Title Data-driven generation of policies / Austin Parker, Gerardo I. Simari, Amy Sliva, V.S. Subrahmanian

Published New York : Springer, 2014

Table of Contents
1.	Introduction and Related Work					1
1.1.	Preliminaries on Event KBs					2
1.2.	Related Work					5
	References					6
2.	Optimal State Change Attempts					9
2.1.	Effect Estimators					12
2.2.	State Change Effectiveness					13
2.3.	Optimal State Change Attempts					14
2.4.	Basic Algorithms for Computing OSCAs					16
	References					18
3.	Different Kinds of Effect Estimators					19
3.1.	Learning Algorithms as Effect Estimators					19
3.2.	Data Selection Effect Estimators					20
3.3.	Computing OSCAs with Data Selection Effect Estimators					22
3.4.	Trie-Enhanced Optimal State Change Attempts (TOSCA)					24
3.4.1.	Reducing Trie Size by Bucketing Values					29
3.4.2.	Annotated Tries					29
	References					29
4.	A Comparison with Planning Under Uncertainty					31
4.1.	Obtaining an MDP from the Specification of an OSCA Problem					32
	References					35
5.	Experimental Evaluation					37
5.1.	Question 1: Which Effect Estimator Gives the Most Accurate Results?					37
5.2.	Question 2: Which Techniques Scale Best?					39
5.3.	Question 3: Which Techniques Provide the Best Running Time as the Number of Attributes and Their Domain Size Increases?					43
5.4.	Question 4: Which Algorithms Perform Best with Real-World Data?					44
	References					45
6.	Conclusions					47
	Index					49

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Description 1 online resource (x, 50 pages) : illustrations Series SpringerBriefs in Computer Science, 2191-5768 SpringerBriefs in computer science, 2191-5768 Contents Introduction and Related Work -- Optimal State Change Attempts -- Different Kinds of Effect Estimators -- A Comparison with Planning under Uncertainty -- Experimental Evaluation -- Conclusions Summary 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 Bibliography Includes bibliographical references and index Notes Online resource; title from PDF title page (SpringerLink, viewed January 6, 2014) Subject Computer algorithms. Artificial intelligence. Algorithms. algorithms. artificial intelligence. COMPUTERS -- Intelligence (AI) & Semantics. Algorithms Artificial intelligence Computer algorithms Form Electronic book Author Parker, Austin (Computer scientist), author. Simari, Gerardo I., author. Sliva, Amy, author. Subrahmanian, V. S., author ISBN 9781493902743 1493902741 1493902733 9781493902736

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