scholarly journals Learning Swarm Behaviors using Grammatical Evolution and Behavior Trees

2019 ◽  
Author(s):  
Aadesh Neupane ◽  
Michael Goodrich
Keyword(s):  
Fitness Function ◽  
Grammatical Evolution ◽  
Individual Agent ◽  
Collective Behaviors ◽  
Fitness Functions ◽  
Swarm Behavior ◽  
Behavior Trees ◽  
Multi Agent ◽  
Nest Maintenance ◽  
And Behavior

Algorithms used in networking, operation research and optimization can be created using bio-inspired swarm behaviors, but it is difficult to mimic swarm behaviors that generalize through diverse environments. State-machine-based artificial collective behaviors evolved by standard Grammatical Evolution (GE) provide promise for general swarm behaviors but may not scale to large problems. This paper introduces an algorithm that evolves problem-specific swarm behaviors by combining multi-agent grammatical evolution and Behavior Trees (BTs). We present a BT-based BNF grammar, supported by different fitness function types, which overcomes some of the limitations in using GEs to evolve swarm behavior. Given human-provided, problem-specific fitness-functions, the learned BT programs encode individual agent behaviors that produce desired swarm behaviors. We empirically verify the algorithm's effectiveness on three different problems: single-source foraging, collective transport, and nest maintenance. Agent diversity is key for the evolved behaviors to outperform hand-coded solutions in each task.

Development of Ghost Controller for Ms Pac-Man Versus Ghost Team with Grammatical Evolution

2013 ◽  
Vol 17 (6) ◽  
pp. 904-912 ◽  
Author(s):  
Kenji Tamura ◽  
◽  
Takashi Torii ◽  
Keyword(s):  
Artificial Intelligence ◽  
Experimental Data ◽  
Video Games ◽  
Video Game ◽  
Fitness Function ◽  
Grammatical Evolution ◽  
Multi Agent System ◽  
Evolutionary System ◽  
Game Ai ◽  
Multi Agent

These days, artificial intelligence (AI) has been used in game AI. Additionally, video game AI is studied actively in late years, for example, application of commercial game or competition etc. In many video games of recent years, real-time action and non-player characters have been required to attract players. This paper describes how to develop a ghost team controller using evolutionary system to play the video game, Ms Pac-Man. Ms Pac-Man has been used as a testbed of AI, especially multi-agent system. We propose a method to generate the ghost team controller with Grammatical Evolution. In case of developingMs Pacman agent with Evolutionary Computation using fitness function, the criterion of the fitness is used its obtained high score in many cases. In contrast, ghost team has to prevent Ms Pac-man to get high score, namely hold score in check. However, if Ms Pacman is captured in low score by accident, its ghost strategy have a possibility to survive next generation, and if the ghosts pursue Ms Pac-man in a line, agent isn’t captured for all time. Therefore developing ghost team agent is required to avoid these issues, and we introduced a penalty to the fitness, grammar like instinct and to attack Ms Pac-Man on both sides. This paper introduces experimental data about the ghost team controller for Ms Pac-Man versus ghost team, we used ghost team agents and tested them Ms Pac-Man agents. The experimental results showed that proposed system could catchMs Pac-Man agent compare with simple hand-coded ghost teams, and the evolved controller we made worked effectively. These results are concluded that proposed method works effectively for generating ghost controller.

Modeling of Distribution Service Network System in Comprehensive Passenger Transportation Hub

2012 ◽  
Vol 253-255 ◽  
pp. 1195-1200
Author(s):  
Zhe Zhang ◽  
Chang Xu Ji ◽  
Mao Jing Jin ◽  
Qian Li ◽  
Lifen Yun ◽  
...  
Keyword(s):  
Dynamic Method ◽  
Network System ◽  
Service Network ◽  
Individual Agent ◽  
Nonlinear Characteristics ◽  
Passenger Transportation ◽  
Environment And Behavior ◽  
Multi Agent ◽  
And Behavior ◽  
Structure Environment

Distribution service network system (DSNS) has dynamic, open, pop and other nonlinear characteristics in terms of structure, environment, and behavior as a subsystem of comprehensive passenger transportation hub system (CPTH). It is a typical complex system. The author putted forward the adaptability of DSNS based on the analysis of complexity of DSNS and analyzed the driving force of adaptability. The model of individual agent and self-adaptive control model of DSNS were designed to provide a dynamic method for adjusting control strategy based on multi-Agent method and CAS theory.

Search Strategies in Evolutionary Multi-Agent Systems: The Effect of Cooperation and Reward on Solution Quality

2011 ◽  
Vol 133 (6) ◽  
Author(s):  
Lindsay Hanna Landry ◽  
Jonathan Cagan
Keyword(s):  
Fitness Function ◽  
Three Dimensional ◽  
Complex Problem ◽  
Multi Agent Systems ◽  
Solution Quality ◽  
Agent Systems ◽  
Fitness Functions ◽  
Engineering Design Problems ◽  
Multi Agent ◽  
Improved Performance

Cooperation and reward of strategic agents in an evolutionary optimization framework is explored in order to better solve engineering design problems. Agents in this Evolutionary Multi-Agent Systems (EMAS) framework rely on one another to better their performance, but also vie for the opportunity to reproduce. The level of cooperation and reward is varied by altering the amount of interaction between agents and the fitness function describing their evolution. The effect of each variable is measured using the problem objective function as a metric. Increasing the amount of cooperation in the evolving team is shown to lead to improved performance for several multimodal and complex numerical optimization and three-dimensional layout problems. However, fitness functions that utilize team-based rewards are found to be inferior to those that reward on an individual basis. The performance trends for different fitness functions and levels of cooperation remain when EMAS is applied to the more complex problem of three-dimensional packing as well.

Beyond Interactive Evolution: Expressing Intentions Through Fitness Functions

Leonardo ◽  
2016 ◽  
Vol 49 (3) ◽  
pp. 251-256 ◽  
Author(s):  
Penousal Machado ◽  
Tiago Martins ◽  
Hugo Amaro ◽  
Pedro H. Abreu
Keyword(s):  
Fitness Function ◽  
Interactive Art ◽  
Support Tool ◽  
Generative Art ◽  
Fitness Functions ◽  
Meta Level ◽  
Evolutionary Art ◽  
Creativity Support ◽  
The Creation ◽  
The Impact

Photogrowth is a creativity support tool for the creation of nonphotorealistic renderings of images. The authors discuss its evolution from a generative art application to an interactive evolutionary art tool and finally into a meta-level interactive art system in which users express their artistic intentions through the design of a fitness function. The authors explore the impact of these changes on the sense of authorship, highlighting the range of imagery that can be produced by the system.

A Geometric Approach to Dynamically Feasible, Real-Time Formation Control

2011 ◽  
Vol 133 (2) ◽  
Author(s):  
D. H. A. Maithripala ◽  
D. H. S. Maithripala ◽  
S. Jayasuriya
Keyword(s):  
Real Time ◽  
Wide Class ◽  
Formation Control ◽  
Geometric Approach ◽  
Control Problems ◽  
Multi Agent Systems ◽  
Individual Agent ◽  
Planning Algorithm ◽  
Explicit Consideration ◽  
Multi Agent

We propose a framework for synthesizing real-time trajectories for a wide class of coordinating multi-agent systems. The class of problems considered is characterized by the ability to decompose a given formation objective into an equivalent set of lower dimensional problems. These include the so called radar deception problem and the formation control problems that fall under formation keeping and/or formation reconfiguration tasks. The decomposition makes the approach scalable, computationally economical, and decentralized. Most importantly, the designed trajectories are dynamically feasible, meaning that they maintain the formation while satisfying the nonholonomic and saturation type velocity and acceleration constraints of each individual agent. The main contributions of this paper are (i) explicit consideration of second order dynamics for agents, (ii) explicit consideration of nonholonomic and saturation type velocity and acceleration constraints, (iii) unification of a wide class of formation control problems, and (iv) development of a real-time, distributed, scalable, computationally economical motion planning algorithm.

Dynamic Correcting Dispersion Parameters of Lagrangian Puff Model in Atmospheric Tracer Experiments

2014 ◽  
Author(s):  
Yuanwei Ma ◽  
Dezhong Wang ◽  
Zhilong Ji ◽  
Nan Qian
Keyword(s):  
Numerical Simulation ◽  
Dispersion Model ◽  
Fitness Function ◽  
Atmospheric Dispersion ◽  
Correction Procedure ◽  
Model Errors ◽  
Dispersion Parameters ◽  
Fitness Functions ◽  
Atmospheric Dispersion Models ◽  
Atmospheric Tracer

In atmospheric dispersion models of nuclear accident, the empirical dispersion coefficients were obtained under certain experiment conditions, which is different from actual conditions. This deviation brought in the great model errors. A better estimation of the radioactive nuclide’s distribution could be done by correcting coefficients with real-time observed value. This reverse problem is nonlinear and sensitive to initial value. Genetic Algorithm (GA) is an appropriate method for this correction procedure. Fitness function is a particular type of objective function to achieving the set goals. To analysis the fitness functions’ influence on the correction procedure and the dispersion model’s forecast ability, four fitness functions were designed and tested by a numerical simulation. In the numerical simulation, GA, coupled with Lagrange dispersion model, try to estimate the coefficients with model errors taken into consideration. Result shows that the fitness functions, in which station is weighted by observed value and by distance far from release point, perform better when it exists significant model error. After performing the correcting procedure on the Kincaid experiment data, a significant boost was seen in the dispersion model’s forecast ability.

scholarly journals Multi-agent systems for cooperative ITS

2021 ◽  
pp. 42-50
Author(s):  
Roman Dushkin
Keyword(s):  
Transport Systems ◽  
Transport Sector ◽  
Multi Agent Systems ◽  
Automated Vehicles ◽  
Intelligent Transport Systems ◽  
Individual Agent ◽  
Intelligent Transport ◽  
Agent Systems ◽  
Interacting Agents ◽  
Multi Agent

This article presents an original perspective upon the problem of creating intelligent transport systems in the conditions of using highly automated vehicles that freely move on the urban street-road networks. The author explores the issues of organizing a multi-agent system from such vehicles for solving the higher level tasks rather than by an individual agent (in this case – by a vehicle). Attention is also given to different types of interaction between the vehicles or vehicles and other agents. The examples of new tasks, in which the arrangement of such interaction would play a crucial role, are described. The scientific novelty is based on the application of particular methods and technologies of the multi-agent systems theory from the field of artificial intelligence to the creation of intelligent transport systems and organizing free-flow movement of highly automated vehicles. It is demonstrated the multi-agent systems are able to solve more complex tasks than separate agents or a group of non-interacting agents. This allows obtaining the emergent effects of the so-called swarm intelligence of the multiple interacting agents. This article may be valuable to everyone interested in the future of the transport sector.

A Multiple Objective Fitness Function Design Scheme for GA-Based Cognitive Engines

2015 ◽  
Vol 764-765 ◽  
pp. 444-447
Author(s):  
Keun Hong Chae ◽  
Hua Ping Liu ◽  
Seok Ho Yoon
Keyword(s):  
Genetic Algorithm ◽  
Parameter Optimization ◽  
Fitness Function ◽  
Multiple Objective ◽  
Cognitive Engine ◽  
Fitness Functions ◽  
Design Scheme ◽  
Transmission Parameter ◽  
Function Design ◽  
Single Objective

In this paper, we propose a multiple objective fitness function for cognitive engines by using the genetic algorithm (GA). Specifically, we propose four single objective fitness functions, and finally, we propose a multiple objective fitness function based on the single objective fitness functions for transmission parameter optimization. Numerical results demonstrate that we can obtain transmission parameter sets optimized for given transmission scenarios with the GA-based cognitive engine incorporating the proposed objective fitness function.

scholarly journals Pixel-Based Approach for Generating Original and Imitating Evolutionary Art

Electronics ◽  
2020 ◽  
Vol 9 (8) ◽  
pp. 1311
Author(s):  
Yuchen Wang ◽  
Rong Xie
Keyword(s):  
Fitness Function ◽  
Visual Complexity ◽  
Generation Process ◽  
Single Object ◽  
Von Neumann ◽  
Modified Particle Swarm Optimization ◽  
Fitness Functions ◽  
Evolutionary Art ◽  
Artistic Styles ◽  
The Aesthetic

We proposed a pixel-based evolution method to automatically generate evolutionary art. Our method can generate diverse artworks, including original artworks and imitating artworks, with different artistic styles and high visual complexity. The generation process is fully automated. In order to adapt to the pixel-based method, a von Neumann neighbor topology-modified particle swarm optimization (PSO) is employed to the proposed method. The fitness functions of PSO are well prepared. Firstly, we come up with a set of aesthetic fitness functions. Next, the imitating fitness function is designed. Finally, the aesthetic fitness functions and the imitating fitness function are weighted into one single object function, which is used in the modified PSO. Both the original outputs and imitating outputs are shown. A questionnaire is designed to investigate the subjective aesthetic feeling of proposed evolutionary art, and the statistics are shown.

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