000 03395nam a22005775i 4500
001 978-1-4939-0274-3
003 DE-He213
005 20200421112225.0
007 cr nn 008mamaa
008 140104s2014 xxu| s |||| 0|eng d
020 _a9781493902743
_9978-1-4939-0274-3
024 7 _a10.1007/978-1-4939-0274-3
_2doi
050 4 _aQ334-342
050 4 _aTJ210.2-211.495
072 7 _aUYQ
_2bicssc
072 7 _aTJFM1
_2bicssc
072 7 _aCOM004000
_2bisacsh
082 0 4 _a006.3
_223
100 1 _aParker, Austin.
_eauthor.
245 1 0 _aData-driven Generation of Policies
_h[electronic resource] /
_cby Austin Parker, Gerardo I. Simari, Amy Sliva, V.S. Subrahmanian.
264 1 _aNew York, NY :
_bSpringer New York :
_bImprint: Springer,
_c2014.
300 _aX, 50 p. 15 illus.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aSpringerBriefs in Computer Science,
_x2191-5768
505 0 _aIntroduction and Related Work -- Optimal State Change Attempts -- Different Kinds of Effect Estimators -- A Comparison with Planning under Uncertainty -- Experimental Evaluation -- Conclusions.
520 _aThis 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.
650 0 _aComputer science.
650 0 _aMathematical statistics.
650 0 _aDatabase management.
650 0 _aData mining.
650 0 _aArtificial intelligence.
650 1 4 _aComputer Science.
650 2 4 _aArtificial Intelligence (incl. Robotics).
650 2 4 _aData Mining and Knowledge Discovery.
650 2 4 _aDatabase Management.
650 2 4 _aProbability and Statistics in Computer Science.
700 1 _aSimari, Gerardo I.
_eauthor.
700 1 _aSliva, Amy.
_eauthor.
700 1 _aSubrahmanian, V.S.
_eauthor.
710 2 _aSpringerLink (Online service)
773 0 _tSpringer eBooks
776 0 8 _iPrinted edition:
_z9781493902736
830 0 _aSpringerBriefs in Computer Science,
_x2191-5768
856 4 0 _uhttp://dx.doi.org/10.1007/978-1-4939-0274-3
912 _aZDB-2-SCS
942 _cEBK
999 _c57650
_d57650