Multi‐agent modelling and simulation of graph‐based predator–prey dynamic systems: A BDI approach

Abstract

We propose a new framework based on Belief‐Desire‐Intention multi‐agent systems for the macroscopic modelling and simulation of continuous dynamic systems. The main idea is to break down the target system model into a collection of autonomous and loosely coupled interacting components endowed with clean message‐based interfaces and local intelligence. Each component is then mapped to a Belief‐Desire‐Intention agent that captures its state as a set of logical facts and its behavioural patterns as a set of plans. The system model can be described as a multi‐agent programme that is specified using the state‐of‐the‐art Jason agent‐oriented programming language. The approach is evaluated by considering a generalized graph‐based model of predator–prey systems. Our approach supports the configuration of the multi‐agent model with various differential equations integration methods, as required by the specific problem.

Grey Wolf Optimizer-Based Approaches to Path Planning and Fuzzy Logic-based Tracking Control for Mobile Robots

“…The vector 7) is the final (or target) point, it is the same for all agents. The vector X best,P R j (t) = (x best,P R j (t), y best,P R j (t)) ∈ R 2 is the position of best agent (as far as the distance between the current point and the target point is concerned) in the population P R j at time moment t. The weighting parameters in (7) are λ 1 > 0, λ 2 > 0 and λ 3 > 0.…”

“…The first term in (7) aims the minimization of the (Euclidian) agent-target position distances at a specific time (iteration) t. The second term in f i,P R k (X i,P R k (t)) aims the maximization of the distance between the agents of a population and the best agents from all other populations (for collision avoidance). The third and fourth terms in (7) aim the maximization of the distance between the robot paths on the x and y axes in order to model specific applications where one axis could be more important than the other one.…”

“…As considered in [63], [57], an additional penalty term α P aP l > 0 is added to the right-hand term in (7) but only in certain situations in step PaPl1. Therefore, such situations will be avoided as they lead to collisions when i th agent's position at iteration t is inside one of the obstacles or the line between the positions at iterations t and t − 1 intersects an obstacle.…”

“…nRobotic platform supports both simulated and real robots, however combinations of real and simulated robots are not supported. The evolutions of the o.f.s defined in (7) are illustrated in Figure 7 for the best agents along the interval 1...t max as an indication of algorithms' convergence. The results correspond to one of the simple experimental scenarios with two robots and one of the populations of agents, i.e., P R1 .…”

“…Constraints specific to technical and economical operating conditions in terms of various linear and nonlinear systems can be formulated [21], [60], [68] [15], [39], [45], [52]. Knowledge-based and fuzzy systems are included [58], [69], [7], [32].…”