Task-Oriented Programming (TOP) is a novel programming paradigm for the construction of distributed systems where users work together on the internet. When multiple users collaborate, they need to interact with each other frequently. TOP supports the definition of tasks that react to the progress made by others. With TOP, complex multiuser interactions can be programmed in a declarative style just by defining the tasks that have to be accomplished, thus eliminating the need to worry about the implementation detail that commonly frustrates the development of applications for this domain. TOP builds on four core concepts: tasks that represent computations or work to do which have an observable value that may change over time, data sharing enabling tasks to observe each other while the work is in progress, generic type driven generation of user interaction, and special combinators for sequential and parallel task composition. The semantics of these core concepts is defined in this paper. As an example we present the iTask3 framework, which embeds TOP in the functional programming language Clean.
The iTask system is a combinator library written in Clean offering a declarative, domain-specific language for defining workflows. From a declarative specification, a complete multi-user, web-enabled, workflow management system (WFMS) is generated. In the iTask paradigm, a workflow is a definition in which interactive elements are defined by editors on model values (abstracting from concrete GUI implementation details). The order of their appearance is calculated dynamically using combinator functions (abstracting from concrete synchronisation details). Defining interactive elements and the order of their appearance are also major concerns when programming GUI applications. For this reason, the iTask paradigm is potentially suited to program GUI applications as well. However, the iTask system was designed for a different application domain and lacks a number of key features to make it suited for programming GUI applications. In this paper, we identify these key features and show how they can be added to the iTask system in an orthogonal way, thus creating a new paradigm for programming GUI applications.
We apply Uppaal Tiga to automatically compute adaptive scheduling strategies for an industrial case study dealing with a state-of-the-art image processing pipeline of a printer. As far as we know, this is the first application of timed automata technology to an industrial scheduling problem with uncertainty in job arrivals
Probabilistic logics combine the expressive power of logic with the ability to reason with uncertainty. Several probabilistic logic languages have been proposed in the past, each of them with their own features. We focus on a class of probabilistic logic based on Sato's distribution semantics, which extends logic programming with probability distributions on binary random variables and guarantees a unique probability distribution. For many applications binary random variables are, however, not sufficient and one requires random variables with arbitrary ranges, e.g. real numbers. We tackle this problem by developing a generalised distribution semantics for a new probabilistic constraint logic programming language. In order to perform exact inference, imprecise probabilities are taken as a starting point, i.e. we deal with sets of probability distributions rather than a single one. It is shown that given any continuous distribution, conditional probabilities of events can be approximated arbitrarily close to the true probability. Furthermore, for this setting an inference algorithm that is a generalisation of weighted model counting is developed, making use of SMT solvers. We show that inference has similar complexity properties as precise probabilistic inference, unlike most imprecise methods for which inference is more complex. We also experimentally confirm that our algorithm is able to exploit local structure, such as determinism, which further reduces the computational complexity.
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