scholarly journals The I/O-Complexity of Ordered Binary-Decision Diagram Manipulation

1996 ◽  
Vol 3 (29) ◽  
Author(s):  
Lars Arge
Keyword(s):  
State Of The Art ◽  
Systems Design ◽  
Main Memory ◽  
Digital Systems ◽  
Decision Diagrams ◽  
Input Output ◽  
Concurrent System ◽  
Ordered Binary Decision Diagrams ◽  
Binary Decision ◽  
Software Packages

Ordered Binary-Decision Diagrams (OBDD) are the state-of-the art<br />data structure for boolean function manipulation and there exist<br />several software packages for OBDD manipulation. OBDDs have<br />been successfully used to solve problems in e.g. digital-systems design, verification and testing, in mathematical logic, concurrent system design and in artificial intelligence. The OBDDs used in many of these applications quickly get larger than the available main memory and it becomes essential to consider the problem of minimizing the Input/Output (I/O) communication. In this paper we analyze why existing OBDD manipulation algorithms perform poorly in an I/O environment and develop new I/O-efficient algorithms.

scholarly journals A generic approach to planning in the presence of incomplete information: Theory and implementation (Extended Abstract)

2017 ◽  
Author(s):  
Son Thanh To ◽  
Tran Cao Son ◽  
Enrico Pontelli
Keyword(s):  
Incomplete Information ◽  
State Of The Art ◽  
Transition Function ◽  
Belief State ◽  
Decision Diagrams ◽  
State Representation ◽  
Ordered Binary Decision Diagrams ◽  
Binary Decision ◽  
Abstract Notion ◽  
Concrete Representations

This paper proposes a generic approach to planning in the presence of incomplete information. The approach builds on an abstract notion of a belief state representation, along with an associated set of basic operations. These operations facilitate the development of a sound and complete transition function, for reasoning about effects of actions in the presence of incomplete information, and a set of abstract algorithms for planning. The paper demonstrates how the abstract definitions and algorithms can be instantiated in three concrete representations—minimal-DNF, minimal-CNF, and prime implicates—resulting in three highly competitive conformant planners: DNF, CNF, and PIP. The paper relates the notion of a representation to that of ordered binary decision diagrams, a well-known belief state representation employed by many conformant planners, and several target compilation languages that have been presented in the literature.The paper also includes an experimental evaluation of the planners DNF, CNF, and PIP and proposes a new set of conformant planning benchmarks that are challenging for state-of-the-art conformant planners.

NC-ALGORITHMS FOR OPERATIONS ON BINARY DECISION DIAGRAMS

1993 ◽  
Vol 03 (01) ◽  
pp. 3-12 ◽  
Author(s):  
DETLEF SIELING ◽  
INGO WEGENER
Keyword(s):  
Binary Decision Diagrams ◽  
Pattern Generation ◽  
Decision Diagrams ◽  
Equivalence Test ◽  
Ordered Binary Decision Diagrams ◽  
Binary Decision ◽  
Verification Test ◽  
Logical Synthesis ◽  
Redundancy Test ◽  
Nc Algorithms

(Ordered) binary decision diagrams are a powerful representation for Boolean functions and are widely used in logical synthesis, verification, test pattern generation or as part of CAD tools. NC-algorithms are presented for the most important operations on this representation, e.g. evaluation for a given input, minimization, satisfiability, redundancy test, replacement of variables by constants or functions, equivalence test and synthesis. The algorithms have logarithmic run time on CRCW COMMON PRAMs with a polynomial number of processors.

scholarly journals Optimal ordered binary decision diagrams for read-once formulas

2000 ◽  
Vol 103 (1-3) ◽  
pp. 237-258 ◽  
Author(s):  
Martin Sauerhoff ◽  
Ingo Wegener ◽  
Ralph Werchner
Keyword(s):  
Binary Decision Diagrams ◽  
Decision Diagrams ◽  
Ordered Binary Decision Diagrams ◽  
Binary Decision

scholarly journals Solving Delete Free Planning with Relaxed Decision Diagram Based Heuristics

2020 ◽  
Vol 67 ◽  
pp. 607-651
Author(s):  
Margarita Paz Castro ◽  
Chiara Piacentini ◽  
Andre Augusto Cire ◽  
J. Christopher Beck
Keyword(s):  
Linear Programming ◽  
Empirical Analysis ◽  
State Of The Art ◽  
The State ◽  
Decision Diagrams ◽  
Binary Decision ◽  
Admissible Heuristics ◽  
The Cost ◽  
Construction Algorithms ◽  
Diagram Representation

We investigate the use of relaxed decision diagrams (DDs) for computing admissible heuristics for the cost-optimal delete-free planning (DFP) problem. Our main contributions are the introduction of two novel DD encodings for a DFP task: a multivalued decision diagram that includes the sequencing aspect of the problem and a binary decision diagram representation of its sequential relaxation. We present construction algorithms for each DD that leverage these different perspectives of the DFP task and provide theoretical and empirical analyses of the associated heuristics. We further show that relaxed DDs can be used beyond heuristic computation to extract delete-free plans, find action landmarks, and identify redundant actions. Our empirical analysis shows that while DD-based heuristics trail the state of the art, even small relaxed DDs are competitive with the linear programming heuristic for the DFP task, thus, revealing novel ways of designing admissible heuristics.

scholarly journals Stochastic Constraint Propagation for Mining Probabilistic Networks

2019 ◽  
Author(s):  
Anna Louise D. Latour ◽  
Behrouz Babaki ◽  
Siegfried Nijssen
Keyword(s):  
Data Mining ◽  
Probability Distributions ◽  
Constraint Propagation ◽  
Mixed Integer ◽  
Test Cases ◽  
Decision Diagrams ◽  
Probabilistic Networks ◽  
Ordered Binary Decision Diagrams ◽  
Binary Decision ◽  
Domain Consistency

A number of data mining problems on probabilistic networks can be modeled as Stochastic Constraint Optimization and Satisfaction Problems, i.e., problems that involve objectives or constraints with a stochastic component. Earlier methods for solving these problems used Ordered Binary Decision Diagrams (OBDDs) to represent constraints on probability distributions, which were decomposed into sets of smaller constraints and solved by Constraint Programming (CP) or Mixed Integer Programming (MIP) solvers. For the specific case of monotonic distributions, we propose an alternative method: a new propagator for a global OBDD-based constraint. We show that this propagator is (sub-)linear in the size of the OBDD, and maintains domain consistency. We experimentally evaluate the effectiveness of this global constraint in comparison to existing decomposition-based approaches, and show how this propagator can be used in combination with another data mining specific constraint present in CP systems. As test cases we use problems from the data mining literature.

ORDERED BINARY DECISION DIAGRAMS AND THEIR SIGNIFICANCE IN COMPUTER-AIDED DESIGN OF VLSI CIRCUITS

1999 ◽  
Vol 09 (03n04) ◽  
pp. 181-198 ◽  
Author(s):  
CHRISTOPH MEINEL ◽  
THORSTEN THEOBALD
Keyword(s):  
Integrated Circuits ◽  
Computer Aided Design ◽  
Binary Decision Diagrams ◽  
Basic Research ◽  
Vlsi Circuits ◽  
Decision Diagrams ◽  
Ordered Binary Decision Diagrams ◽  
Binary Decision ◽  
Computer Aided ◽  
Aided Design

Many problems in computer-aided design of highly integrated circuits (CAD for VLSI) can be transformed to the task of manipulating objects over finite domains. The efficiency of these operations depends substantially on the chosen data structures. In the last years, ordered binary decision diagrams (OBDDs) have proven to be a very efficient data structure in this context. Here, we give a survey on these developments and stress the deep interactions between basic research and practically relevant applied research with its immediate impact on the performance improvement of modern CAD design and verification tools.

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