We improve the efficiency of Sampath’s diagnoser approach by exploiting compact symbolic representations of the system and diagnoser in terms of binary decision diagrams. We present an algorithm for synthesising the symbolic diagnoser with promising results on test cases derived from a telecommunication application.

Recently, universal planning has become feasible through the use of efficient symbolic methods for plan generation and representation based on reduced ordered binary decision diagrams (OBDDs). In this paper, we address adversarial universal planning for multi-agent domains in which a set of uncontrollable agents may be adversarial to us. We present two new OBDD-based universal planning algorith...

Decision Diagrams (DDs) are a well populated family of data structures, used for efficient representation and manipulation of huge data sets. Typically a given application requires choosing one particular category of DDs, like Binary Decision Diagrams (BDDs) or Data Decision Diagrams (DDDs), and sticking with it. Each category provides a language to specify its operations. For instance, the ope...

We derive the average and worst case number of nodes in decision diagrams of r-valued symmetric functions of n variables. We show that, for large n, both numbers approach n r r ! . For binary decision diagrams (r = 2 ), we compute the distribution of the number of functions on n variables with a specified number of nodes. Subclasses of symmetric functions appear as features in this distribution...

In this paper, we present a novel implementation of a real-time musical decision-maker. In our scenario, the behaviour of a musical decision-making agent is specified as a constraint satisfaction problem. Generally, constraint satisfaction problems are solved using methods which are not suitable for real-time musical performance, because the amount of time they will take to arrive at a solution...

Information entropy is one of crucial concepts in modern information theory and related fields. Over the years a large number of methods for reliable estimation of information entropy have been invented. In this paper we introduce a method for the estimation entropy of binary streams based on binary decision diagrams. Binary decision diagrams are an efficient method of representation of discret...

Link puzzles involve finding paths or a cycle in a grid that satisfy given local and global properties. This paper proposes algorithms that enumerate solutions and instances of two link puzzles, Slitherlink and Numberlink, by zero-suppressed binary decision diagrams (ZDDs). A ZDD is a compact data structure for a family of sets provided with a rich family of set operations, by which, for exampl...

This thesis studies the learnability of several representation classes of Boolean functions: a restriction on k-term DNF, ordered binary decision diagrams and monotone term decision lists. We also show that some operations on these representation classes can be done in polynomial time. This fact is one of the motivations of the study (and use) of those classes.

There are many diierent ways of proving formulas in proposition logic. Many of these can easily be characterized as forms of resolution (e.g. 12] and 9]). Others use so-called binary decision diagrams (BDDs) 2, 10]. Experimental evidence suggests that BDDs and resolution based techniques are fundamentally diierent, in the sense that their performance can diier very much on benchmarks 14]. In th...

(Abstract) There is a demand for efficient indexed-substring data structures, which can store all substrings of a given text. Suffix trees and Directed Acyclic Word Graphs (DAWGs) are examples of substring indices, but they lack operations for manipulating sets of strings. The Sequence Binary Decision Diagram (SeqBDD) data structure proposed) is a new type of Binary Decision Diagram (BDD), and ...