Semantic annotation of complex human scenes for multimedia surveillance

Presentado al 10th Congress of the Italian Association for Artificial Intelligence (AI*IA-2007) celebrado en Roma (Italia) del 10 al 13 de septiembre.A Multimedia Surveillance System (MSS) is considered for automatically retrieving semantic content from complex outdoor scenes, involving both human behavior and traffic domains. To characterize the dynamic information attached to detected objects, we consider a deterministic modeling of spatio-temporal features based on abstraction processes towards fuzzy logic formalism. A situational analysis over conceptualized information will not only allow us to describe human actions within a scene, but also to suggest possible interpretations of the behaviors perceived, such as situations involving thefts or dangers of running over. Towards this end, the different levels of semantic knowledge implied throughout the process are also classified into a proposed taxonomy.This work has been supported by EC grant IST-027110 for the HERMES project and by the Spanish MEC under projects TIC-2003-08865 and DPI -2004-5414. Jordi Gonz`alez also acknowledges the support of a Juan de la Cierva Postdoctoral fellowship from the Spanish MEC.Peer Reviewe

A conceptual approach to gene expression analysis enhanced by visual analytics

The analysis of gene expression data is a complex task for biologists wishing to understand the role of genes in the formation of diseases such as cancer. Biologists need greater support when trying to discover, and comprehend, new relationships within their data. In this paper, we describe an approach to the analysis of gene expression data where overlapping groupings are generated by Formal Concept Analysis and interactively analyzed in a tool called CUBIST. The CUBIST workflow involves querying a semantic database and converting the result into a formal context, which can be simplified to make it manageable, before it is visualized as a concept lattice and associated charts

A creative intelligent object classification system using Google's™ images import search function

Published ArticleLimits of artificial intelligent, expert systems are defined by the specific hardware limitation of the specific system. Limits can be overcome, or addressed, by giving an intelligent system web access; therefore giving it access to Google's™ vast hardware, search functions and databases. Reverse image searches can be done directly in Google's™ image search bar since October 2011. This reverse image search function is used by the proposed system to do object recognition. Computational creativity, or the ability of a program or computer to show human-level creativity and interaction, is achieved by means of a voice communication of the object identification result to the user. The proposed system interprets the result by doing a definition web search and communicating this to the user. The results show that with the novel interpretation software, it should be possible to use Google™ as an artificial intelligent, computational creative system. This proposed system thus has the ability to do object classification by accessing Google's™ vast hardware, search functions and databases, thereafter would the proposed system search a suitable definition for the classification. All of this information is communicated to the user using voice. These techniques could be used on an automatic guided vehicle, robots or expert system

On the Implementation of the Probabilistic Logic Programming Language ProbLog

The past few years have seen a surge of interest in the field of probabilistic logic learning and statistical relational learning. In this endeavor, many probabilistic logics have been developed. ProbLog is a recent probabilistic extension of Prolog motivated by the mining of large biological networks. In ProbLog, facts can be labeled with probabilities. These facts are treated as mutually independent random variables that indicate whether these facts belong to a randomly sampled program. Different kinds of queries can be posed to ProbLog programs. We introduce algorithms that allow the efficient execution of these queries, discuss their implementation on top of the YAP-Prolog system, and evaluate their performance in the context of large networks of biological entities.Comment: 28 pages; To appear in Theory and Practice of Logic Programming (TPLP

Learning Probabilistic Features for Robotic Navigation Using Laser Sensors

SLAM is a popular task used by robots and autonomous vehicles to build a map of an unknown environment and, at the same time, to determine their location within the map. This paper describes a SLAM-based, probabilistic robotic system able to learn the essential features of different parts of its environment. Some previous SLAM implementations had computational complexities ranging from O(Nlog(N)) to O(N2), where N is the number of map features. Unlike these methods, our approach reduces the computational complexity to O(N) by using a model to fuse the information from the sensors after applying the Bayesian paradigm. Once the training process is completed, the robot identifies and locates those areas that potentially match the sections that have been previously learned. After the training, the robot navigates and extracts a three-dimensional map of the environment using a single laser sensor. Thus, it perceives different sections of its world. In addition, in order to make our system able to be used in a low-cost robot, low-complexity algorithms that can be easily implemented on embedded processors or microcontrollers are used.This work has been supported by the Spanish Ministerio de Ciencia e Innovación (www.micinn.es), project TIN2009-10581

TP-Compilation for inference in probabilistic logic programs

We propose TP -compilation, a new inference technique for probabilistic logic programs that is based on forward reasoning. TP -compilation proceeds incrementally in that it interleaves the knowledge compilation step for weighted model counting with forward reasoning on the logic program. This leads to a novel anytime algorithm that provides hard bounds on the inferred probabilities. The main difference with existing inference techniques for probabilistic logic programs is that these are a sequence of isolated transformations. Typically, these transformations include conversion of the ground program into an equivalent propositional formula and compilation of this formula into a more tractable target representation for weighted model counting. An empirical evaluation shows that TP -compilation effectively handles larger instances of complex or cyclic real-world problems than current sequential approaches, both for exact and anytime approximate inference. Furthermore, we show that TP -compilation is conducive to inference in dynamic domains as it supports efficient updates to the compiled model