AI for Social Good: Learning and Planning in the End-to-End, Data-to-Deployment Pipeline Prof. Milind Tambe University of Southern California  17/04/2019 Room 0.19/0.20, IST – Pavilhão de Informática II, Alameda | 13:30H Abstract With the maturing of AI and multiagent systems research, we have a tremendous opportunity to direct these advances towards addressing complex societal problems. I will focus on the problems of public safety and security, wildlife conservation and public health in low-resource communities, and present research advances in multiagent systems to address one key cross-cutting challenge: how to strategically deploy our limited intervention resources in these problem domains. I will discuss the importance of conducting this research via building the full data to field deployment end-to-end pipeline rather than just building machine learning or planning components in isolation. Results from our deployments from around the world show concrete improvements over the state of the art. In pushing this research agenda, we believe AI can indeed play an important role in fighting social injustice and improving society. Bio Milind Tambe is Helen N. and Emmett H. Jones Professor in Engineering at the University of Southern California(USC) and the Founding Co-Director of CAIS, the USC Center for Artificial Intelligence in Society, where his research focuses on advancing AI and multiagent systems research for Social Good. He is recipient of the IJCAI (International Joint Conference on AI) John McCarthy Award, ACM/SIGAI Autonomous Agents Research Award from AAMAS (Autonomous Agents and Multiagent Systems Conference), AAAI (Association for Advancement of Artificial Intelligence) Robert S Engelmore Memorial Lecture award, INFORMS Wagner prize, the Rist Prize of the Military Operations Research Society, the Christopher Columbus Fellowship Foundation Homeland security award, International Foundation for Agents and Multiagent Systems influential paper award; he is a fellow of AAAI and ACM. He has also received meritorious Commendation from […]

Generating Software Tests Andreas Zeller CISPA Helmholtz Institute for IT Security 15/04/2019 Anfiteatro VA4 no piso-1 do Edificio de Civil – IST/Alameda | 11:00H Abstract Software has bugs. What can we do to find as many of these as possible? In this talk, I show how to systematically test software by generating such tests automatically, starting with simple random “fuzzing” generators and then proceeding to more effective grammar-based and coverage-guided approaches. Being fully automatic and easy to deploy, such fuzzers run at little cost, yet are very effective in finding bugs: Our own Langfuzz grammar-based test generator for JavaScript runs around the clock for the Firefox, Chrome, and Edge web browsers and so far has found more than 2,600 confirmed bugs. Our latest test generator prototypes are even able to automatically learn the input language of a given program, which allows to generate highly effective tests for arbitrary programs without any particular setup. In the past months, we have collected our tools and techniques in an interactive textbook (www.fuzzingbook.org) with 10,000 well-documented lines of Python code for highly productive fuzzing. Bio Andreas Zeller is Faculty at the CISPA Helmholtz Center for Information Security, and professor for Software Engineering at Saarland University, both in Saarbrücken, Germany. In 2010, Zeller was inducted as Fellow of the ACM for his contributions to automated debugging and mining software archives, for which he also obtained the ACM SIGSOFT Outstanding Research Award in 2018. His current work focuses on specification mining and test case generation, funded by grants from DFG and the European Research Council (ERC). Host António Manuel Ferreira Rito da Silva