Integrated Page Rank Algorithm of Optimization Search Engine - Semantic Search Engine

There are many search engine finding The web pages of exact keyword like Google are search the keyword from page rank with highest SEO .we develop the search engine optimization with time based upon the user are visit the page many times ,but the visit the pages for tracking the action on the page based on the time recorded. This search engine update for the any search engine getting the output fast for user time based. The example of our search engine is the user are visit one website for two Minute, second website visit Five minute And third website visit for ten minute so your important websites third one based on time next time when you enter the keyword same as previous the List are shown as the more time you visit that website are shown first. Like the last time you visit third website are show first on the list. This Search Engine are showing the list page rank order time vise. The time is recorded in the database .Google are showing the page vise like search engine optimization but we are developing the search engine person vise

IALE: Imitating Active Learner Ensembles

Active learning (AL) prioritizes the labeling of the most informative data samples. However, the performance of AL heuristics depends on the structure of the underlying classifier model and the data. We propose an imitation learning scheme that imitates the selection of the best expert heuristic at each stage of the AL cycle in a batch-mode pool-based setting. We use DAGGER to train the policy on a dataset and later apply it to datasets from similar domains. With multiple AL heuristics as experts, the policy is able to reflect the choices of the best AL heuristics given the current state of the AL process. Our experiment on well-known datasets show that we both outperform state of the art imitation learners and heuristics.Comment: 17 page

Multi-Agent Path Finding with Continuous Time Using SAT Modulo Linear Real Arithmetic

This paper introduces a new approach to solving a continuous-time version of the multi-agent path finding problem. The algorithm translates the problem into an extension of the classical Boolean satisfiability problem, satisfiability modulo theories (SMT), that can be solved by off-the-shelf solvers. This enables the exploitation of conflict generalization techniques that such solvers can handle. Computational experiments show that the new approach scales better with respect to the available computation time than state-of-the art approaches and is usually able to avoid their exponential behavior on a class of benchmark problems modeling a typical bottleneck situation.Comment: Full version of the pape

High performance constraint satisfaction problem solving: State-recomputation versus state-copying.

Constraint Satisfaction Problems (CSPs) in Artificial Intelligence have been an important focus of research and have been a useful model for various applications such as scheduling, image processing and machine vision. CSPs are mathematical problems that try to search values for variables according to constraints. There are many approaches for searching solutions of non-binary CSPs. Traditionally, most CSP methods rely on a single processor. With the increasing popularization of multiple processors, parallel search methods are becoming alternatives to speed up the search process. Parallel search is a subfield of artificial intelligence in which the constraint satisfaction problem is centralized whereas the search processes are distributed among the different processors. In this thesis we present a forward checking algorithm solving non-binary CSPs by distributing different branches to different processors via message passing interface and execute it on a high performance distributed system called SHARCNET. However, the problem is how to efficiently communicate the state of the search among processors. Two communication models, namely, state-recomputation and state-copying via message passing, are implemented and evaluated. This thesis investigates the behaviour of communication from one process to another. The experimental results demonstrate that the state-recomputation model with tighter constraints obtains a better performance than the state-copying model, but when constraints become looser, the state-copying model is a better choice.Dept. of Computer Science. Paper copy at Leddy Library: Theses & Major Papers - Basement, West Bldg. / Call Number: Thesis2004 .Y364. Source: Masters Abstracts International, Volume: 44-01, page: 0417. Thesis (M.Sc.)--University of Windsor (Canada), 2005

Couplage de la configuration de produit et de projet de réalisation : exploitation des approches par contraintes et des algorithmes évolutionnaires

Dans le contexte actuel de compétitivité des marchés, la maîtrise et l'optimisation des processus de conception et de planification sont nécessaires pour garantir, d'une part la fiabilité et la qualité des produits systèmes ou services conçus et, d'autre part, le cycle de développement et les coûts. Ce constat impose de développer et d'améliorer les méthodes, modèles, techniques et outils relatifs aux processus de conception et de gestion ou de planification. Les travaux présentés dans cette thèse s'inscrivent dans ce contexte et proposent de mettre en relation ou encore de faire intéragir la configuration de produit avec la planification du projet de réalisation. Le but de ces travaux est d'apporter une aide à la décision pour le couplage de la configuration de produit et de la planification du projet associé, en exploitant deux outils issus de l'Intelligence Artificielle : les approches par contraintes et les algorithmes évolutionnaires. Cette aide à la décision est présentée en deux parties. La première partie décrit l'utilisation des approches par contraintes afin de permettre au décideur de configurer son produit et son projet de réalisation de manière simultanée et interactive. Pour ce faire, les techniques de propagation et de filtrage des contraintes sont exploitées spécifiquement. La deuxième partie s'intéresse à l'exploitation des algorithmes évolutionnaires pour optimiser l'espace de solutions selon les critères coût et délai afin de présenter au décideur, un ensemble réduit de solutions optimisées. Un algorithme SPEA2 modifié en intégrant des méthodes de filtrage dans ses opérateurs de parcours de l'espace de recherche y est présenté. Toutes nos propositions sont illustrées sur un exemple d'avion de tourisme et d'affaire