600527
Learning from BDDs in SAT-based bounded model checking
Bounded Model Checking (BMC) based on Boolean Satisfiability (SAT) procedures has recently gained popularity as an alternative to BDD-based model checking techniques for finding bugs in large designs. In this paper, we explore the use of learning from BDDs, where learned clauses generated by BDD-based analysis are added to the SAT solver, to supplement its other learning mechanisms. We propose several heuristics for guiding this process, aimed at increasing the usefulness of the learned clauses, while reducing the overheads. We demonstrate the effectiveness of our approach on several industrial designs, where BMC performance is improved and the design can be searched up to a greater depth by use of BDD-based learning.
39505715
Iterative Abstraction using SAT-based BMC with Proof Analysis
6555974
Lazy constraints and SAT heuristics for proof-based abstraction
SAT proof analysis techniques have been used recently with BMC in order to generate proof-based abstractions that preserve the correctness of the property up to a certain depth. In this paper, we propose various techniques for handling BMC constraints that are targeted at reducing the size of the abstract model while preserving bounded correctness. We propose lazy constraints, where the idea is to delay propagation of certain implied values without modifying a standard SAT solver. We propose different methods for automatic use of lazy constraints in SAT-based BMC. Their use can be regarded as a heuristic, which typically leads to significant reduction in the size of the abstract model, frequently accompanied by an overall performance improvement. We also use SAT proof analysis to loosen the user-specified environmental constraints, in order to generate smaller abstract models for conservative verification. We briefly describe other SAT-based heuristics for reducing the size of the abstract model. We have implemented our techniques in a prototype verification platform, and demonstrate their effectiveness on several large industry designs.
8920735
Partition-based decision heuristics for image computation using SAT and BDDs
Methods based on Boolean satisfiability (SAT) typically use a conjunctive normal form (CNF) representation of the Boolean formula, and exploit the structure of the given problem through use of various decision heuristics and implication methods. We propose a new decision heuristic based on separator-set induced partitioning of the underlying CNF graph. It targets those variables whose choice generates clause partitions with disjoint variable supports. This can potentially improve performance of SAT applications by decomposing the problem dynamically within the search. In the context of a recently proposed image computation method combining SAT and BDDs, this results in simpler BDD subproblems. We provide algorithms for CNF partitioning - one based on a clause-variable dependency matrix, and another based on standard hypergraph partitioning techniques, and also for the use of partitioning information in decision heuristics for SAT. The effectiveness of the proposed partition-based heuristic is shown with practical results for reachability analysis of benchmark sequential circuits.
24549782
Adaptive Fault Tolerant Hospital Resource Scheduling
In a distributed hospital system, different hospitals should collaborate among themselves for sharing their resources to provide better and faster service to the patients. We address the distributed hospital resource scheduling problem and apply the Generalized Partial Global Planning (GPGP) approach used for multiagent systems for the same. An adaptive coordination mechanism based on the contract net protocol is proposed to support inter-hospital collaboration. We have performed simulation studies and the experimental results show that our coordination mechanism performs better than commonly used schemes. Fault handling mechanisms are presented so that the system continues to provide service even in the presence of faults. The proposed approach is applicable in contexts other than the distributed hospital system as well.
6212306
Constraint checking with partial information
Constraints are a valuable tool for managing information across multiple databases, as well as for general purposes of assuring data integrity. However, efficient implementation of constraint checking is difficult. In this paper we explore techniques for assuring constraint satisfaction without performing a complete evaluation of the constraints. We consider methods that use only constraint definitions, methods that use constraints and updates, and methods that use constraints, updates, and “local” data.
155165
Multivariate Integration on Hypercubic and Mesh Networks
Abstract We analyze a class of adaptive algorithms for integration over N-dimensional hyper-rectangular or simplical regions, on distributed systems. An adaptive algorithm attempts to achieve the requested accuracy by refining the subdivision of the integration region, thus allowing for a concentration of subdivisions near singularities. At the subdivision of a region, the error behaves according to a prescribed model, relating the error of the parent region to that of its children. The analysis can also be applied to problems in other areas, as long as the task selection is based on a priority function which behaves according to a suitable model. Using an efficient management of the subregions, we show that an O (p/ log p) speedup can be achieved on a p-processor hypercubic network, such as shuffle exchange, butterfly and hypercube. Furthermore, a speedup of O ( p ) can be achieved on a p × p mesh network. We also show that our algorithms compare favorably with well-known dynamic load balancing strategies.
8765967
A Dual-Pathway Neural Network Model of Control Relinquishment in Motor Skill Learning
Cognitive psychologists have long recognized that the acquisition of a motor skill involves a transition from attention-demanding controlled processing to more fluent automatic processing. Neuroscientific studies suggest that controlled and automatic processing rely on two largely distinct neural pathways. The controlled pathway, which includes the prefrontal cortex, is seen as acquiring declarative representations of skills. In comparison, the automatic pathway is thought to develop procedural representations. Automaticity in motor skill learning involves a reduction in dependence on frontal systems and an increased reliance on the automatic pathway. In this paper, we propose a biologically plausible computational model of motor skill automaticity. This model offers a dual-pathway neurocomputational account of the translation of declarative knowledge into procedural knowledge during motor learning. In support of the model, we review some previously reported human experimental results involving the learning of a sequential key pressing task, and we demonstrate, through simulation, howthe model provides a parsimonious explanation for these results.
13088866
Specification-based anomaly detection: a new approach for detecting network intrusions
Unlike signature or misuse based intrusion detection techniques, anomaly detection is capable of detecting novel attacks. However, the use of anomaly detection in practice is hampered by a high rate of false alarms. Specification-based techniques have been shown to produce a low rate of false alarms, but are not as effective as anomaly detection in detecting novel attacks, especially when it comes to network probing and denial-of-service attacks. This paper presents a new approach that combines specification-based and anomaly-based intrusion detection, mitigating the weaknesses of the two approaches while magnifying their strengths. Our approach begins with state-machine specifications of network protocols, and augments these state machines with information about statistics that need to be maintained to detect anomalies. We present a specification language in which all of this information can be captured in a succinct manner. We demonstrate the effectiveness of the approach on the 1999 Lincoln Labs intrusion detection evaluation data, where we are able to detect all of the probing and denial-of-service attacks with a low rate of false alarms (less than 10 per day). Whereas feature selection was a crucial step that required a great deal of expertise and insight in the case of previous anomaly detection approaches, we show that the use of protocol specifications in our approach simplifies this problem. Moreover, the machine learning component of our approach is robust enough to operate without human supervision, and fast enough that no sampling techniques need to be employed. As further evidence of effectiveness, we present results of applying our approach to detect stealthy email viruses in an intranet environment.