Data-driven Generation of Policies

1. Front Matter

   Pages i-x

   PDF

2. Introduction and Related Work

   • Austin Parker, Gerardo I. Simari, Amy Sliva, V. S. Subrahmanian

   Pages 1-7

3. Optimal State Change Attempts

   • Austin Parker, Gerardo I. Simari, Amy Sliva, V. S. Subrahmanian

   Pages 9-18

4. Different Kinds of Effect Estimators

   • Austin Parker, Gerardo I. Simari, Amy Sliva, V. S. Subrahmanian

   Pages 19-29

5. A Comparison with Planning Under Uncertainty

   • Austin Parker, Gerardo I. Simari, Amy Sliva, V. S. Subrahmanian

   Pages 31-35

6. Experimental Evaluation

   • Austin Parker, Gerardo I. Simari, Amy Sliva, V. S. Subrahmanian

   Pages 37-45

7. Conclusions

   • Austin Parker, Gerardo I. Simari, Amy Sliva, V. S. Subrahmanian

   Pages 47-48

8. Back Matter

   Pages 49-50

   PDF

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.

• Automatic policy generation
• Data-driven information systems
• Effect estimators
• Event databases
• Trie data structure

• Department of Computer Science, University of Maryland, College Park, USA

  Austin Parker

• Department of Computer Science, University of Oxford, Oxford, United Kingdom

  Gerardo I. Simari

• Charles River Analytics Inc., Cambridge, USA

  Amy Sliva

• Computer Science Department, University of Maryland, College Park, USA

  V.S. Subrahmanian

Policies and ethics