Decision-Making and Tactical Behavior with Potential Fields

Meyer, Jens; Adolph, R.A.; Stephan, Daniel; Daniel, Andreas; Seekamp, Matthias; Weinert, Volker; Visser, Ubbo

doi:10.1007/978-3-540-45135-8_25

Cited by 5 publications

(6 citation statements)

References 4 publications

(3 reference statements)

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“…This division is due to the fact that not all the decisions necessarily lead to a physical action or reaction whereas there are decisions for higher levels of intelligence [8]. Having said that, in the proposed system the decisions leading to a physical action are called lowlevel decisions and all other decisions are called high-level decisions.…”

Section: Decision Layermentioning

confidence: 98%

An approach to design a robust software architecture and an intelligent model for multi-agent systems

Norouzi

Acosta

2013

2013 3rd Joint Conference of AI &Amp; Robotics and 5th RoboCup Iran Open International Symposium

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A successful multi-agent system requires the intelligent agents to perform within a dynamically complex environment where proper and quick response in a cooperative manner is a primary key to successfully complete a task. This paper proposes a non-deterministic decision making method using electric fields and high-level decision making. Different layers are designed, defined, and implemented for the software architecture with focus on system adaptability, sustainability, and optimization. Consequently, a software architecture is proposed in this paper to complement the AI algorithms. The proposed architecture aims to provide a well-structured and managed system for control, behavior, and decision making of multi-agent systems. The proposed decision making approach in this paper is based on layered artificial intelligence implemented using vectorbased fuzzy electric fields and a decision tree. Furthermore, an approach to model the world which, in this paper, is called Agent Relative Polar Localization is introduced. This world model is based on fuzzy measurements and polar coordinates. In order to optimize the overall performance of the system learning methods have been introduced to the system. The proposed system in this paper has been implemented on soccer robots to evaluate the performance of the system. The results show that the proposed system implemented on the soccer robots is reliable and robust.

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Section: Decision Layermentioning

confidence: 98%

An approach to design a robust software architecture and an intelligent model for multi-agent systems

Norouzi

Acosta

2013

2013 3rd Joint Conference of AI &Amp; Robotics and 5th RoboCup Iran Open International Symposium

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“…There is a rich selection of behaviour types that the agent can select from, which depend on the tactics and strategic characteristics, and also on the agent's role in a formation, position of the ball and the other players. A description can be found in reference [82]. A detailed analysis of multi-agent decision making is however beyond the scope of this paper, see references [83] and [84].…”

Section: Multi-agent Decision Processesmentioning

confidence: 99%

Autonomous vehicle control systems — a review of decision making

Veres

Molnár

Lincoln

et al. 2011

Proceedings of the Institution of Mechanical Engineers, Part I:

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A systematic review is provided on artificial agent methodologies applicable to control engineering of autonomous vehicles and robots. The paper focuses on some fundamentals that make a machine autonomous: decision making that involves modelling the environment and forming data abstractions for symbolic processing and logic-based reasoning. Most relevant capabilities such as navigation, autonomous path planning, path following control, and communications, that directly affect decision making, are treated as basic skills of agents. Although many autonomous vehicles have been engineered in the past without using the agentoriented approach, most decision making onboard of vehicles is similar to or can be classified as some kind of agent architecture, even if in a naïve form. First the ANSI standard of intelligent systems is recalled then a summary of the fundamental types of possible agent architectures for autonomous vehicles are presented, starting from reactive, through layered, to advanced architectures in terms of beliefs, goals, and intentions. The review identifies some missing links between computer science results on discrete agents and engineering results of continuous world sensing, actuation, and path planning. In this context design tools for 'abstractions programming' are identified as needed to fill in the gap between logic-based reasoning and sensing. Finally, research is reviewed on autonomous vehicles in water, on the ground, in the air, and in space with comments on their methods of decision making. One of the main conclusions of this review is that standardization of decision making through agent architectures is desirable for the future of intelligent vehicle developments and their legal certification.

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“…Potential field based team control is also popular outside of the SSL, in the mid-size [34], simulation [27] and Sony AIBO legged leagues [20]. Potential fields are used to determine target field positions for moving, or kicking.…”

Section: Related Workmentioning

confidence: 99%

STP: Skills, tactics, and plays for multi-robot control in adversarial environments

Browning

Bruce

Bowling

et al. 2005

Proceedings of the Institution of Mechanical Engineers, Part I:

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In an adversarial multi-robot task, such as playing robot soccer, decisions for team and single robot behavior must be made quickly to take advantage of short-term fortuitous events when they occur. When no such opportunities exist, the team must execute sequences of coordinated action across team members that increases the likelihood of future opportunities. We have developed a hierarchical architecture, called STP, to control an autonomous team of robots operating in an adversarial environment. STP consists of Skills for executing the low-level actions that make up robot behavior, Tactics for determining what skills to execute, and Plays for coordinating synchronized activity amongst team members. Our STP architecture combines each of these components to achieve autonomous team control. Moreover, the STP hierarchy allows for fast team response in adversarial environments while carrying out actions with longer goals. In this article, we present our STP architecture for controlling an autonomous robot team in a dynamic adversarial task that allows for coordinated team activity towards long-term goals, with the ability to respond rapidly to dynamic events. Secondly, we present the sub-component of skills and tactics as a generalized, single-robot control hierarchy for hierarchical problem decomposition with flexible control policy implementation and reuse. Thirdly, we contribute our play techniques as a generalized method for encoding and synchronizing team behavior, providing multiple competing team responses, and for supporting effective strategy adaptation against opponent teams. STP has been fully implemented on a robot platform and thoroughly tested against a variety of unknown opponent teams under in a number of RoboCup robot soccer competitions. We present these competition results as a mechanism to analyze the performance of STP in a real setting.

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Decision-Making and Tactical Behavior with Potential Fields

Cited by 5 publications

References 4 publications

An approach to design a robust software architecture and an intelligent model for multi-agent systems

An approach to design a robust software architecture and an intelligent model for multi-agent systems

Autonomous vehicle control systems — a review of decision making

STP: Skills, tactics, and plays for multi-robot control in adversarial environments

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