On the Minimality and Decomposability of Constraint Networks

Constraint networks have been shown to be useful in formulating such diverse problems as scene labeling, natural language parsing, and temporal reasoning. Given a constraint network, we often wish to (i) nd a solution that satis es the constraints and (ii) nd the corresponding minimal network where the constraints are as explicit as possible. Both tasks are known to be NP-complete in the general case. Task (i) is usually solved using a backtracking algorithm, and task (ii) is often solved only approximately by enforcing various levels of local consistency. In this paper, we identify a property of binary constraints called row convexity and show its usefulness in deciding when a form of local consistency called path consistency is su cient to guarantee a network is both minimal and decomposable. Decomposable networks have the property that a solution can be found without backtracking. We show that the row convexity property can be tested for e ciently and we show, by examining applicat...

• Type: Conference
• Year: 1992
• Where: AAAI
• Authors: Peter van Beek
• Keywords: AAAI 1992, Constraint Networks, Intelligent Agents, Local Consistency, Row Convexity