A maximum entropy approach to nonmonotonic reasoning

An approach to nonmonotonic reasoning that combines the principle of infinitesimal probabilities with that of maximum entropy, thus extending the inferential power of the probabilistic interpretation of defaults, is proposed. A precise formalization of the consequences entailed by a conditional knowledge base is provided, the computational machinery necessary for drawing these consequences is developed, and the behavior of the maximum entropy approach is compared to related work in default reasoning. The resulting formalism offers a compromise between two extremes: the cautious approach based on the conditional interpretations of defaults and the bold approach based on minimizing abnormalities     

Yemanja—A Layered Fault Localization System for Multi-Domain Computing Utilities

Yemanja is a model-based event correlation engine for multi-layer fault diagnosis. It targets complex propagating fault scenarios, and can smoothly correlate low-level network events with high-level application performance alerts related to quality-of-service violations. Entity-models that represent devices or abstract components encapsulate their behavior. Distantly associated entity-models are not explicitly aware of each other, and communicate through internal event chains. Yemanja's state-based engine supports generic scenario definitions, prioritization of alternate solutions, integrated problem and device testing, and simultaneous analysis of overlapping problems. The system of correlation rules was developed based on the analysis of device and layer functions, and the dependencies among physical and abstract system components. The primary objectives of this research include the development of reusable, configuration independent, correlation scenarios, adaptability and extensibility of the engine to match the constantly changing topology of a multi-domain server farm, and development of a concise specification language that is relatively simple yet powerful.     

Bayesian Network Classifiers

Recent work in supervised learning has shown that a surprisingly simple Bayesian classifier with strong assumptions of independence among features, called naive Bayes, is competitive with state-of-the-art classifiers such as C4.5. This fact raises the question of whether a classifier with less restrictive assumptions can perform even better. In this paper we evaluate approaches for inducing classifiers from data, based on the theory of learning Bayesian networks. These networks are factored representations of probability distributions that generalize the naive Bayesian classifier and explicitly represent statements about independence. Among these approaches we single out a method we call Tree Augmented Naive Bayes (TAN), which outperforms naive Bayes, yet at the same time maintains the computational simplicity (no search involved) and robustness that characterize naive Bayes. We experimentally tested these approaches, using problems from the University of California at Irvine repository, and compared them to C4.5, naive Bayes, and wrapper methods for feature selection.     

Delegated agents for network management

Current network management depends on centralized monitoring, analysis, and control by operations staff who must manipulate detailed and often obscure element instrumentation. This complex and labor-intensive management paradigm has been stretched to its limits by the scale and complexity of emerging networks. This article describes the use of delegated agents to distribute and automate management functions. With delegated agents, management intelligence can be dynamically embedded in elements and domains so that networked systems can be programmed to be self-managed