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Agent-based Simulation with Netlogo to Evaluate AmI Scenarios

J. Carbo, N. Sanchez, J. M. Molina

TL;DR

The paper addresses the challenge of evaluating Ambient Intelligence (AmI) in complex, privacy-sensitive settings by translating a previously defined AmI architecture (ontology, 12-step protocol) into a NetLogo-based agent simulation of an airport. It compares AmI-enabled versus non-AmI configurations to quantify benefits in two metrics: agent goal-satisfaction and time savings derived from context usage. The study reports that, under high population load, AmI scenarios yield about 18% faster flows and roughly 40% higher agent satisfaction, suggesting meaningful pragmatic gains from AmI in airport contexts. This work provides a scalable, reproducible framework for quantifying AmI advantages and guiding deployment decisions in large-scale, real-world environments.

Abstract

In this paper an agent-based simulation is developed in order to evaluate an AmI scenario based on agents. Many AmI applications are implemented through agents but they are not compared to any other existing alternative in order to evaluate the relative benefits of using them. The proposal simulation environment developed in Netlogo analyse 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. So, here, a previously suggested agent architecture, an ontology and a 12-steps protocol to provide AmI services in airports, is evaluated using a NetLogo simulation environment. The present work uses a NetLogo model considering scalability problems 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 presented simulates an airport with agent users 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 initial data in simulations are generated randomly, and the model is just an approximation of real-world airports, the definition of this case of use of Ambient Intelligence through NetLogo agents opens an interesting way to evaluate the benefits of using Ambient Intelligence, which is a significant contribution to the final development of them.

Agent-based Simulation with Netlogo to Evaluate AmI Scenarios

TL;DR

The paper addresses the challenge of evaluating Ambient Intelligence (AmI) in complex, privacy-sensitive settings by translating a previously defined AmI architecture (ontology, 12-step protocol) into a NetLogo-based agent simulation of an airport. It compares AmI-enabled versus non-AmI configurations to quantify benefits in two metrics: agent goal-satisfaction and time savings derived from context usage. The study reports that, under high population load, AmI scenarios yield about 18% faster flows and roughly 40% higher agent satisfaction, suggesting meaningful pragmatic gains from AmI in airport contexts. This work provides a scalable, reproducible framework for quantifying AmI advantages and guiding deployment decisions in large-scale, real-world environments.

Abstract

In this paper an agent-based simulation is developed in order to evaluate an AmI scenario based on agents. Many AmI applications are implemented through agents but they are not compared to any other existing alternative in order to evaluate the relative benefits of using them. The proposal simulation environment developed in Netlogo analyse 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. So, here, a previously suggested agent architecture, an ontology and a 12-steps protocol to provide AmI services in airports, is evaluated using a NetLogo simulation environment. The present work uses a NetLogo model considering scalability problems 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 presented simulates an airport with agent users 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 initial data in simulations are generated randomly, and the model is just an approximation of real-world airports, the definition of this case of use of Ambient Intelligence through NetLogo agents opens an interesting way to evaluate the benefits of using Ambient Intelligence, which is a significant contribution to the final development of them.
Paper Structure (8 sections, 6 figures, 1 table)

This paper contains 8 sections, 6 figures, 1 table.

Table of Contents

  1. Introduction
  2. Foundations
  3. Problem definition
  4. AmI in an airport
  5. Agent System architecture
  6. Agent simulation with NetLogo
  7. Results
  8. Conclusions

Figures (6)

  • Figure 1: Schema of the multi-agent architecture.
  • Figure 2: Followed steps by ingoing agents with AmI
  • Figure 3: Followed steps by outgoing agents with AmI
  • Figure 4: Description of elements in our NetLogo Model and initial parameter setup
  • Figure 5: Final outcome of a NetLogo simulation of the airpot
  • ...and 1 more figures