P20: Learning to Reason about and to Act on Cascading Events

Authors: Yuval Atzmon, Eli Meirom, Shie Mannor, Gal Chechik

Abstract: Training agents to control a dynamic environment is a fundamental problem in AI. Many environment can be characterized by a small set of qualitatively distinct events. These events form chains or cascades, that capture the semantic behavior of the system. We often wish to change the system behavior, using a local intervention that propagates through the cascade until reaching a goal. For instance, one may reroute a truck in logistic chains to meet a special delivery, or trigger a biochemical cascade to switch the state of a cell. We introduce a new supervised learning setup called {\em Cascade}. An agent observes a system with a known dynamics evolving from some initial conditions. It is given a structured semantic instruction and needs to make a localized intervention that trigger a cascade of events, such that the system reaches an alternative (counterfactual) behavior. We provide a test-bed for this problem, consisting of physical objects. This problem is hard because the cascades make search space is highly fragmented and discontinuous. We combine semantic tree search with an event-driven forward model and devise an algorithm that learns to efficiently search in exponentially large semantic trees of continuous spaces. We demonstrate that our approach learns to effectively follow instructions to intervene in previously unseen complex scenes. It can also reason about alternative outcomes, when provided an observed cascade of events.