We propose a position-based approach for largescale simulations of rigid bodies at interactive frame-rates. Our method solves positional constraints between rigid bodies and therefore integrates nicely with other position-based methods. Interaction of particles and rigid bodies through common constraints enables two-way coupling with deformables. The method exhibits exceptional performance and ...

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In this paper, we describe a new algebra for XML retrieval. We first describe how to transform an XPath-like query in our algebra. The latter contains a vague predicate, about, which defines a set of document parts within an XML document that fullfill a query expressed as in “flat” Information Retrieval – a query that contains only constraints on content but not on structure. This predicate is ...

We present a new reinforcement learning (RL) approach that enables an autonomous agent to solve decision making problems under constraints. Our assured reinforcement learning approach models the uncertain environment as a high-level, abstract Markov decision process (AMDP), and uses probabilistic model checking to establish AMDP policies that satisfy a set of constraints defined in probabilisti...

Necessary and su cient conditions for metric regularity of several joint probabilistic constraints are derived using recent results from nonsmooth analysis The conditions apply to fairly general nonconvex nonsmooth probabilistic constraints and extend earlier work in this direction Further a veri able su cient condition for quadratic growth of the objective function in a more speci c convex sto...

Preferences and uncertainty occur in many real-life problems. We are concerned with the coexistence of preference and uncertainty in the same problem. In particular, we consider uncertainty, defined by the theory of possibility [2], that is one non-probabilistic way of dealing with uncertainty, that comes from lack of data or imprecise knowledge. We propose a method to integrate fuzzy preferenc...

We present an approach to reasoning about statistical knowledge and degrees of belief, which is based on a combination of probabilistic reasoning from conditional constraints and default reasoning from conditional knowledge bases. More precisely, we generalize the notions of Pearl's entailment in system , Lehmann's lexicographic entailment, and Geffner's conditional entailment to strict and def...

We consider augmenting abstract argumentation frameworks with probabilistic information and discuss different constraints to obtain meaningful probabilistic information. Moreover, we investigate the problem of incomplete probability assignments and propose a solution for completing these assignments by applying the principle of maximum entropy.

In recent papers, the partial order reduction approach has been adapted to reason about the probabilities for temporal properties in concurrent systems with probabilistic behaviours. This paper extends these results by presenting reduction criteria for a probabilistic branching time logic that allows specification of constraints on quantitative measures given by a reward or cost function for th...

We hypothesise that all electroweak symmetry breaking terms such as fermion masses and the W and Z gauge boson masses arise radiatively from just one explicit symmetry breaking term in the Lagrangian. Our hypothesis is motivated by the lack of experimental support and the lack of predictivity of the standard symmetry breaking scenario. We construct a simple model which illustrates our ideas. We...