A Web Smart Space Framework for Intelligent Search Engines

A web smart space is an intelligent environment which has additional capability of searching the information smartly and efficiently. New advancements like dynamic web contents generation has increased the size of web repositories. Among so many modern software analysis requirements, one is to search information from the given repository. But useful information extraction is a troublesome hitch due to the multi-lingual; base of the web data collection. The issue of semantic based information searching has become a standoff due to the inconsistencies and variations in the characteristics of the data. In the accomplished research, a web smart space framework has been proposed which introduces front end processing for a search engine to make the information retrieval process more intelligent and accurate. In orthodox searching anatomies, searching is performed only by using pattern matching technique and consequently a large number of irrelevant results are generated. The projected framework has insightful ability to improve this drawback and returns efficient outcomes. Designed framework gets text input from the user in the form complete question, understands the input and generates the meanings. Search engine searches on the basis of the information provided

Porqpine: a peer-to-peer search engine

In this paper, we present a fully distributed and collaborative search engine for web pages: Porqpine. This system uses a novel query-based model and collaborative filtering techniques in order to obtain user-customized results. All knowledge about users and profiles is stored in each user node?s application. Overall the system is a multi-agent system that runs on the computers of the user community. The nodes interact in a peer-to-peer fashion in order to create a real distributed search engine where information is completely distributed among all the nodes in the network. Moreover, the system preserves the privacy of user queries and results by maintaining the anonymity of the queries? consumers and results? producers. The knowledge required by the system to work is implicitly caught through the monitoring of users actions, not only within the system?s interface but also within one of the most popular web browsers. Thus, users are not required to explicitly feed knowledge about their interests into the system since this process is done automatically. In this manner, users obtain the benefits of a personalized search engine just by installing the application on their computer. Porqpine does not intend to shun completely conventional centralized search engines but to complement them by issuing more accurate and personalized results.Postprint (published version

An Expressive Language and Efficient Execution System for Software Agents

Software agents can be used to automate many of the tedious, time-consuming information processing tasks that humans currently have to complete manually. However, to do so, agent plans must be capable of representing the myriad of actions and control flows required to perform those tasks. In addition, since these tasks can require integrating multiple sources of remote information ? typically, a slow, I/O-bound process ? it is desirable to make execution as efficient as possible. To address both of these needs, we present a flexible software agent plan language and a highly parallel execution system that enable the efficient execution of expressive agent plans. The plan language allows complex tasks to be more easily expressed by providing a variety of operators for flexibly processing the data as well as supporting subplans (for modularity) and recursion (for indeterminate looping). The executor is based on a streaming dataflow model of execution to maximize the amount of operator and data parallelism possible at runtime. We have implemented both the language and executor in a system called THESEUS. Our results from testing THESEUS show that streaming dataflow execution can yield significant speedups over both traditional serial (von Neumann) as well as non-streaming dataflow-style execution that existing software and robot agent execution systems currently support. In addition, we show how plans written in the language we present can represent certain types of subtasks that cannot be accomplished using the languages supported by network query engines. Finally, we demonstrate that the increased expressivity of our plan language does not hamper performance; specifically, we show how data can be integrated from multiple remote sources just as efficiently using our architecture as is possible with a state-of-the-art streaming-dataflow network query engine

Creating agent platforms to host agent-mediated services that share resources

After a period where the Internet was exclusively filled with content, the present efforts are moving towards services, which handle the raw information to create value from it. Therefore labors to create a wide collection of agent-based services are being perfomed in several projects, such as Agentcities does. In this work we present an architecture for agent platforms named a-Buildings. The aim of the proposed architecture is to ease the creation, installation, search and management of agent-mediated services and the share of resources among services. To do so the a-Buildings architecture creates a new level of abstraction on top of the standard FIPA agent platform specification. Basically, an a-Building is a service-oriented platform which offers a set of low level services to the agents it hosts. We define low level services as those required services that are neccesary to create more complex high level composed services.Postprint (published version

Beyond Monte Carlo Tree Search: Playing Go with Deep Alternative Neural Network and Long-Term Evaluation

Monte Carlo tree search (MCTS) is extremely popular in computer Go which determines each action by enormous simulations in a broad and deep search tree. However, human experts select most actions by pattern analysis and careful evaluation rather than brute search of millions of future nteractions. In this paper, we propose a computer Go system that follows experts way of thinking and playing. Our system consists of two parts. The first part is a novel deep alternative neural network (DANN) used to generate candidates of next move. Compared with existing deep convolutional neural network (DCNN), DANN inserts recurrent layer after each convolutional layer and stacks them in an alternative manner. We show such setting can preserve more contexts of local features and its evolutions which are beneficial for move prediction. The second part is a long-term evaluation (LTE) module used to provide a reliable evaluation of candidates rather than a single probability from move predictor. This is consistent with human experts nature of playing since they can foresee tens of steps to give an accurate estimation of candidates. In our system, for each candidate, LTE calculates a cumulative reward after several future interactions when local variations are settled. Combining criteria from the two parts, our system determines the optimal choice of next move. For more comprehensive experiments, we introduce a new professional Go dataset (PGD), consisting of 253233 professional records. Experiments on GoGoD and PGD datasets show the DANN can substantially improve performance of move prediction over pure DCNN. When combining LTE, our system outperforms most relevant approaches and open engines based on MCTS.Comment: AAAI 201

Performance and prediction: Bayesian modelling of fallible choice in chess

Evaluating agents in decision-making applications requires assessing their skill and predicting their behaviour. Both are well developed in Poker-like situations, but less so in more complex game and model domains. This paper addresses both tasks by using Bayesian inference in a benchmark space of reference agents. The concepts are explained and demonstrated using the game of chess but the model applies generically to any domain with quantifiable options and fallible choice. Demonstration applications address questions frequently asked by the chess community regarding the stability of the rating scale, the comparison of players of different eras and/or leagues, and controversial incidents possibly involving fraud. The last include alleged under-performance, fabrication of tournament results, and clandestine use of computer advice during competition. Beyond the model world of games, the aim is to improve fallible human performance in complex, high-value tasks