Tuesday, March 3, 2009

Attending DECOI 2009

From February 23 until February 27, another researcher and me attended the 2009 International Workshop on Collective Intelligence and Evolution (DECOI). The main reason to participate in DECOI 2009 was too gain some more insight in multi-agent systems regarding the SAHNE project in which we are both involved. The venue for this year's workshop was in the Lorentz Center in Leiden, The Netherlands. Leiden is a beautiful small town around 30 Km from Amsterdam. Unfortunately, the work was such that we couldn't make it to see the town in day light.

DECOI is more a “school” than what is traditionally understood as a workshop. The program was distributed in key-note presentations and work in projects. The project details were given on the first day of the workshop, and each group of 4-6 people had to choose one of such projects to work on for the rest of the week. My group chose the project titled “Learning, communication and establishment of norms in pedestrian crowds”. This project was first introduced by Anders Johansson and, for the time being, the slides can be found here. In his lecture, Dr. Johansson introduced a basic model of pedestrian behaviour in crowds. The objective of the project was to incorporate “smarter” behaviour in the pedestrians so they would negotiate better how to avoid getting stuck in groups of pedestrians that are trying to get to the same place at the same time. The project involved first to theorise how this intelligent behaviour could be accomplished, and then to develop a simulation that showed how smarter pedestrians (agents) could obtain better results.

The simulations were developed with NetLogo, which is a Java-based multi-agent simulation tool that deploys its own language. None of the team members had used this tool before, so we not only faced the challenge to first write the simplistic model that Dr. Johansson presented in order to build on top of it, but also how to do this using a programming tool that we didn't know. For this reason, we spent 3 days learning and building this basic model, and the last two days writing the new smart features and gathering results. The features that we implemented were:

  • A grouping factor, that encourages groups of “friends” to get closer to each other on the search for the goal.

  • A learning factor, that tries to remember which direction worked best in the past and use it in the decision making

  • A rumour factor, which is spread by those pedestrians which are stuck in a crowd and that tries to prevent other pedestrians to go towards the direction of the crowd

The simulation takes place in a room with two entries and one exit. The entries and the exit are separated by doors whose position and size can be varied. The objective of every pedestrian in the simulation is to get to the exit door as soon as possible. In order to avoid overlapping with walls and other pedestrians, a set of “forces” in the form of vectors are implemented and sum to the gradient force that attracts pedestrians towards the exit. A na├»ve approach would generate a crowd that tries to approach the exit through the shortest path, getting stuck in the intermediary door. All the smart features that we implemented helped one way or another to increase the performance of the system. A summary of the strategies and results can be found here . A Java applet accessible here allows you to play the simulation directly from a web browser.

Tuesday, January 20, 2009

Aircraft management with multi-agent systems

Here at DIAL we have a project sponsored by The Boeing Company to develop a multi-agent system for emergent behavior in aircraft configuration control. In this project, codenamed SAHNE (Self-serving assets for higly networked environments), agents are the virtual counterparts of aircraft spare parts, and are autonomous in terms of decision making regarding their life-cycle. In SAHNE, we put special emphasis in the process of part replacement, and develop a fully autonomous system in which the agents detect when the parts need to be replaced (e.g. expiration date on life bests, worn out of mechanical components, etc) and initiate and coordinate the parts procurement. This later process involves finding suppliers, ask for quotes on the replacements and decide which supplier will better supply the needed part for an specific aircraft.

My main role in this project is to assess the integration of sensor data into the agent decisions. Until now, the development of the platform focused on creating a certain degree of functionality based on static data and software agents. For the second year of the project, we will use actual spare parts and capture technologies to feed real-time data into the software agents. The project is scheduled to run until 2010.