Figure 3 - uploaded by Costin Badica
Content may be subject to copyright.
Source publication
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 intelligen...
Contexts in source publication
Context 1
... n} represent species and each arc (i, j) ∈ E represents a predator-prey relationship where species i predates species j. An example graph is presented in Figure 3. In this example species 1 predates species 2 and 3, while species 2 predates species 3. ...
Context 3
... this section we present an experiment in- volving a multi-agent system that simulates the predator-prey model that we introduced in Sec- tion 4. There are three populations in this model and they exhibit predator-prey relationships ac- cording to the graph from Figure 3. The starting point of our modeling is the set of equations (13). ...
Similar publications
The EMMA Modular Control Concept (EMCC) is a Java framework for controlling EMMA2, an autonomous,
mobile, learning and vision enabled robot, designed by the RoboLab. With the help of a highly
modular framework, the implementation of robot controllers serving a wide range of purposes becomes a
simple and user-friendly task that can be can be accompl...
Citations
... Agent-Oriented Programming (AOP) languages [31] offer such rich architectures through logic based programming constructs that are both verifiable and explainable [32]. Recently, there has been a resurgence of interest in using AOP for simulation [33][34][35][36]. Other work has explored how to support discrete event simulation using AOP. ...
This paper presents a vision for a new breed of Agent-Based Simulations that are based on the principles of the current generation web technologies. We propose a novel approach to implementing complex agent-based simulations built from loosely-coupled reusable components in a manner that ensures scalability. This vision is inspired by the emergence of the recently proposed hypermedia multi-agent systems concept, which combines hypermedia systems, semantic web and affordances.
... As a starting point, the control parameters are assigned as a generic "predator-prey" model (Bădică et al., 2018;Karsai et al., 2016) which is interpreted in the current context as a kinetic model where the predator is the bacteria and the prey is the plastic. Furthermore, a plastic agent remains fairly static whereas a bacterium is more mobile, performing a random walk at a given "speed" (SP) until a plastic agent comes within its "detect distance" (DD). ...
In this paper we describe the functionality of a decision support modelling approach to select appropriate biomaterial blends depending on their mechanical/chemical properties on the one hand, and their biodegradation behaviour, on the other. Firstly, a Case Based Reasoning (CBR) approach is applied to predict expected biodegradation behaviour over time, based on historical examples and using a weighted distance metric on the material properties in order to calculate the trend curve of the new case. Secondly, a Multi-Agent System (MAS) is applied to dynamically simulate the biodegradation curve, in which the two main agents, bacteria and plastic, interact to reproduce the biodegradation kinetics over time. The results of the interpolation are very promising with a good approximation to the real curve time series and % biodegradation, and the Multi-Agent System successfully simulates the different trend curves over time. The system has been confirmed as useful by materials expert end-users, who participated in the project, in order to evaluate a priori new blends “in silico”, and identify and select the most promising, before conducting the long duration biodegradation experiments in the real environment.
... Related models of diffusion processes were investigated using multi-agent simulation, including agent-based emotion contagion [4], information propagation in large teams [11] and BDI models for predator-prey systems [6]. ...
This paper proposes a multi-agent system for modeling and simulation of epidemics spread management strategies. The core of the proposed approach is a generic spatial Susceptible-Infected-Recovered stochastic discrete system. Our model aims at evaluating the effect of prophylactic and mobility limitation measures on the impact and magnitude of the epidemics spread. The paper introduces the modeling approach and, next, it proceeds to the development of a multi-agent simulation system. The proposed system is implemented and evaluated using the GAMA multi-agent platform, using several simulation scenarios, while the experimental results are discussed in detail. Our model is abstract and well defined, making it very suitable as a starting point for extension and application to more detailed models of the specific problems.
... Authors in Bădică et al. (2018) present general framework for modelling and simulation continuous dynamic systems with Belief-Desire-Intention paradigm applied and use of Jason AOP language. Each agent was described as a software module that provides an interface with the external world and consists of three components: belief base, plan library and reasoning engine. ...
The procedures based on simulation have become a feasible testing method that does not require investing valuable resources to create a concrete prototype, especially with the increasing computational power of computers. Thus, design changes can be adopted and design errors can be fixed before it is too late. Simulation turns to be a cheap, safe and often more acceptable from an ethical perspective. In our work we summarize the results from the analysis with the help of a computational simulation of an elementary, yet analytically intractable problem scenario from the field of ecology. Our main goal is to confirm that even with a seemingly simple agent-based model and simulation, one could obtain plausible results regarding a system’s real life behavior. As a last point, we propose an efficient alternative for analysis, rather than the expensive simulation process.
... 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 . ...
... The GWO-based approach to tracking fuzzy control was next validated using the simplified dynamic model (20) of several NWMRs with two degrees of freedom that is integrated in the nRobotic platform, and the control system structure given in Figure 3. Accepting in one of the experimental scenarios that [59] the obstacles are placed at (3,3), (9,3), (6,7), the initial position of the robot is (10,4), and the goal (or the desired / final position) is placed at (6,11), one typical reference trajectory (x r , y r ) produced by the GWO-based PaPl approach will be presented as follows. All positions are expressed in m. ...
... 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]. ...
This paper proposes two applications of Grey Wolf Optimizer (GWO) algorithms to a path planning (PaPl) problem and a Proportional-Integral (PI)-fuzzy controller tuning problem. Both optimization problems solved by GWO algorithms are explained in detail. An off-line GWO-based PaPl approach for Nonholonomic Wheeled Mobile Robots (NWMRs) in static environments is proposed. Once the PaPl problem is solved resulting in the reference trajectory of the robots, the paper also suggests a GWO-based approach to tune cost-effective PI-fuzzy controllers in tracking control problem for NWMRs. The experimental results are demonstrated through simple multiagent settings conducted on the nRobotic platform developed at the Politehnica University of Timisoara, Romania, and they prove both the effectiveness of the two GWO-based approaches and major performance improvement.
... We open the field in two directions. First, we move the modelling of immune struggles from the realm of ordinary-differential equation models (Álvarez et al., 2017) to the modelling by multi-agent simulations (Bȃdicȃ et al., 2018;Karsai et al., 2016;Zhao and Jin, 2005). We highlight that a https://orcid.org/0000-0002-5852-7716 ...
... This advantage of multi-agent systems has been illustrated in the field of ecology (Bȃdicȃ et al., 2018) and other specific disciplines (Montagna and Viroli, 2010;Montagna et al., , 2016. Multi-agent systems have been used to explore what-if scenarios of environments that combine cell proliferation and random walks of cells on regular lattices, reactions for nutrients and growth factors (Figueredo et al., 2013a). ...
There is exciting news in recent developments suggesting the potential to treat some human cancers by stimulating the patients own immune system. However, there is still much to understand; therefore, modelling the battle between those cells that are constituents of the human immune system against tumorous cells can significantly provide insights as mathematical modelling has done regarding the immune system behaviour against virus infections. In this paper we innovate in two directions. First, we move the modelling of immune struggles from the sphere of ordinary-differential equation models to the modelling by multi-agent simulations. We highlight the advantages of the multi-agent simulation, for example the consideration of elaborate spatial proximity interactions. Secondly, we move away from the realm of infectious diseases to the complex modelling of the stimulation of T-Cells and their participation in fighting cancerous cell tumours.
... Other researchers were interested in exploiting DEVS modularity features to enhance agent-based simulation models [1,3,8,16,22]. Finally, BDI agents were also proposed as a promising approach for enhancing simulation models of continuous and discrete dynamic systems [2,5,6,15]. ...
In this paper we propose a method for mapping DEVS simulation models to BDI multi-agent systems. Our work opens the possibility of reusing BDI multi-agent frameworks for the modeling and simulation of discrete event dynamic systems following the DEVS formalized approach. Moreover, thanks to the key features of the BDI architecture, we are are now able to systematically enhance simulation models with cognitive and intelligence aspects specific to BDI agents. Our method is demonstrated with the help of a simple, yet illustrative example that we developed using the Jason multi-agent systems development platform.
