Optimal Data Structures for Spatially Localised Agent-Based Automata and Hybrid Systems
نویسندگان
چکیده
Agent-based systems and cellular automata are two closely related model formulations that are heavily used in studying complex systems. They are both formulated as microscopically simple rule-based models that are applied to individual cells or agents in a collection, where the spatially localised neighbourhood of other cells or agents is used as input to update each one. We have experimented with a range of models including classic cellular automata, through more sophisticated multi-state automata, flocking models, and stochastic-agent models and animat agent-based predator-prey models. We discuss algorithmic commonalities and code implementation patterns that have emerged as common properties of these models and describe how we have experimented with optimal data structures to support spatially localised models of this class. We show how concurrency and model correctness issues are affected by different data structures in addition to their effect on model update computational efficiency.
منابع مشابه
Improving Agent Performance for Multi-Resource Negotiation Using Learning Automata and Case-Based Reasoning
In electronic commerce markets, agents often should acquire multiple resources to fulfil a high-level task. In order to attain such resources they need to compete with each other. In multi-agent environments, in which competition is involved, negotiation would be an interaction between agents in order to reach an agreement on resource allocation and to be coordinated with each other. In recent ...
متن کاملUtilizing Generalized Learning Automata for Finding Optimal Policies in MMDPs
Multi agent Markov decision processes (MMDPs), as the generalization of Markov decision processes to the multi agent case, have long been used for modeling multi agent system and are used as a suitable framework for Multi agent Reinforcement Learning. In this paper, a generalized learning automata based algorithm for finding optimal policies in MMDP is proposed. In the proposed algorithm, MMDP ...
متن کاملA class of multi-agent discrete hybrid non linearizable systems: Optimal controller design based on quasi-Newton algorithm for a class of sign-undefinite hessian cost functions
In the present paper, a class of hybrid, nonlinear and non linearizable dynamic systems is considered. The noted dynamic system is generalized to a multi-agent configuration. The interaction of agents is presented based on graph theory and finally, an interaction tensor defines the multi-agent system in leader-follower consensus in order to design a desirable controller for the noted system. A...
متن کاملUsing Interval Petri Nets and Timed Automata for Diagnosis of Discrete Event Systems (DES)
A discrete event system (DES) is a dynamic system that evolves in accordance with the abrupt occurrence, at possibly unknown irregular intervals, of physical events. Because of the special nature of these systems, different tools are currently used for their analysis, design and modeling. The main focus of this paper is the presentation of a new modeling approach of Discrete Event Systems. The ...
متن کاملSequential and Mixed Genetic Algorithm and Learning Automata (SGALA, MGALA) for Feature Selection in QSAR
Feature selection is of great importance in Quantitative Structure-Activity Relationship (QSAR) analysis. This problem has been solved using some meta-heuristic algorithms such as: GA, PSO, ACO, SA and so on. In this work two novel hybrid meta-heuristic algorithms i.e. Sequential GA and LA (SGALA) and Mixed GA and LA (MGALA), which are based on Genetic algorithm and learning automata for QSAR f...
متن کامل