GRASP: A Search Algorithm for Propositional Satisfiability
IEEE Transactions on Computers
New methods to color the vertices of a graph
Communications of the ACM
SATO: An Efficient Propositional Prover
CADE-14 Proceedings of the 14th International Conference on Automated Deduction
20.2 New Techniques for Deterministic Test Pattern Generation
VTS '98 Proceedings of the 16th IEEE VLSI Test Symposium
Domain-independent extensions to GSAT: solving large structured satisfiability problems
IJCAI'93 Proceedings of the 13th international joint conference on Artifical intelligence - Volume 1
A new method for solving hard satisfiability problems
AAAI'92 Proceedings of the tenth national conference on Artificial intelligence
| Hi-index | 0.00 |
Computational forensic engineering is the process of identification of the tool or algorithm that was used to produce a particular output or solution by examining the structural properties of the output. We introduce a new Relative Generic Forensic Engineering (RGFE) technique that has several advantages over the previously proposed approaches. The new RGFE technique not only performs more accurate identification of the tool used but also provides the identification with a level of confidence. Additionally, we introduce a generic formulation (integer linear programming formulation) which enables rapid application of the RGFE approach to a variety of problems that can be formulated as 0-1 integer linear programs. The key innovations of the RGFE technique include the development of a simulated annealing-based (SA) CART classification technique and a generic property formulation technique that facilitates property reuse. We introduce instance properties which enable an enhanced classification of problem instances leading to a higher accuracy of algorithm identification. Finally, the single most important innovation, property calibration, interprets the value for a given algorithm for a given property relative to the values for other algorithms. We demonstrated the effectiveness of the RGFE technique on the boolean satisfiability (SAT) and graph coloring (GC) problems.