NS Seminar

Date and Location

Jun 08, 2018 - 1:00pm to 2:00pm
Bldg 434, room 122


GIS and agent-based models for humanitarian assistance (presented by Su Burtner, Geography)

Crooks, A. T., & Wise, S. (2013). GIS and agent-based models for humanitarian assistance. Computers, Environment and Urban Systems, 41, 100-111.

Natural disasters such as earthquakes and tsunamis occur all over the world, altering the physical landscape and often severely disrupting people’s daily lives. Recently researchers’ attention has focused on using crowds of volunteers to help map the damaged infrastructure and devastation caused by natural disasters, such as those in Haiti and Pakistan. This data is extremely useful, as it is allows us to assess damage and thus aid the distribution of relief, but it tells us little about how the people in such areas will react to the devastation. This paper demonstrates a prototype spatially explicit agent-based model, created using crowdsourced geographic information and other sources of publicly available data, which can be used to study the aftermath of a catastrophic event. The specific case modelled here is the Haiti earthquake of January 2010. Crowdsourced data is used to build the initial populations of people affected by the event, to construct their environment, and to set their needs based on the damage to buildings. We explore how people react to the distribution of aid, as well as how rumours relating to aid availability propagate through the population. Such a model could potentially provide a link between socio-cultural information about the people affected and the relevant humanitarian relief organizations.


Swarm Reinforcement Learning for traffic signal control based on cooperative multi-agent framework (presented by Roman Aguilera, Computer Science)

Tahifa, M., Boumhidi, J., & Yahyaouy, A. (2015, March). Swarm reinforcement learning for traffic signal control based on cooperative multi-agent framework. In Intelligent Systems and Computer Vision (ISCV), 2015 (pp. 1-6). IEEE.

Congestion, accidents, pollution, and many other problems resulting from urban traffic are present every day in most cities around the world. The growing number of traffic lights in intersections needs efficient control, and hence, automatic systems are essential nowadays for optimally tackling this task. Agent based technologies and reinforcements learning are largely used for modelling and controlling intelligent transportation systems, where agents represent a traffic signal controller. Each agent learns to achieve its goal through many episodes. With a complicated learning problem, it may take much computation time to acquire the optimal policy. In this paper, we use a population based methods such as particle swarm optimization to be able to find rapidly the global optimal solution for multimodal functions with wide solution space. Agents learn through not only on their respective experiences, but also by exchanging information among them, simulation results show that the swarm Q-learning surpass the simple Q-learning causing less average delay time and higher flow rate.