Learning to Reason and Act in Cascading Processes

Training agents to control a dynamic environment is a fundamental task in AI. In many environments the dynamics can be summarized by a small set of events that capture the semantic behavior of the system. Typically, these events form chains or cascades. We often wish to change the system behavior using a single intervention that propagates through the cascade. For instance, one may trigger a biochemical cascade to switch the state of a cell, or reroute a truck in logistic chains to meet an unexpected, urgent delivery. We introduce a new supervised learning setup called "Cascade". An agent observes a system with a known dynamics evolving from some initial state. It is given a structured semantic instruction and needs to make an intervention that triggers 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. 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. When provided an observed cascade of events, it can also reason about alternative outcomes.