The ramification problem in temporal databases: an approach with conflicting constraints

In this paper we study the ramification problem in the setting of temporal databases. Standard solutions from the literature on reasoning about action are inadequate because they rely on the assumption that fluents persist, and because actions have effects on the next situation only. In this paper we provide a solution to the ramification problem based on an extension of the situation calculus and the work of McCain and Turner. More specifically, we study the case where there are conflicting effects of an action, a particularly complex problem. Also we present a tool which implements the proposed solution.     

Introducing web service accessibility assessment techniques through a unified quality of service context

This paper introduces novel web service (WS) accessibility assessment techniques through a unified Quality of Services (QoS) context. The goal is to enable future QoS-aware service selection systems to select and provide accessible web services, ones that are properly designed so as to allow their consumption from end-user applications, used from people with disabilities. In this line, a WS accessibility assessment Framework (WSaaF) has been developed, on the basis of WS accessibility guidelines, dealing with accessibility issues that can appear both on the presentation level of content delivered through WSs and on the content level itself. The WSaaF and its guidelines follow the rationale behind W3C WCAG 2.0-based accessibility standardization of web content. It provides the basis toward building future accessible WSs, a task that can be further facilitated by the use of an appropriate Tool (WSaaT), developed with the aim to provide automatic assessment of services, against guidelines of the proposed framework. Then, the WS accessibility attribute is introduced, as a metric that can be used in conjunction to ones typically utilized so far, within QoS-aware service selection systems. As a result, a novel unified QoS framework is proposed, incorporating the notion of accessibility in the service selection process. The proposed unified QoS framework can eventually lead to the provision of services, which are selected from appropriate repositories and better suit the special needs of people with disabilities.     

Efficient Dynamic Constraints for Animating Articulated Figures

This paper presents an efficient dynamics-based computer animation system for simulating and controlling the motion of articulated figures. A non-trivial extension of Featherstone's O(n) recursive forward dynamics algorithm is derived which allows enforcing one or more constraints on the animated figures. We demonstrate how the constraint force evaluation algorithm we have developed makes it possible to simulate collisions between articulated figures, to compute the results of impulsive forces, to enforce joint limits, to model closed kinematic loops, and to robustly control motion at interactive rates. Particular care has been taken to make the algorithm not only fast, but also easy to implement and use. To better illustrate how the constraint force evaluation algorithm works, we provide pseudocode for its major components. Additionally, we analyze its computational complexity and finally we present examples demonstrating how our system has been used to generate interactive, physically correct complex motion with small user effort.     

Estimation of Time-Varying Parameters in Statistical Models: An Optimization Approach

We propose a convex optimization approach to solving the nonparametric regression estimation problem when the underlying regression function is Lipschitz continuous. This approach is based on the minimization of the sum of empirical squared errors, subject to the constraints implied by Lipschitz continuity. The resulting optimization problem has a convex objective function and linear constraints, and as a result, is efficiently solvable. The estimated function computed by this technique, is proven to convergeto the underlying regression function uniformly and almost surely, when the sample size grows to infinity, thus providing a very strong form of consistency. Wealso propose a convex optimization approach to the maximum likelihood estimation of unknown parameters in statistical models, where the parameters depend continuously on some observable input variables. For a number of classical distributional forms, the objective function in the underlying optimization problem is convex and the constraints are linear. These problems are, therefore, also efficiently solvable.     

Scalable nonlinear dynamical systems for agent steering and crowd simulation

We present a new methodology for agent modeling that is scalable and efficient. It is based on the integration of nonlinear dynamical systems and kinetic data structures. The method consists of three layers, which together model 3D agent steering, crowds and flocks among moving and static obstacles. The first layer, the local layer employs nonlinear dynamical systems theory to models low-level behaviors. It is fast and efficient, and it does not depend on the total number of agents in the environment. This dynamical systems-based approach also allows us to establish continuous numerical parameters for modifying each agent's behavior. The second layer, a global environment layer consists of a specifically designed kinetic data structure to track efficiently the immediate environment of each agent and know which obstacles/agents are near or visible to the given agent. This layer reduces the complexity in the local layer. In the third layer, a global planning layer, the problem of target tracking is generalized in a way that allows navigation in maze-like terrains, avoidance of local minima and cooperation between agents. We implement this layer based on two approaches that are suitable for different applications: One approach is to track the closest single moving or static target; the second is to use a pre-specified vector field, which may be generated automatically (with harmonic functions, for example) or based on user input to achieve the desired output. We also discuss how hybrid systems concepts for global planning can capitalize on both our layered approach and the continuous, reactive nature of our agent steering.

We demonstrate the power of the approach through a series of experiments simulating single/multiple agents and crowds moving towards moving/static targets in complex environments.     

Qualitative representation of spatial knowledge in two-dimensional space

Various relation-based systems, concerned with the qualitative representation and processing of spatial knowledge, have been developed in numerous application domains. In this article, we identify the common concepts underlying qualitative spatial knowledge representation, we compare the representational properties of the different systems, and we outline the computational tasks involved in relation-based spatial information processing. We also describesymbolic spatial indexes, relation-based structures that combine several ideas in spatial knowledge representation. A symbolic spatial index is an array that preserves only a set of spatial relations among distinct objects in an image, called the modeling space; the index array discards information, such as shape and size of objects, and irrelevant spatial relations. The construction of a symbolic spatial index from an input image can be thought of as a transformation that keeps only a set of representative points needed to define the relations of the modeling space. By keeping the relative arrangements of the representative points in symbolic spatial indexes and discarding all other points, we maintain enough information to answer queries regarding the spatial relations of the modeling space without the need to access the initial image or an object database. Symbolic spatial indexes can be used to solve problems involving route planning, composition of spatial relations, and update operations.