Rune K. Rasmussen

An approximate Bayesian computation approach for estimating parameters of complex environmental processes in a cellular automata

Modelling an environmental process involves creating a model structure and parameterising the model with appropriate values to accurately represent the process. Determining accurate parameter values for environmental systems can be challenging. Existing methods for parameter estimation typically make assumptions regarding the form of the Likelihood, and will often ignore any uncertainty around estimated values. This can be problematic, however, particularly in complex problems where Likelihoods may be intractable. In this paper we demonstrate an Approximate Bayesian Computational method for the estimation of parameters of a stochastic CA. We use as an example a CA constructed to simulate a range expansion such as might occur after a biological invasion, making parameter estimates using only count data such as could be gathered from field observations. We demonstrate ABC is a highly useful method for parameter estimation, with accurate estimates of parameters that are important for the management of invasive species such as the intrinsic rate of increase and the point in a landscape where a species has invaded. We also show that the method is capable of estimating the probability of long distance dispersal, a characteristic of biological invasions that is very influential in determining spread rates but has until now proved difficult to estimate accurately.

Design and implementation of a virtual world training simulation of ICU first hour handover processes.

Nursing training for an Intensive Care Unit (ICU) is a resource intensive process. High demands are made on staff, students and physical resources. Interactive, 3D computer simulations, known as virtual worlds, are increasingly being used to supplement training regimes in the health sciences; especially in areas such as complex hospital ward processes. Such worlds have been found to be very useful in maximising the utilisation of training resources. Our aim is to design and develop a novel virtual world application for teaching and training Intensive Care nurses in the approach and method for shift handover, to provide an independent, but rigorous approach to teaching these important skills. In this paper we present a virtual world simulator for students to practice key steps in handing over the 24/7 care requirements of intensive care patients during the commencing first hour of a shift. We describe the modelling process to provide a convincing interactive simulation of the handover steps involved. The virtual world provides a practice tool for students to test their analytical skills with scenarios previously provided by simple physical simulations, and live on the job training. Additional educational benefits include facilitation of remote learning, high flexibility in study hours and the automatic recording of a reviewable log from the session. To the best of our knowledge, we believe this is a novel and original application of virtual worlds to an ICU handover process. The major outcome of the work was a virtual world environment for training nurses in the shift handover process, designed and developed for use by postgraduate nurses in training.

A Theoretical Basis for Using Virtual Worlds as a Personalised Process Visualisation Approach

Communication processes are vital in the lifecycle of BPM projects. With this in mind, much research has been performed into facilitating this key component between stakeholders. Amongst the methods used to support this process are personalized process visualisations. In this paper, we review the development of this visualization trend, then, we propose a theoretical analysis framework based upon communication theory. We use this framework to provide theoretical support to the conjecture that 3D virtual worlds are powerful tools for communicating personalised visualisations of processes within a workplace. Meta-requirements are then derived and applied, via 3D virtual world functionalities, to generate example visualisations containing personalized aspects, which we believe enhance the process of communication between analysts and stakeholders in BPM process (re)design activities.

Human Resource Behaviour Simulation in Business Processes

The structure and dynamics of a modern business environment are very hard to model using traditional methods. Such complexity raises challenges to effective business analysis and improvement. The importance of applying business process simulation to analyse and improve business activities has been widely recognised. However, one remaining challenge is the development of approaches to human resource behaviour simulation. To address this problem, we describe a novel simulation approach where intelligent agents are used to simulate human resources by performing allocated work from a workflow management system. The behaviour of the intelligent agents is driven a by state transition mechanism called a hierarchical task network (HTN). We demonstrate and validate our simulator via a medical treatment process case study. Analysis of the simulation results shows that the behaviour driven by the HTN is consistent with design of the workflow model. We believe these preliminary results support the development of more sophisticated agent-based human resource simulation systems.

A template matching table for speeding-up game-tree searches for hex

Transposition tables have long been a viable tool in the pruning mechanisms of game-tree search algorithms. In such applications, a transposition table can reduce a game-tree to a game-graph with unique board positions at the nodes. This paper proposes a transposition table extension, called a template matching table, where templates that prove winning positions are used to map features of board positions to board values. This paper demonstrates that a game-tree search for the game of Hex can have a more effective pruning mechanism using a template matching table than it does using a transposition table.

An extension of the h-search algorithm for artificial hex players

Hex is a classic board game invented in the middle of the twentieth century by Piet Hein and rediscovered later by John Nash The best Hex artificial players analyse the board positions by deducing complex virtual connections from elementary connections using the H-Search algorithm In this paper, we extend the H-search with a new deduction rule This new deduction rule is capable of discovering virtual connections that the H-search cannot prove Thanks to this new deduction rule, the horizon of the artificial Hex players should move further away.

A move generating algorithm for hex solvers

Generating good move orderings when searching for solutions to games can greatly increase the efficiency of game solving searches. This paper proposes a move generating algorithm for the board game called Hex, which in contrast to many other approaches, determines move orderings from knowledge gained during the search. This move generator has been used in Hex searches solving the 6x6 Hex board with comparative results indicating a significant improvement in performance. One anticipates this move generator will be advantageous in searches for complete solutions of Hex boards, equal to, and larger than, the 7x7 Hex board.

A deductive system for proving workflow models from operational procedures

Many modern business environments employ software to automate the delivery of workflows; whereas, workflow design and generation remains a laborious technical task for domain specialists. Several different approaches have been proposed for deriving workflow models. Some approaches rely on process data mining approaches, whereas others have proposed derivations of workflow models from operational structures, domain specific knowledge or workflow model compositions from knowledge-bases. Many approaches draw on principles from automatic planning, but conceptual in context and lack mathematical justification. In this paper we present a mathematical framework for deducing tasks in workflow models from plans in mechanistic or strongly controlled work environments, with a focus around automatic plan generations. In addition, we prove an associative composition operator that permits crisp hierarchical task compositions for workflow models through a set of mathematical deduction rules. The result is a logical framework that can be used to prove tasks in workflow hierarchies from operational information about work processes and machine configurations in controlled or mechanistic work environments.

A likelihood-free Bayesian derivation method for service variants

Abstract Application programming interfaces (API), allowing systems to be accessed by the services they expose, have proliferated on the Internet and gained strategic interest in the IT industry. However, integration opportunities for larger, enterprise systems are hampered by complex and overloaded operations of their interfaces, having hundreds of parameters and multiple levels of nesting, corresponding to multiple business entities. Static (code) analysis techniques have been proposed to analyse service interfaces of enterprise systems. They support the derivation of business entities and relationships from the parameters of interface operations, allowing the restructure of operations, based on individual entities. In this paper, we extend the repertoire of static interface analysis to derive service variants, whereby subsets of operation parameters correspond to multiple nested business entity subtypes of variants. Specifically, we apply a Monte Carlo sampling method, based on likelihood-free Bayesian sampling, to traverse large parameter spaces, based on higher probabilistic tree search, to efficiently find subsets of parameters related to prospective subtypes. The results demonstrate a method with significant success rates in massive search spaces, as applied to the FedEx Shipment interface whose operations have in excess of 1000 parameters.

Efficient Service Variant Analysis with Markov Updates in Monte Carlo Tree Search (Short Paper).

Static analysis techniques can be used to analyse and simplify interfaces of enterprise systems, such as those from SAP, Oracle and FedEx, which becoming more prominent on the internet and vying for new systems integration and extension opportunities. Web services of enterprise systems are notoriously complex, having hundreds of parameters per operation, multiple levels of nesting, leading to ambiguities about valid invocations of operations. To derive valid invocations, which in turn assists service users with invoking services correctly, this paper focuses on a challenging aspect of static interface analysis, namely, the identification of service variants in operations, in which the parameters are subtypes of business entities involved in a service. To efficiently search for which combinations of parameters are for a valid invocation, we have proposed a Monte Carlo method, based on likelihood-free Bayesian sampling, to identify higher probability parameters spaces, from which to test prospective invocations. A significant performance boost was found by extending Monte Carlo sampling with Markov look-up, with validation using a simulated FedEx service interface, whose structural complexity exceeds many web services of enterprise systems available on the internet.