Agent-based simulation with NetLogo to evaluate ambient intelligence scenarios

In This Paper An Agent-Based Simulation Is Developed In Order To Evaluate An Ambient Intelligence Scenario Based On Agents. Many Ami Applications Are Implemented Through Agents But They Are Not Compared With Any Other Existing Alternative In Order To Evaluate The Relative Benefits Of Using Them. The Proposed Simulation Environment Analyses Such Benefits Using Two Evaluation Criteria: First, Measuring Agent Satisfaction Of Different Types Of Desires Along The Execution. Second, Measuring Time Savings Obtained Through A Correct Use Of Context Information. In This Paper An Existing Agent Architecture, An Ontology And A 12-Steps Protocol To Provide Ami Services In Airports, Is Evaluated Using The Netlogo Simulation Environment. In Our Netlogo Model We Are Considering Scalability Issues Of This Application Domain But Using Fipa And Bdi Extensions To Be Coherent With Our Previous Works And Our Previous Jade Implementation Of Them. The Netlogo Model Simulates An Airport With Agent &#39 Passengers&#39 Passing Through Several Zones Located In A Specific Order In A Map: Passport Controls, Check-In Counters Of Airline Companies, Boarding Gates, Different Types Of Shopping. Although The Initial Data In Each Simulation Is Generated Randomly, And The Model Is Just An Approximation Of Real-World Airports, The Definition Of This Case Of Use Of Ami Through Netlogo Agents Opens An Interesting Way To Evaluate The Benefits Of Using Ami, Which Is A Significant Contribution To The Final Development Of Ami Systems.This work was partially funded by CNPq PVE Project 314017/2013-5, FAPERJ APQ1 Project 211.500/2015 and by Projects MINECO TEC2012-37832-C02-01, CICYT TEC2011-28626-C02-0

K-Trek: A Peer-to-Peer Approach To Distribute Knowledge In Large Environments

In this paper, we explore an architecture, called K-Trek, that enables mobile users to travel across knowledge distributed over a large geographical area (ranging from large public buildings to a national park). Our aim is providing, dis-tributing, and enriching the environment with location-sensitive information for use by agents on board of mobile and static devices. Local interactions among K-Trek devices and the distribution of information in the larger environment adopt some typical peer-to-peer patterns and techniques. We introduce the architecture, discuss some of its potential knowledge management applications, and present a few experimental results obtained with simulation

How Resilient Are Our Societies? Analyses, Models, and Preliminary Results

Traditional social organizations such as those for the management of healthcare and civil defence are the result of designs and realizations that matched well with an operational context considerably different from the one we are experiencing today: A simpler world, characterized by a greater amount of resources to match less users producing lower peaks of requests. The new context reveals all the fragility of our societies: unmanageability is just around the corner unless we do not complement the "old recipes" with smarter forms of social organization. Here we analyze this problem and propose a refinement to our fractal social organizations as a model for resilient cyber-physical societies. Evidence to our claims is provided by simulating our model in terms of multi-agent systems.Comment: Paper submitted for publication in the Proc. of SERENE 2015 (http://serene.disim.univaq.it/2015/

Transmission Capacity as a Common-Pool Resource: The Case of Gas Interconnector Capacity

We investigated the very real problem of congestion at gas interconnectors. Instead of suggesting further incremental change to the European regulation in force to remedy congestion problems, we took a step back and consider gas interconnectors as a Common-Pool Resource (CPR). We suggest to wait and see what institutions the shippers let emerge to govern and manage interconnector capacity. To explore this idea, we developed a model to simulate the possible emergence of institutions that would coordinate the shippers and help overcome congestion. We simulate 40 shippers at the Dutch and Belgian interconnectors and allow them to autonomously book capacity. Agents can learn over time to improve their behaviour and coordinate with each other to collectively define a new institution in the system. The main simulator indicators are the observed booking behaviour, agent profits and emerging institutions. We present and discuss preliminary results from a set of simulation runs

Multi-Agent Based Simulation of an Unmanned Aerial Vehicles System

The rapid growth of using Unmanned Aerial Vehicles (UAV) for civilian and military applications has promoted the development of research in many areas. Most of the unmanned aerial vehicles in use are manually controlled. Often, UAVs require highly trained pilot operators. Hence, the main challenge faced by researchers has been to make UAVs autonomous or semiautonomous. The goal of this research project is to develop and implement a simulation for a user-defined environment allowing UAVs to maneuver in free environments and obstacle-laden environments using Boid's algorithm of flocking with obstacle avoidance. The users are permitted to analyze the maneuvering area and coverage efficiency of the UAVs and to dynamically change environments. This project makes use of Boid's flocking algorithm to generate different kinds of movements for the flying agents, enabling the user to analyze the effectiveness of patrolling in that particular scenario. The number of UAVs and the type of environment are set by the user. The set number of UAVs moves as a flock or swarm inside the set environment by using Boid's rules of flocking: cohesion, alignment, and separation. The coverage efficiency of the UAVs in that particular environment is reported based on the ratio between the area covered and the time when the search time reaches a threshold. The advantages and feasibilities of the approach are discussed with the simulation results

An Agent-based Decision Support for a Vaccination Campaign

We explore the Covid-19 diffusion with an agent-based model of an Italian region with a population on a scale of 1:1000. We also simulate different vaccination strategies. From a decision support system perspective, we investigate the adoption of artificial intelligence techniques to provide suggestions about more effective policies. We adopt the widely used multi-agent programmable modeling environment NetLogo, adding genetic algorithms to evolve the best vaccination criteria. The results suggest a promising methodology for defining vaccine rates by population types over time. The results are encouraging towards a more extensive application of agent-oriented methods in public healthcare policies