Behavioural validation of the ADACOR2 Self-organized holonic multi-agent manufacturing system

Global economy is driving manufacturing companies into a paradigm revolution. Highly customizable products at lower prices and with higher quality are among the most imposed influence factors. To respond properly to these external and internal constraints, such as work absence and machine failures, companies must be in a constant adaptation phase. Several manufacturing control architectures have been proposed throughout the years displaying more or less success to adapt into different manufacturing situations. These architectures follow different design paradigms but recently the decentralization and distribution of the processing power into a set of cooperating and collaborative entities is becoming the trend. Despite of the effort spent, there is still the need to empower those architectures with evolutionary capabilities and self-organization mechanisms to enable the constant adaption to disturbances. This paper presents a behavioural mechanism embed in the ADACOR2 holons. A validation procedure for this mechanism is also presented and results extracted. This validation is achieved through the use of a benchmark and results are compared with classical hierarchical and heterarchical architectures as also with the ADACOR.info:eu-repo/semantics/publishedVersio

Demonstration of Multi-agent Potential Fields in Real-time Strategy Game

Bots for Real Time Strategy (RTS) games provide a rich challenge to implement. A bot controls a number of units that may have to navigate in a partially unknown environment, while at the same time search for enemies and coordinate attacks to fight them down. Potential fields is a technique originating from the area of robotics where it is used in controlling the navigation of robots in dynamic environments. Although attempts have been made to transfer the technology to the gaming sector, assumed problems with efficiency and high costs for implementation have made the industry reluctant to adopt it. Our demo shows the use of Multi-agent Potential Fields (MAPF) in an open source RTS game. We will demonstrate both the potential fields as such, and the coordination of the agents

Demonstration of Multi-agent Potential Fields in Real-time Strategy Game

Bots for Real Time Strategy (RTS) games provide a rich challenge to implement. A bot controls a number of units that may have to navigate in a partially unknown environment, while at the same time search for enemies and coordinate attacks to fight them down. Potential fields is a technique originating from the area of robotics where it is used in controlling the navigation of robots in dynamic environments. Although attempts have been made to transfer the technology to the gaming sector, assumed problems with efficiency and high costs for implementation have made the industry reluctant to adopt it. Our demo shows the use of Multi-agent Potential Fields (MAPF) in an open source RTS game. We will demonstrate both the potential fields as such, and the coordination of the agents

Using Multi-Agent Potential Fields in Real-Time Strategy Games

Bots for Real Time Strategy (RTS) games provide a rich challenge to implement. A bot controls a number of units that may have to navigate in a partially unknown environment, while at the same time search for enemies and coordinate attacks to fight them down. Potential fields is a technique originating from the area of robotics where it is used in controlling the navigation of robots in dynamic environments. Although attempts have been made to transfer thetechnology to the gaming sector, assumed problems with efficiency and high costs for implementation have made the industry reluctant to adopt it. We present a Multi-agent Potential Field based bot architecture that is evaluated in a real time strategy game setting and compare it, both in terms of performance, and in terms of softer attributes such as configurability with other state-of-the-art solutions. Although our solution did not reach the performance standards of traditional RTS bots in the test, we see great unexploited benefits inusing multi-agent potential field based solutions in RTS games

A Multiagent Potential Field-Based Bot for Real-Time Strategy Games

Bots for real-time strategy (RTS) games may be very challenging to implement. A bot controls a number of units that will have to navigate in a partially unknown environment, while at the same time avoid each other, search for enemies, and coordinate attacks to fight them down. Potential fields are a technique originating from the area of robotics where it is used in controlling the navigation of robots in dynamic environments. Although attempts have been made to transfer the technology to the gaming sector, assumed problems with efficiency and high costs for implementation have made the industry reluctant to adopt it. We present a multiagent potential field-based bot architecture that is evaluated in two different real-time strategy game settings and compare them, both in terms of performance, and in terms of softer attributes such as configurability with other state-of-the-art solutions. We show that the solution is a highly configurable bot that can match the performance standards of traditional RTS bots. Furthermore, we show that our approach deals with Fog of War (imperfect information about the opponent units) surprisingly well. We also show that a multiagent potential field-based bot is highly competitive in a resource gathering scenario

A Study on Human like Characteristics in Real Time Strategy

Abstract — Computer controlled characters (NPCs) are important in any video game to make the game world interesting, give more depth to a game and make the game playable. In almost any game the player has to cooperate with, fight against or interact with NPCs. This is especially true for singleplayer games but NPCs are also important in most multi-player games. When creating NPCs the developers often strive to create human like characters that behave reasonably intelligent in most cases. We have performed a study aiming to give an idea of the characteristics of human like NPCs in real-time strategy (RTS) games. In the study participants were asked to watch a recording of an RTS game and decide and motivate if the players in the game were controlled by a human player or a computer. We recorded matches were human players played against bots as well as bots playing against other bots. The results were categorized into different groups and they showed that some characteristics, for example simultaneous movement, are perceived as very bot-like and other things such as ability to try different tactics are perceived as humanlike. I