Discovering Patterns in Sequence of Events

Discovering Patterns in Sequence of Events (1985)

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by Thomas G. Dietterich , Ryszard S. Michalski

Venue:	ARTIFICIAL INTELLIGENCE
Citations:	24 - 3 self

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Datum	Value	Source
TITLE	Discovering Patterns in Sequence of Events	user correction - Legacy Corrections
AUTHOR NAME	Thomas G. Dietterich	user correction
AUTHOR AFFIL	Department of Computer Science, Stanford University, Stanford,	user correction
AUTHOR ADDR	CA 94305, U.S.A.	user correction
AUTHOR NAME	Ryszard S. Michalski	user correction
AUTHOR AFFIL	University of Illinois, Urbana	user correction
AUTHOR ADDR	IL 61801, U.S.A.	user correction
ABSTRACT	Given a sequence of events (or ob]ects), each 'characterized by a set of attributes, the problem considered is to discover a rule characterizing the sequence and able to predict a plausible sequence continuation. The rule, called a sequence-generating rule, is nondeterministic in the sense that it does not necessarily tell exactly which etent must appear next in the sequence, but rather, defines a set of plausible next eents. The basic assumption of the methodology presented here is that the next etent depends solely on the attributes of the previous eents in the sequence. These attributes are either initially given or can be den'td from the initial ones through a chain of inferences. Three basic rule models are employed to guide the search for a sequence.generating rule: decomposition, periodic, and disjunctive normal form (DNF). The search process involves simultaneously transforming the initial sequences to derived sequences and instantiating models to find the best match between the instantiated model and the derived sequence. A program, called SPARC/E, is described that implements most of the methodology a.v applied to discosring sequence generating rules in the card game Eleusis. This game, which models the process of scientiftc discovery, is used as a sottrce of examples for illustrating the performance of SPARC/E.	user correction - Legacy Corrections
YEAR	1985	INFERENCE
VENUE	ARTIFICIAL INTELLIGENCE	user correction
VENUE TYPE	JOURNAL	INFERENCE
PAGES	25--187	INFERENCE
VOLUME	25	INFERENCE
NUMBER	2	user correction
CITATIONS	36 found	ParsCit 1.0