Automatically identified invariants are an important part of reductions of state-space reachability problems to SAT and related formalisms as a method of pruning the search space. No general algorithms for computing temporal invariants have been proposed before. Earlier algorithms restrict to unconditional actions and at-most-one invariants. We propose a powerful inductive algorithm for computi...

Modelling the density of an infectious disease in space and time is a task generally carried out separately from the diagnosis of that disease in individuals. These two inference problems are complementary, however: diagnosis of disease can be done more accurately if prior information from a spatial risk model is employed, and in turn a disease density model can benefit from the incorporation o...

State–space modeling techniques provide a convenient modeling platform for representing systematic trends as well as patterns of intraindividual variability around these trends. Their flexibility in accommodating multivariate processes renders them particularly suited to studying dyadic and family processes that show complex ebbs and flows over time. Using dyadic data collected during the Face-...

Conventionally, the state space and behavior of an object is determined when the object is created. Attributes and methods are not allowed to be attached to or retracted from an object during its lifetime. In other words, it is not feasible for an object to change its type dynamically. Furthermore, it is not possible to define several different views on an object allowing to exhibit a specific ...

This study implements a chaos-based model to predict the foreign exchange rates. In the first stage, the delay coordinate embedding is used to reconstruct the unobserved phase space (or state space) of the exchange rate dynamics. The phase space exhibits the inherent essential characteristic of the exchange rate and is suitable for financial modeling and forecasting. In the second stage, kernel...

Conformance checking techniques asses the suitability of a process model in representing an underlying process, observed through a collection of real executions. These techniques suffer from the wellknown state space explosion problem, hence handling process models exhibiting large or even infinite state spaces remains a challenge. One important metric in conformance checking is to asses the pr...

We consider the problem of multi-step ahead prediction in time series analysis using the non-parametric Gaussian process model. -step ahead forecasting of a discrete-time non-linear dynamic system can be performed by doing repeated one-step ahead predictions. For a state-space model of the form , the prediction of at time is based on the point estimates of the previous outputs. In this paper, w...

Effective use of robots in unstructured environments requires that they have sufficient autonomy and agility to execute task-level commands successfully. A challenging example of such a robot is a bipedal walking machine. Such a robot should be able to walk to a particular location within a particular time, while observing foot placement constraints, and avoiding a fall, if this is physically p...

This paper describes a set of algorithms for quickly and reliably solving linear rational expectations models. The utility, reliability and speed of these algorithms are a consequence of 1) the algorithm for computing the minimal dimension state space transition matrix for models with arbitrary numbers of lags or leads, 2) the availability of a simple modeling language for characterizing a line...

Conflict detection and analysis are of high importance, e.g., when integrating conceptual schemata, such as UML-Specifications, or analysing goal-fulfilment of sets of autonomous agents. In general, models for this introduce unnecessarily complicated frameworks with several disadvantages regarding semantics as well as complexity. This paper demonstrates that an important set of static and dynam...