In this paper we present a novel approach to solving Constraint Satisfaction Problems whose constraint graphs are highly clustered and the graph of clusters is close to being acyclic. Such graphs are encountered in many real world application domains such as configuration, diagnosis, model-based reasoning and scheduling. We present a class of variable ordering heuristics that exploit the cluste...

Word-Level Decision Diagrams (WLDDs), like BMDs or K BMDs, have been introduced to overcome the limitations of Binary Decision Diagrams (BDDs), which are the state-of-the-art data structure to represent and manipulate Boolean functions. However, the size of these graph types largely depends on the variable ordering, i.e. it may vary from linear to exponential. In the meantime, dynamic approache...

The Multiagent Agreement Problem (MAP) is a special form of Distributed Constraint Optimization (DCOP) that requires agents to choose values for variables to satisfy not only their own constraints, but also equality constraints with other agents. For solving MAPs, we introduce the AdoptMVA algorithm which is an extension of the existing Adopt algorithm designed to take advantage of the partial ...

This paper presents the Incremental Breakout Algorithm with Variable Ordering (IncBA). This algorithm belongs to the class of local search algorithms for solving Constraint Satisfaction Problems (CSP), and is based on the standard breakout algorithm extended by an incremental solving scheme combined with variable ordering. By evaluating this algorithm with two large sets of Graph Colouring and ...

Pi-Yu Chung, Ibrahim N. Hajj, and Janak H. Patel Coordinated Science Laboratory and Department of Electrical and Computer Engineering University of Illinois at Urbana-Champaign Abstract In this paper we describe several ordering heuristics for shared, Reduced and Ordered Binary Decision Diagrams(ROBDDs). These heuristics have been tested on ISCAS and MCNC benchmark circuits. In all examples, th...

In symbolic model checking, decision diagrams are used to store all reachable states of a computer program. The size of decision diagrams is highly dependent on the used variable ordering of the underlying structure, a matrix that represents transitions and variables of the system. The model checker LTSmin [16] currently uses a custom implementation to find a good variable ordering, but with th...

The variable ordering of constraint satisfaction problems affect the performance of search algorithms in CSPs. Dynamic Variable Ordering (DVO) has more advantage in improving the performance of search algorithms than static variable ordering. It is a newly developed method recent years that using particle swarm algorithm to solve binary constraint satisfaction problems, which is a global stocha...

Constraint satisfaction problems (CSPs) naturally occur in a number of important industrial applications such as planning and scheduling defeating many algorithmic search methods. GENET and it extended model, EGENET, are probabilistic neural networks which had some remarkable success in solving some hard instances of CSPs such as a set of hard graph coloring problems. Both GENET or EGENET does ...

Binary Decision Diagrams (BDDs) can be graphically manipulated to reduce the number of nodes and hence the area. In this context, ordering of BDDs play a major role. Most of the algorithms for input variable ordering of OBDD focus primarily on area minimization. However, suitable input variable ordering helps in minimizing the power consumption also. In this particular work, we have proposed tw...