Membrane backtracking

Relevant Backtracking: Improved Intelligent Backtracking Using Relevance

This work investigates and develops a backtracking algorithm with a novel approach to enumerating and traversing search space. A basic logical framework inspired by relevant logics is presented, highlighting relationships between search and refutation proof construction. Mechanisation of a relevance aware Davis Putnam Logemann Loveland procedure is investigated, and this yields an intelligent b...

Dynamic Backtracking

Because of their occasional need to return to shallow points in a search tree, existing backtracking methods can sometimes erase meaningful progress toward solving a search problem. In this paper, we present a method by which backtrack points can be moved deeper in the search space, thereby avoiding this di culty. The technique developed is a variant of dependency-directed backtracking that use...

Backtracking Influence

Backtracking influence is influence that zigzags in time. For example, backtracking influence exists when an event E1 makes an event E2 more likely by way of a nomic connection that goes from E1 back in time to an event C and then forward in time to E2. I contend that backtracking influence is redundant in the sense that any existing backtracking influence exerted by E1 on E2 is equivalent to E...

Shallow Backtracking Points in an Intelligent Backtracking Schema

Local Search and Backtracking vs Non-Systematic Backtracking

This paper addresses the following question: what is the essential difference between stochastic local search (LS) and systematic backtracking (BT) that gives LS superior scalability? One possibility is LS’s lack of firm commitment to any variable assignment. Three BT algorithms are modified to have this feature by introducing randomness into the choice of backtracking variable: a forward check...