Data-driven Generation of Policies

Data-driven Generation of Policies

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This Springer Brief presents a basic algorithm that 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. It explores correctness and algorithmic complexity results for both algorithms and experiments comparing their performance on both real-world and synthetic data. Topics addressed include optimal state change attempts, state change effectiveness, different kind of effect estimators, planning under uncertainty and experimental evaluation. These topics will help researchers analyze tabular data, even if the data contains 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. With a wide range of applications in computer science and the social sciences, the information in this Springer Brief is valuable for professionals and researchers dealing with tabular data, artificial intelligence and data mining. The applications are also useful for advanced-level students of computer science.have a set of repairs that have worked in the past: change the light bulb, make sure that ita#39;s plugged in, check the wiring, etc. Though our approach also relies on past experience in order to find a solution, the main difference with CBR is that event DBs do not consist of a€œrecipesa€; as we have ... Rowman and Littlefield, 1998 .

Title:Data-driven Generation of Policies
Author: Austin Parker, Gerardo I. Simari, Amy Sliva, V.S. Subrahmanian
Publisher:Springer Science & Business Media - 2014-01-04

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