عنوان

Data-driven generation of policies /

شابک

1493902741 (electronic bk.)

شابک

9781493902743 (electronic bk.)

شابک اشتباه

9781493902736

شماره

dltt

عنوان اصلي

Data-driven generation of policies /

نام عام مواد

[Book]

نام نخستين پديدآور

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

نام خاص و کميت اثر

1 online resource (x, 50 pages) :

ساير جزييات

illustrations.

عنوان فروست

SpringerBriefs in Computer Science,

شاپا ي ISSN فروست

2191-5768

متن يادداشت

Includes bibliographical references and index

متن يادداشت

Introduction and Related Work -- Optimal State Change Attempts -- Different Kinds of Effect Estimators -- A Comparison with Planning under Uncertainty -- Experimental Evaluation -- Conclusions

بدون عنوان

0

متن يادداشت

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

موضوع مستند نشده

Artificial intelligence.

موضوع مستند نشده

Computer algorithms.

موضوع مستند نشده

Artificial Intelligence (incl. Robotics)

موضوع مستند نشده

Computer Science.

موضوع مستند نشده

Data Mining and Knowledge Discovery.

موضوع مستند نشده

Database Management.

موضوع مستند نشده

Probability and Statistics in Computer Science.

موضوع مستند نشده

COM004000

موضوع مستند نشده

TJFM1

موضوع مستند نشده

UYQ

شماره

ويراست

شماره رده

مستند نام اشخاص تاييد نشده

Parker, Austin, (Computer scientist)

مستند نام تنالگان تاييد نشده

Ohio Library and Information Network.

تاريخ عمليات

20201222074418.0

قواعد فهرست نويسي ( بخش توصيفي )

pn

نام الکترونيکي

نوع ماده

[Book]

کد کاربرگه

270410

تكميل شده

Y