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

Learning causal networks from data

Causal concepts play a crucial role in many reasoning tasks. Organized as a model revealing the causal structure of a domain, they can guide inference through relevant knowledge. This is a specially difficult knowledge to acquire, so some methods for automating the induction of causal models from data have been put forth. Here we review those that have a DAG (Directed Acyclic Graph) representation. Most work has been done on the problem of recovering belief nets from data but some extensions are appearing that claim to exhibit a true causal semantics. We'll review the analogies between belief networks andPostprint (published version

Learning causal networks from data

Causal concepts play a crucial role in many reasoning tasks. Organized as a model revealing the causal structure of a domain, they can guide inference through relevant knowledge. This is a specially difficult knowledge to acquire, so some methods for automating the induction of causal models from data have been put forth. Here we review those that have a DAG (Directed Acyclic Graph) representation. Most work has been done on the problem of recovering belief nets from data but some extensions are appearing that claim to exhibit a true causal semantics. We'll review the analogies between belief networks an

The ACE recommender system

In this report we present the ACE Recommender System, a system built using the Multi Agent technology. In a practical way we study the use of cognitive and collaborative filtering to improve the accuracity of the recommendations. We also show the way the user and the documents are modelled in the ACE system to combine this two aproaches. Finally some results are presented and discussed.Preprin

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

BayesProfile: application of bayesian networks to website user tracking

Detecting the most probable {it next} page a user is bound to visit inside a website has important practical consequences: it allows to suggest recommendations to the visitors as to which may be the pages of interest to them in a complex website; it is of help for website designers for deciding how to organize the site contents and it is also useful for pre-caching voluminous objects that the user will very probably need. In sum, it helps to customize web contents. In order to achieve that goal a classification, prediction an evaluation cycle has to be performed. Among the several possible alternative technologies we discuss a real use of Bayesian Network representations. The obtained results are commented, compared to other approaches and its applicability to other domains is also discussed.Postprint (published version

Probabilistic conditional independence: a similarity-based measure and its application to causal network learning

A new definition for similarity between possibility distributions is introduced and discussed as a basis for detecting dependence between variables by measuring the similarity degree of their respective distributions. This new definition is used to detect conditional independence relations in possibility distributions derived from data. This is the basis for a new hybrid algorithm for recovering possibilistic causal networks. The algorithm POSSCAUSE is presented and its applications discussed and compared with analogous developments in possibilistic and probabilistic causal networks learning.Postprint (published version

Probabilistic conditional independence: a similarity-based measure and its application to causal network learning

A new definition for similarity between possibility distributions is introduced and discussed as a basis for detecting dependence between variables by measuring the similarity degree of their respective distributions. This new definition is used to detect conditional independence relations in possibility distributions derived from data. This is the basis for a new hybrid algorithm for recovering possibilistic causal networks. The algorithm POSSCAUSE is presented and its applications discussed and compared with analogous developments in possibilistic and probabilistic causal networks learning

Probabilistic conditional independence: a similarity-based measure and its application to causal network learning

A new definition for similarity between possibility distributions is introduced and discussed as a basis for detecting dependence between variables by measuring the similarity degree of their respective distributions. This new definition is used to detect conditional independence relations in possibility distributions derived from data. This is the basis for a new hybrid algorithm for recovering possibilistic causal networks. The algorithm POSSCAUSE is presented and its applications discussed and compared with analogous developments in possibilistic and probabilistic causal networks learning