm was used for site selection and the shortest escape path is searched by combining breadth-first algorithm and adjacency matrix. The simulation results show that the multi-agent simulation modeling method has great advantages in simulating the evolution of complex systems.

Data-Driven Modeling of Complex Business Process in Heterogeneous Environment of Healthcare Organization with Health Information Systems

Business process modeling aims to construct digital representations of processes being executed in the company. However, models derived from the event logs of their execution tend to overcomplicate the desired representation, making them difficult to apply. The most accurate recovery of the business process model requires a comprehensive study of the various artifacts stored in the company’s information system. This paper, however, aims to explore the possibility to automatically obtain the most accurate model of business process, using mutual optimization of models recovered from a set of event logs. Further, the obtained models are executed in multi-agent simulation model of company, and the resulting event logs are examined to determine patterns that are specific to distinct employees and those that generally characterize business process.