A Hierarchical Database Model for a Logic Programming Language

This paper presents an extended Clausal Database Model for a logic programming language. Instead of being restricted to one global database, as is the case with Prolog, we allow segmentation of the database into database units which are linked together into a semi-lattice. Each database unit defines a database view which includes clauses which have been asserted into that unit as well as clauses inherited from its ancestors higher in the lattice structure. This model supports arbitrary retraction. Retracting a clause in a database unit effectively blocks its inheritance for that unit and all of its descendants. Motivations for using this model are given. We also discuss the implementation of a Prolog meta-interpreter that uses this model. (hereafter referred to as (Phd) or Prolog Hierarchical Database) This meta-interpreter is in the spirit of Prolog and therefore has a version of assert, retract and cut

Abductive Reasoning in Multiple Fault Diagnosis

Abductive reasoning involves generating an explanation for a given set of observations about the world. Abduction provides a good reasoning framework for many AI problems, including diagnosis, plan recognition and learning. This paper focuses on the use of abductive reasoning in diagnostic systems in which there may be more than one underlying cause for the observed symptoms. In exploring this topic, we will review and compare several different approaches, including Binary Choice Bayesian, Sequential Bayesian, Causal Model Based Abduction, Parsimonious Set Covering, and the use of First Order Logic. Throughout the paper we will use as an example a simple diagnostic problem involving automotive troubleshooting

The Need for User Models in Generating Expert System Explanations

An explanation facility is an important component of an expert system, but current systems for the most part have neglected the importance of tailoring a system\u27s explanations to the user. This paper explores the role of user modeling in generating expert system explanations, making the claim that individualized user models are essential to produce good explanations when the system users vary in their knowledge of the domain, or in their goals, plans, and preferences. To make this argument, a characterization of explanation, and good explanation is made, leading to a presentation of how knowledge about the user affects the various aspects of a good explanation. Individualized user models are not only important, it is practical to obtain them. A method for acquiring a model of the user\u27s beliefs implicitly by eavesdropping on the interaction between user and system is presented, along with examples of how this information can be used to tailor an explanation

On Data Management in Pervasive Computing Environments

Abstract—This paper presents a framework to address new data management challenges introduced by data-intensive, pervasive computing environments. These challenges include a spatio-temporal variation of data and data source availability, lack of a global catalog and schema, and no guarantee of reconnection among peers due to the serendipitous nature of the environment. An important aspect of our solution is to treat devices as semiautonomous peers guided in their interactions by profiles and context. The profiles are grounded in a semantically rich language and represent information about users, devices, and data described in terms of “beliefs,” “desires, ” and “intentions. ” We present a prototype implementation of this framework over combined Bluetooth and Ad Hoc 802.11 networks and present experimental and simulation results that validate our approach and measure system performance. Index Terms—Mobile data management, pervasive computing environments, data and knowledge representation, profile-driven caching algorithm, profile driven data management, data-centric routing algorithm. æ

Contribution of precipitation and reference evapotranspiration to drought indices under different climates

In this study we analyzed the sensitivity of four drought indices to precipitation (P) and reference evapotranspiration (ETo) inputs. The four drought indices are the Palmer Drought Severity Index (PDSI), the Reconnaissance Drought Index (RDI), the Standardized Precipitation Evapotranspiration Index (SPEI) and the Standardized Palmer Drought Index (SPDI). The analysis uses long-term simulated series with varying averages and variances, as well as global observational data to assess the sensitivity to real climatic conditions in different regions of the World. The results show differences in the sensitivity to ETo and P among the four drought indices. The PDSI shows the lowest sensitivity to variation in their climate inputs, probably as a consequence of the standardization procedure of soil water budget anomalies. The RDI is only sensitive to the variance but not to the average of P and ETo. The SPEI shows the largest sensitivity to ETo variation, with clear geographic patterns mainly controlled by aridity. The low sensitivity of the PDSI to ETo makes the PDSI perhaps less apt as the suitable drought index in applications in which the changes in ETo are most relevant. On the contrary, the SPEI shows equal sensitivity to P and ETo. It works as a perfect supply and demand system modulated by the average and standard deviation of each series and combines the sensitivity of the series to changes in magnitude and variance. Our results are a robust assessment of the sensitivity of drought indices to P and ETo variation, and provide advice on the use of drought indices to detect climate change impacts on drought severity under a wide variety of climatic conditions. © 2014 Elsevier B.V.This work has been supported by research project CGL2011-27574-CO2-02 financed by the Spanish Commission of Science and Technology and FEDER and “ENV/ES/000536 - Demonstration and validation of innovative methodology for regional climate change adaptation in the Mediterranean area (LIFE MEDACC)” financed by the LIFE programme of the European Commission. C. A-M was supported by the JCI-2011-10263 postdoctoral fellowship by the Spanish Government.Peer Reviewe

An Ontology Pattern for Oceanographic Cruises: Towards an Oceanographer\u27s Dream of Integrated Knowledge Discovery

EarthCube is a major effort of the National Science Foundation to establish a next-generation knowledge architecture for the broader geosciences. Data storage, retrieval, access, and reuse are central parts of this new effort. Currently, EarthCube is organized around several building blocks and research coordination networks. OceanLink is a semantics enabled building block that aims at improving data retrieval and reuse via ontologies, Semantic Web technologies, and Linked Data for the ocean sciences. Cruises, in the sense of research expeditions, are central events for ocean scientists. Consequently, information about these cruises and the involved vessels has to be shared and made retrievable. For example, the ability to find cruises in the vicinity of physiographic features of interest, e.g., a hydrothermal vent field or a fracture zone, is of primary interest for oceanographers. In this paper, we use a design pattern-centric strategy to engineer ontologies for OceanLink. We provide a formal axiomatization of the introduced patterns and ontologies using the Web Ontology Language, explain design choices, discuss the re-usability of our models, and provide lessons learned for the future geo-ontologies

The Semantic Interpretation of Compound Nominals

Coordinated Science Laboratory changed its name from Control Systems LaboratoryOffice of Naval Research / N00014-75-C-0612Ope

An Interpreter and Compiler for Augmented Transition Networks

Coordinated Science Laboratory was formerly known as Control Systems LaboratoryOffice of Naval Research / N00014-75-C-0612Ope