Automated revision of CLIPS rule-bases

This paper describes CLIPS-R, a theory revision system for the revision of CLIPS rule-bases. CLIPS-R may be used for a variety of knowledge-base revision tasks, such as refining a prototype system, adapting an existing system to slightly different operating conditions, or improving an operational system that makes occasional errors. We present a description of how CLIPS-R revises rule-bases, and an evaluation of the system on three rule-bases

Tuning Numeric Parameters to Troubleshoot a Telephone-Network Loop

The Nynex Max expert system analyzes the result of an automated electric test on a telephone line and determines the type of problem. However, tuning the system\u27s parameter values can be difficult. The Opti-Max system can automatically set these parameters by analyzing decisions made by experts who troubleshoot problem

A Natural Language Query Interface for Searching Personal Information on Smartwatches

Currently, personal assistant systems, run on smartphones and use natural language interfaces. However, these systems rely mostly on the web for finding information. Mobile and wearable devices can collect an enormous amount of contextual personal data such as sleep and physical activities. These information objects and their applications are known as quantified-self, mobile health or personal informatics, and they can be used to provide a deeper insight into our behavior. To our knowledge, existing personal assistant systems do not support all types of quantified-self queries. In response to this, we have undertaken a user study to analyze a set of “textual questions/queries” that users have used to search their quantified-self or mobile health data. Through analyzing these questions, we have constructed a light-weight natural language based query interface - including a text parser algorithm and a user interface - to process the users’ queries that have been used for searching quantified-self information. This query interface has been designed to operate on small devices, i.e. smartwatches, as well as augmenting the personal assistant systems by allowing them to process end users’ natural language queries about their quantified-self data

User evaluation of a market-based recommender system

Recommender systems have been developed for a wide variety of applications (ranging from books, to holidays, to web pages). These systems have used a number of different approaches, since no one technique is best for all users in all situations. Given this, we believe that to be effective, systems should incorporate a wide variety of such techniques and then some form of overarching framework should be put in place to coordinate them so that only the best recommendations (from whatever source) are presented to the user. To this end, in our previous work, we detailed a market-based approach in which various recommender agents competed with one another to present their recommendations to the user. We showed through theoretical analysis and empirical evaluation with simulated users that an appropriately designed marketplace should be able to provide effective coordination. Building on this, we now report on the development of this multi-agent system and its evaluation with real users. Specifically, we show that our system is capable of consistently giving high quality recommendations, that the best recommendations that could be put forward are actually put forward, and that the combination of recommenders performs better than any constituent recommende

Scalable Daily Human Behavioral Pattern Mining from Multivariate Temporal Data

This work introduces a set of scalable algorithms to identify patterns of human daily behaviors. These patterns are extracted from multivariate temporal data that have been collected from smartphones. We have exploited sensors that are available on these devices, and have identified frequent behavioral patterns with a temporal granularity, which has been inspired by the way individuals segment time into events. These patterns are helpful to both end-users and third parties who provide services based on this information. We have demonstrated our approach on two real-world datasets and showed that our pattern identification algorithms are scalable. This scalability makes analysis on resource constrained and small devices such as smartwatches feasible. Traditional data analysis systems are usually operated in a remote system outside the device. This is largely due to the lack of scalability originating from software and hardware restrictions of mobile/wearable devices. By analyzing the data on the device, the user has the control over the data, i.e. privacy, and the network costs will also be removed

Antimicrobial resistance gene shuffling and a three-element mobilisation system in the monophasic Salmonella typhimurium strain ST1030

In this study we describe the genetic elements and the antimicrobial resistance units (RUs) harboured by the Salmonella Typhimurium monophasic variant 1,4,[5],12:i:- strain ST1030. Of the three identified RUs two were chromosomal, RU1 (IS26-blaTEM-1-IS26-strAB-sul2- IS26) and RU2 (IS26-tetR(B)-tetA(B)-ΔIS26), and one, RU3 (a sul3-associated class 1 integron with cassette array dfrA12-orfF-aadA2-cmlA1-aadA1), was embedded in a Tn21-derived element harboured by the conjugative I1 plasmid pST1030-1A. IS26 elements mediated the antimicrobial resistance gene (ARG) shuffling and this gave rise to pST1030-1A derivatives with different sets of ARGs. ST1030 also harboured two ColE1-like plasmids of which one, pST1030-2A, was mobilisable and the target of an intracellular translocation of the Tn21-derived element; the second (pST1030-3) was an orphan mob-associated oriT plasmid co-transferred with pST1030-1A and pST1030-2A. pST1030-2A and pST1030-3 also carried a parA gene and a type III restriction modification system, respectively. Overall analysis of our data reinforces the role played by IS26, Tn21-derived elements and non-conjugative plasmids in the spread of ARGs and supplies the first evidence, at least in Salmonella, for the identification of a natural isolate harbouring a three-element mobilisation system in the same cell

e-Tourism: a tourist recommendation and planning application

e-Tourism is a tourist recommendation and planning application to assist users on the organization of a leisure and tourist agenda. First, a recommender system offers the user a list of the city places that are likely of interest to the user. This list takes into account the user demographic classification, the user likes in former trips and the preferences for the current visit. Second, a planning module schedules the list of recommended places according to their temporal characteristics as well as the user restrictions; that is the planning system determines how and when to realize the recommended activities. Having the list of recommended activities organized as an agenda (i.e. an executable plan), is a relevant characteristic that most recommender systems lack.This work has been partially funded by Consolider Ingenio 2010 CSD2007-00022 project, by the Spanish Government MICINN TIN2008-6701-C03-01 project and by the Valencian Government GVPRE/2008/384 project. We thank J. Benton for having provided us with the system Sapa to execute our experiments.Sebastiá Tarín, L.; García García, I.; Onaindia De La Rivaherrera, E.; Gúzman Álvarez, CA. (2009). e-Tourism: a tourist recommendation and planning application. International Journal on Artificial Intelligence Tools. 18(5):717-738. https://doi.org/10.1142/S0218213009000378S71773818