Approximate Achievability in Event Databases

Abstract

An event DB is a database about states (of the world) and events (taken by an agent) whose effects are not well understood. Event DBs are omnipresent in the social sciences and may include diverse scenarios from political events and the state of a country to education-related actions and their effects on a school system. We consider the following problem: given an event DB $\mathcal{K}$ representing historical events (what was the state and what actions were done at various past time points), and given a goal we wish to accomplish, what “change attempts” can the agent make so as to “optimize” the potential achievement of the goal? We define a formal version of this problem and derive results on its complexity. We then present a basic algorithm that provably provides a correct solution to finding an optimal state change attempt, as well as an enhanced algorithm that is built on top of the well known trie data structure and is also provably correct. We show correctness and algorithmic complexity results for both algorithms and report on experiments comparing their performance on synthetic data.