Paper Info
Title
Heuristiscs-Based High-Level Strategy for Multi-agent Systems
Abstract
In this paper, a high-level strategy concept is presented for robot soccer, based on low level heuristic inference methods, rather than explicit rule-based strategy. During tactical positioning, no strict role set is assigned for the agents, instead a fitting point of the role-space is selected dynamically. The algorithm for this approach applies fuzzy logic. We compute fields-of-quality, regarding some relevant aspects of the scenario, and integrate them into one decision-field, according to given strategic parameters (used as weights). The most relevant locations are derived from the decision-field through subtractive clustering, and the agents are allocated to these locations, as their desired positions, according to their significance and their cost of reaching the given target. If an agent is in a position to manipulate the ball, an appropriate action is being selected for it. The simulation and experiments prove that the proposed approach can be efficient in dynamically changing environment or against opponents of different strategies.
Year
DOI
Venue
2008
10.1007/978-3-540-87536-9_72
ICANN (1)
Keywords
Field
DocType
fuzzy logic,relevant aspect,high-level strategy concept,fitting point,different strategy,heuristiscs-based high-level strategy,appropriate action,relevant location,low level heuristic inference,explicit rule-based strategy,multi-agent systems,rule based,multi agent system
Heuristic,Inference,Computer science,Fuzzy logic,Subtractive clustering,Multi-agent system,Artificial intelligence,Robot,Role set,Machine learning,Tactical planning
Conference
Volume
ISSN
Citations 
5163
0302-9743
0
PageRank 
References 
Authors
0.34
4
2
Name
Order
Citations
PageRank
Péter Gasztonyi100.34
István Harmati2446.49