Silvana Badaloni

A Fuzzy Extension of Allen`s Interval Algebra

The aim of this work is to integrate the ideas of flexibility and uncertainty into Allens interval-based temporal logic [1], defining a new formalism which extends classical Interval Algebra (IA). Some results obtained in the framework of Fuzzy Constraint Satisfaction Problem (FCSP) approach [3] are used in the specific domain of temporal reasoning. A new fuzzy interval algebra IAfuz is defined. Classical concepts of consistency and minimality are generalized to deal with IAfuz. Path-consistency and branch & bound algorithms are shown. A tractable sub-algebra of IAfuz is defined.

Hybrid temporal reasoning for planning and scheduling

This paper address the problem of representing heterogeneous temporal information in a uniform framework. Metric information relative to intervals is combined with qualitative information in a homogeneous representation based on a temporal constraint network. We illustrate the properties of the new sub-algebra called IDSA (Interval-Distance Sub-Algebra), the algorithms used to propagate temporal information and their complexity.

Dealing with Time Granularity in a Temporal Planning System

We have introduced the notion of temporal granularity in a planning system based on a temporal model. The use of different time scales together with the semantic effect of unit time change allows the planning of actions defined at different levels of abstractions. The advantage of a hierarchical planner consists in the fact that it can work, at each moment, at a granularity level as coarse as required, thus reducing the complexity of the problem we are addressing.

Discriminating exanthematic diseases from temporal patterns of patient symptoms

The temporal dimension is a characterizing factor of many diseases, in particular, of the exanthematic diseases. Therefore, the diagnosis of this kind of diseases can be based on the recognition of the typical temporal progression and duration of different symptoms. To this aim, we propose to apply a temporal reasoning system we have developed. The system is able to handle both qualitative and metric temporal knowledge affected by vagueness and uncertainty. In this preliminary work, we show how the fuzzy temporal framework allows us to represent typical temporal structures of different exanthematic diseases (e.g. Scarlet Fever, Measles, Rubella et c.) thus making possible to find matches with data coming from the patient disease.

ATS-PD An Adaptive Testing System for Psychological Disorders

The clinical assessment of mental disorders can be a time-consuming and error-prone procedure, consisting of a sequence of diagnostic hypothesis formulation and testing aimed at restricting the set of plausible diagnoses for the patient. In this article, we propose a novel computerized system for the adaptive testing of psychological disorders. The proposed system combines a mathematical representation of psychological disorders, known as the “formal psychological assessment,” with an algorithm designed for the adaptive assessment of an individual’s knowledge. The assessment algorithm is extended and adapted to the new application domain. Testing the system on a real sample of 4,324 healthy individuals, screened for obsessive-compulsive disorder, we demonstrate the system’s ability to support clinical testing, both by identifying the correct critical areas for each individual and by reducing the number of posed questions with respect to a standard written questionnaire.

Making an Autonomous Robot Plan Temporally Constrained Maintenance Operations

We present a system that can determine temporal scheduling of actions of an autonomous mobile robot by constructing a plan taking into account temporal constraints. Both qualitative and metric temporal information are represented in a constraint based network. Planning tasks are performed on the basis of the knowledge derived by this temporal knowledge representation system. Ordering temporally qualified actions falls out from maintaining temporal consistency. We applied it to plan under temporal constraints the actions of a manipulator mounted on a mobile platform in the case of the maintenance intervention over a hydraulic circuit.

Qualitative Reasoning for Biological Network Inference from Systematic Perturbation Experiments

The systematic perturbation of the components of a biological system has been proven among the most informative experimental setups for the identification of causal relations between the components. In this paper, we present Systematic Perturbation-Qualitative Reasoning (SPQR), a novel Qualitative Reasoning approach to automate the interpretation of the results of systematic perturbation experiments. Our method is based on a qualitative abstraction of the experimental data: for each perturbation experiment, measured values of the observed variables are modeled as lower, equal or higher than the measurements in the wild type condition, when no perturbation is applied. The algorithm exploits a set of IF-THEN rules to infer causal relations between the variables, analyzing the patterns of propagation of the perturbation signals through the biological network, and is specifically designed to minimize the rate of false positives among the inferred relations. Tested on both simulated and real perturbation data, SPQR indeed exhibits a significantly higher precision than the state of the art.

Tractable Fragments of Fuzzy Qualitative Algebra

Abstract In this paper we study the computational complexity of Fuzzy Qualitative Temporal Algebra (QA fuz ), a framework that combines qualitative temporal constraints between points and intervals, and allows modelling vagueness and uncertainty. Its tractable fragments can be identified by generalizing the results obtained for crisp Constraint Satisfaction Problems (CSPs) to fuzzy CSPs (FCSPs); to do this, we apply a general methodology based on the notion of α-cut. In particular, the results concerning the tractability of Qualitative Algebra QA, obtained in a recent study by different authors, can be extended to identify the tractable algebras of the fuzzy Qualitative Algebra QA fuz in such a way that the obtained set is maximal, namely any maximal tractable fuzzy algebra belongs to this set.

Typicality for Plausible Reasoning

We describe the key ideas of an approach to non-monotonic reasoning (HP-logic) involving an implicit notion of typicality. In this framework, the problem of irrelevant information is addressed and the deductive power and the reliability of the system are compared with those of the Circumscription approach to non-monotonic reasoning.

Bayesian Network structure learning: Hybridizing complete search with independence tests

Bayesian Networks (BN) are probabilistic graphical models used to encode in a compact way a joint probability distribution over a set of random variables. The NP-complete problem of finding the most probable BN structure given the observed data has been largely studied in recent years. In the literature, several complete algorithms have been proposed for the problem; in parallel, several tests for statistical independence between the random variables have been proposed, in order to reduce the size of the search space. In this work, we study how to hybridize the algorithm representing the state-of-the-art in complete search with two types of independence tests, and assess the performance of the two hybrid algorithms in terms of both solution quality and computational time. Experimental results show that hybridization with both types of independence test results in a substantial gain in computational time, against a limited loss in solution quality, and allow us to provide some guidelines on the choice of the test type, given the number of nodes in the network and the sample size.