Distributed Social Constraints Optimization Problems (DSCOPs) are DCOPs in which the global objective function for optimization incorporates a social welfare function (SWF). DSCOPs have individual, non-binary and asym-metric constraints and thus form a unique DCOP class. DSCOPs provide a natural framework for agents that compute their costs individually and are therefore self-interested. The co...

In a distributed system using message logging and checkpointing to provide fault tolerance there is always a unique maximum recoverable system state regardless of the message logging protocol used The proof of this relies on the observation that the set of system states that have occurred during any single execution of a system forms a lattice with the sets of consistent and recoverable system ...

Stochastic simulation approaches perform probabilistic inference in Bayesian networks by estimating the probability of an event based on the frequency that that· event occurs in a set of simulation trials. This paper describes the evidence weighting mechanism, for augmenting the lo�ic sampling stochastic simulation algo­ rithm l5]. Evidence weighting modifies the logic sampling algorithm by wei...

Reference immutability constraints restrict certain references in a program from modifying objects. These constraints, such as those provided by the Javari programming language, have been shown to increase the expressiveness of a language and to prevent and detect errors. Unfortunately, annotating existing code, including libraries, with reference immutabil­ ity constraints is a tedious and err...

We describe and analyze the complexity of a procedure for computing and updating a Delaunay triangulation of a set of points in the plane subject to incremental insertions and deletions. Our method is based on a recent algo rithm of Guibas, Knuth, and Sharir for constructing Delaunay triangulations by incremental point insertion only. Our implementation features several meth ods that are not us...

Feature selection can be de ned as a problem of nding a minimum set of M relevant at tributes that describes the dataset as well as the original N attributes do where M N After examining the problems with both the exhaustive and the heuristic approach to fea ture selection this paper proposes a proba bilistic approach The theoretic analysis and the experimental study show that the pro posed app...

We explore the issue of refining an exis­ tent Bayesian network structure using new data which might mention only a subset of the variables. Most previous works have only considered the refinement of the net­ work's conditional probability parameters, and have not addressed the issue of refin­ ing the network's structure. We develop a new approach for refining the network's structure. Our appro...

This paper presents algorithms developed for pixel merging phase of object space parallel polygon rendering on hypercube connected multicomputers These algorithms reduce volume of communication in pixel merging phase by only exchanging local foremost pixels In order to avoid message fragmentation local foremost pixels should be stored in consecutive memory locations An algorithm called modi ed ...

Diagnosing a system requires the identification of a set of components whose abnormal behavior could explain the faulty sys­ tem behavior. Previously, model-based diagnosis schemes have pro­ ceeded through a cycle of assumptions -* predictions observations assumptions-adjustment, where the basic assumptions entail the proper functioning of those components whose failure is not esta­ blished. He...

Q-Iearning is a reinforcement learning alg()rithm that learns expected utilities for stateaction transitions through successive interactions with the environment The algorithm '5 simplicity as well as its convergence properties have made it a popular algorithm for study However; its non-parametric representation of utilities limits its effectiveness in environments with large amounts of percept...