A Genetic Programming-Based Low-Level Instructions Robot for Realtimebattle

Abstract

RealTimeBattle is an environment in which robots controlled by programs fight each other. Programs control the simulated robots using low-level messages (e.g., turn radar, accelerate). Unlike other tools like Robocode, each of these robots can be developed using different programming languages. Our purpose is to generate, without human programming or other intervention, a robot that is highly competitive in RealTimeBattle. To that end, we implemented an Evolutionary Computation technique: Genetic Programming. The robot controllers created in the course of the experiments exhibit several different and effective combat strategies such as avoidance, sniping, encircling and shooting. To further improve their performance, we propose a function-set that includes short-term memory mechanisms, which allowed us to evolve a robot that is superior to all of the rivals used for its training. The robot was also tested in a bout with the winner of the previous “RealTimeBattle Championship”, which it won. Finally, our robot was tested in a multi-robot battle arena, with five simultaneous opponents, and obtained the best results among the contenders.

Discovering Stick-Slip-Resistant Servo Control Algorithm Using Genetic Programming

“…A relatively small part of the GP training attempts results in a fitness function value better than the best one obtained by the plain PID control, even though the number of generations is large. Indeed, the number of generations amounts to 500 here ( Table 2 ) while, e.g., a training based on not more than 20 generations was sufficient to discover successful strategies for robot RealTimeBattle [ 26 ]. However, the large number of generations is really required in this experiments, as the generations evolve slowly and often do not stagnate even after the 500 stages ( Figure 8 ).…”

“…The GP is also used in mechatronics, but not so often [ 24 , 25 , 26 ]. It may be a result of getting used to parametric models of mechatronic systems.…”

Validation of an Aesthetic Assessment System for Commercial Tasks

“…The metrics used by the system are metrics obtained through genetic programming [ 29 ], artificial neural networks (ANN) [ 30 , 31 ] and ad-hoc metrics [ 32 ].…”

Current trend in control of artificial intelligence for health robotic manipulator

“…Robots are designed and controlled according to the desired and required characteristics or behaviors. The robot design is adapted to functions that can measure the ability to adapt to the environment, without programming specific behaviors as known as robotic evolution [5]. Robotic evolution produced an optimal robotic innovation in the robotic system or its subsystem, morphology, kinematics, and control.…”