Steffen Michels

Task-oriented programming in a pure functional language

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 multi-user 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.

iTask as a new paradigm for building GUI applications

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.

An integrated reconfigurable system for maritime situational awareness

Nowadays the maritime operational picture is characterised by a growing number of entities whose interactions and activities are constantly changing. To provide timely support in this dynamic environment, automated systems need to be equipped with tools— lacking in existing systems—for real-time prioritisation of the application tasks (missions), selection and alignment of relevant information, and efficient reasoning at a situation level. In this paper, we present METIS—an industrial prototype system for supporting real-time, actionable maritime situational awareness. In particular, we focus on the innovation of METIS, which lies in the employment and integration of several state-of-the-art AI technologies to build the overall systems intelligence. These include reconfiguration of multi-context systems, natural language processing of heterogeneous (un)structured data and probabilistic reasoning of uncertain information. The capabilities of the system have been demonstrated in a proof of concept, which is deployed as a situational awareness plugin in the Tacticos command-and-control platform of our industrial partner. The principles exploited by METIS are giving valuable insights into what is considered to become the next generation of situational awareness systems.

A new probabilistic constraint logic programming language based on a generalised distribution semantics

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 Satos 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.

Rationale visualization for safety and security

In safety and security domains where objects of interest (OOI), such as people, vessels, or transactions, are continuously monitored, automated reasoning is required due to their sheer number and volume of information. We present a method to visually explain the rationale of a reasoning engine that raises an alarm if a certain situation is reached. Based both on evidence from heterogeneous and possibly unreliable sources, and on a domain specific reasoning structure, this engine concludes with a certain probability that, e.g., the OOI is suspected of smuggling. To support decision making, we visualize the rationale, an abstraction of the complicated reasoning structure. The evidence is displayed in a color‐coded matrix that easily reveals if and where observations contradict. In it, domain and operational experts can quickly understand and find complicated patterns and relate them to real‐world situations. Also, two groups of these experts evaluate our system through maritime use cases based on real data.

Probabilistic model-based assessment of information quality in uncertain domains

In various domains, such as security and surveillance, a large amount of information from heterogeneous sources is continuously gathered to identify and prevent potential threats, but it is unknown in advance what the observed entity of interest should look like. The quality of the decisions made depends, of course, on the quality of the information they are based on. In this paper, we propose a novel method for assessing the quality of information taking into account uncertainty. Two properties --- soundness and completeness --- of the information are used to define the notion of information quality and their expected values are defined using a probabilistic model output. Simulation experiments with data from a maritime scenario demonstrates the usage of the proposed method and its potential for decision support in complex tasks such as surveillance.

Getting a grip on tasks that coordinate tasks

Workflow management systems (WFMS) are software systems that coordinate the tasks human workers and computers have to perform to achieve a certain goal. The tasks to do and their interdependencies are described in a Workflow Description Language (WDL). Work can be organized in many, many ways and in the literature already more than hundred of useful workflow patterns for WDLs have been identified. The iTask system is not a WFMS, but a combinator library for the functional language Clean to support the construction of WFMS applications. Workflows can be described in a compositional style, using pure functions and combinators as self-contained building blocks. Thanks to the expressive power of the underlying functional language, complex workflows can be defined on top of just a handful of core task combinators. However, it is not sufficient to define the tasks that need to be done. We also need to express the way these tasks are being supervised, managed and visualized. In this paper we report on our current research effort to extend the iTask system such that the coordination of work can be defined as special tasks in the system as well. We take the opportunity to redesign editors which share information and the combinators for defining GUI interfaces for tasks, such as buttons, menus and windows. Even though the expressiveness of the resulting system increases significantly, the number of core combinators can be reduced. We argue that only two general Swiss-Army-Knife higher order functions are needed to obtain the desired functionality. This simplifies the implementation significantly and increases the maintainability of the system. We discuss the design space and decisions that lead to these two general functions for constructing tasks.

Defining multi-user web applications with itasks

In these lecture notes we explain how multi-user web applications can be developed in a programming style that favors tasks as main building block for the construction of such systems. A task is work that has to be performed by human-beings and computers working together on the internet. This concept has been implemented in the iTask framework as a monadic combinator library that is embedded in the pure and lazy functional programming language Clean. These lecture notes consist of many examples and exercises, and also discusses the foundation of both the iTask system and task-oriented programming.

Imprecise probabilistic horn clause logic

Approaches for extending logic to deal with uncertainty immanent to many real-world problems are often on the one side purely qualitative, such as modal logics, or on the other side quantitative, such as probabilistic logics. Research on combinations of qualitative and quantitative extensions to logic which put qualitative constraints on probability distributions, has mainly remained theoretical until now. In this paper, we propose a practically useful logic, which supports qualitative as well as quantitative uncertainty and can be extended with modalities with varying level of quantitative precision. This language has a solid semantic foundation based on imprecise probability theory. While in general imprecise probabilistic inference is much harder than the precise case, this is the first expressive imprecise probabilistic formalism for which probabilistic inference is shown to be as hard as corresponding precise probabilistic problems. A second contribution of this paper is an inference algorithm for this language based on the translation to a weighted model counting (WMC) problem, an approach also taken by state-of-the-art probabilistic inference methods for precise problems.

The Beauty of Declarative Purely Functional Projections

The concept of mathematical functions allows to declaratively express knowledge of how to project information to new derived information. The beauty of this approach emerges from the properties of pure functions fitting into a whole system like a piece of a jigsaw puzzle. Projections defining information in terms of other available information can be the building blocks of a system with a complex behaviour, where all details of how this complex behaviour is achieved can be completely separated from dealing with information used in the system. In this essay this is illustrated by examples of how the same projections can be used in two very different contexts.