Applying Multi-Agent Techniques to Cancer Modeling


Matthew Brown, Emma Bowring, Shira Epstein, Mufaddal Jhaveri, Rajiv Maheswaran, Parag Mallick, Shannon Mumenthaler, Michelle Povinelli, and Milind Tambe. 2011. “Applying Multi-Agent Techniques to Cancer Modeling .” In Workshop on Multiagent Sequential Decision Making in Uncertain Domains(MSDM) at AAMAS 2011 .


Each year, cancer is responsible for 13% of all deaths worldwide. In the United States, that percentage increases to 25%, translating to an estimated 569,490 deaths in 2010 [1]. Despite significant advances in the fight against cancer, these statistics make clear the need for additional research into new treatments. As such, there has been growing interest in the use of computer simulations as a tool to aid cancer researchers. We propose an innovative multi-agent approach in which healthy cells and cancerous cells are modeled as opposing teams of agents using a decentralized Markov decision process (DEC-MDP). We then describe changes made to traditional DEC-MDP algorithms in order to better handle the complexity and scale of our domain. We conclude by presenting and analyzing preliminary simulation results. This paper is intended to introduce the cancer modeling domain to the multi-agent community with the hope of fostering a discussion about the opportunities and challenges it presents. Given the complexity of the domain, we do not claim our approach to be a definitive solution but rather a first step toward the larger goal of creating realistic simulations of cancer.
See also: 2011