6824501
The Undecidability of Iterated Modal Relativization
In dynamic epistemic logic and other fields, it is natural to consider relativization as an operator taking sentences to sentences. When using the ideas and methods of dynamic logic, one would like to iterate operators. This leads to iterated relativization. We are also concerned with the transitive closure operation, due to its connection to common knowledge. We show that for three fragments of the logic of iterated relativization and transitive closure, the satisfiability problems are fi1 Σ11–complete. Two of these fragments do not include transitive closure. We also show that the question of whether a sentence in these fragments has a finite (tree) model is fi0 Σ01–complete. These results go via reduction to problems concerning domino systems.
7900596
On self-embeddings of computable linear orderings
The Dushnik{Miller Theorem states that every innite countable linear ordering has a nontrivial self-embedding. We examine computability-theoretical aspects of this classical theorem.
13902265
Simulation and the semantic Web
One of the missions of the semantic Web is to put more knowledge on the Web in an organized fashion and link it to other information and data sources. Three successively more capable languages are (or will soon be) provided for this: RDF, OWL, and SWRL. This paper makes a case for using all three for the domain of modeling and simulation. Based on experience developing the discrete-event modeling ontology (DeMO) some observations on the issues and challenges involved in creating such ontologies are presented. An approach for decomposing models into behavioral and observable parts, a la hidden Markov models, which can make ontologies smaller and easier to understand, is also discussed
7380263
Getting more from the data in a multinomial selection problem
We consider the problem of determining which of k simulated systems is most likely to be the best per former based on some objective performance measure. The standard experiment is to generate v in dependent vector observations (replications) across the k- systems. A classical multinomial selection pro cedure, BEM (Bechhofer, Elmaghraby, and Morse), prescribes a minimum number of replications so that the probability of correctly selecting the true best system meets or exceeds a prespecified probability. Assuming that larger is better, BEM selects as best the system having the largest value of the performance measure in more replications than any other. We propose using these same v replications across k systems to form vk pseudoreplications (no longer in dependent) that contain one observation from each system, and again select as best the system having the largest value of the performance measure in more pseudoreplications than any other. We expect that this new procedure, AVC (all vector comparisons), dominates BEM in the sense that AVC will never require more independent replications than DEM to meet a prespecified probability of correct selection. We present analytical and simulation results to show how AVC fares versus BEM for different underly ing distribution families, different numbers of populations and various values of v. We also present results for the closely related problem of estimating the probability that a specific system is the best.
10095666
Simulation modeling within workflow technology
This paper presents an approach for integrating simulation modeling and analysis capabilities within the workflow management system (WFMS) being developed in the Large Scale Distributed Information Systems (LSDIS) Lab at the University of Georgia. Simulation modeling can be used for studying the efficiency of workflow designs as well as studying the general performance and reliability of WFMSs. We also discuss the importance of using sophisticated monitoring and animation capabilities, and the use of workflow management technology to advance simulation technology itself. Finally, we demonstrate a sample simulation where tasks and task managers are simulated.
17749917
Cartesian Genetic Programming
This paper presents a new form of Genetic Programming called Cartesian Genetic Programming in which a program is represented as an indexed graph. The graph is encoded in the form of a linear string of integers. The inputs or terminal set and node outputs are numbered sequentially. The node functions are also separately numbered. The genotype is just a list of node connections and functions. The genotype is then mapped to an indexed graph that can be executed as a program. Evolutionary algorithms are used to evolve the genotype in a symbolic regression problem (sixth order polynomial) and the Santa Fe Ant Trail. The computational effort is calculated for both cases. It is suggested that hit effort is a more reliable measure of computational efficiency. A neutral search strategy that allows the fittest genotype to be replaced by another equally fit genotype (a neutral genotype) is examined and compared with non-neutral search for the Santa Fe ant problem. The neutral search proves to be much more effective.
14082423
Neutrality and the Evolvability of Boolean Function Landscape
This work is a study of neutrality in the context of Evolutionary Computation systems. In particular, we introduce the use of explicit neutrality with an integer string coding scheme to allow neutrality to be measured during evolution. We tested this method on a Boolean benchmark problem. The experimental results indicate that there is a positive relationship between neutrality and evolvability: neutrality improves evolvability. We also identify four characteristics of adaptive/neutral mutations that are associated with high evolvability. They may be the ingredients in designing effective Evolutionary Computation systems for the Boolean class problem.