Planning under Uncertainty in Constrained and Partially Observable Environments

Developing intelligent decision making systems in the real world requires planning algorithms which are able to deal with sources of uncertainty and constraints. An example can be found in smart distribution grids, in which planning can be used to decide when electric vehicles charge their batteries, such that the capacity limits of lines are respected at all times. In this particular example there can be uncertainty in the arrival time and charging demand of vehicles, and constraints follow directly from the capacity limits of the distribution grid to which vehicles are connected. Existing algorithms for planning under uncertainty subject to constraints are currently not suitable for these types of applications, and therefore this dissertation aims improve the applicability of these algorithms by advancing the state of the art in constrained multi-agent planning under uncertainty. The dissertation presents new algorithmic techniques for exact POMDP planning, finite-horizon POMDPs and POMDPs with constraints. Additionally, the dissertation shows how models for constrained planning can be used in smart distribution grids.Algorithmic

Structured parallel programming for Monte Carlo Tree Search

The thesis is part of a bigger project, the HEPGAME (High Energy Physics Game). The main objective for HEPGAME is the utilization of AI solutions, particularly by using MCTS for simplification of HEP calculations. One of the issues is solving mathematical expressions of interest with millions of terms. These calculations can be solved with the FORM program, which is software for symbolic manipulation. Since these calculations are computationally intensive and take a large amount of time, the FORM program was parallelized to solve them in a reasonable amount of time.Therefore, any new algorithm based on MCTS, should also be parallelized. This requirement was behind the problem statement of the thesis: “How do we design a structured pattern-based parallel programming approach for efficient parallelism of MCTS for both multi-core and manycore shared-memory machines?”.To answer this question, the thesis approached the MCTS parallelization problem in three levels: (1) implementation level, (2) data structure level, and (3) algorithm level.In the implementation level, we proposed task-level parallelization over thread-level parallelization. Task-level parallelization provides us with efficient parallelism for MCTS to utilize cores on both multi-core and manycore machines.In the data structure level, we presented a lock-free data structure that guarantees the correctness. A lock-free data structure (1) removes the synchronization overhead when a parallel program needs many tasks to feed its cores and (2) improves both performance and scalability.In the algorithm level, we first explained how to use pipeline pattern for parallelization of MCTS to overcome search overhead. Then, through a step by step approach, we were able to propose and detail the structured parallel programming approach for Monte Carlo Tree Search.Algorithms and the Foundations of Software technolog

Business incubators: the impact of their support

A New Technology-Based Firm (NTBF) is a significant enabler of job creation and a driver of the economy through stimulating innovation. In the last two decades, we have seen an enormous development of the NTBFs. However, the liability of smallness, newness, and weak networking ties are three important obstacles in the early stages of an NTBF’s lifecycle. Consequently, there is a high rate of failure among NTBFs.A remedy to avoid these failures is in using the support and resources by Business Incubators (BIs). BIs provide supportive services to promote the NTBFs capabilities and to help them address their liabilities.So far, there is almost no reliable evidence on the effectiveness of BIs on the performance of NTBFs. Therefore, we aim to identify the supportive activities by BIs and, to understand to what extent the supports by them have a serious impact on the performance of their NTBFs. Building on qualitative and quantitative research methods, a model to measure the impact of support by BIs on the performances of NTBFs is developed, and tested among Dutch and German NTBFs. The research results provide practical guidelines for the management teams of the incubators, which can increase the effectiveness of their performances. The research reported in this thesis has been founded by the Initial Training Networks (ITN) as part of the Marie Curie program. The research is part of the larger project: A Networked and IT-enabled Firm’s Perspective on Crisis, project number: FP7 PEOPLE-2012-ITNAlgorithms and the Foundations of Software technolog

Volitional Cybersecurity

This dissertation introduces the “Volitional Cybersecurity” (VCS) theory as a systematic way to think about adoption and manage long-term adherence to cybersecurity approaches. The validation of VCS has been performed in small- and medium-sized enterprises or businesses (SMEs/SMBs) context. The focus on volitional activities promotes theoretical viewpoints. Also, it aids in demystifying the aspects of cybersecurity behaviour in heterogeneous contexts that have neither been systematically elaborated in prior studies nor embedded in cybersecurity solutions. Abundant literature demonstrates a lack of adoption of manifold cybersecurity remediations. It is still not adequately clear how to select and compose cybersecurity approaches into solutions for meeting the needs of many diverse cybersecurity-adopting organisations. Moreover, the studied theories in this context mainly originated from disciplines other than information systems and cybersecurity. The constructs were developed based on data, for instance, in psychology or criminology, that seem not to fit properly for the cybersecurity context. Consequently, discovering new methods and theories that can be of help in active and volitional forms of cybersecurity behaviour in diverse contexts may be conducive to a better quality of cybersecurity engagement. This leads to the main research question of this dissertation: How can we support volitional forms of behaviour with a self-paced tool to increase the quality of cybersecurity engagement? The main contribution of this dissertation is the VCS theory. VCS is a cybersecurity-focused theory structured around the core concept of volitional cybersecurity behaviour. It suggests that a context can be classified based on the cybersecurity competence of target groups and their distinct requirements. This classification diminishes the complexity of the context and is predictive of improvement needs for each class. Further, the theory explicates that supporting three factors: A) personalisation, B) cybersecurity competence, and C) connectedness to cybersecurity expertise affect the adoption of cybersecurity measures and better quality of cybersecurity engagement across all classes of the context. Therefore, approaches that ignore the personalisation of cybersecurity solutions, the cybersecurity competence of target groups, and the connectedness of recipients to cybersecurity expertise may lead to poorer acceptance of the value or utility of solutions. Subsequently, it can cause a lack of motivation for adopting cybersecurity solutions and adherence to best practices. VCS generates various implications. It has implications for cybersecurity research in heterogeneous contexts to transcend the common cybersecurity compliance approaches. Building on VCS, researchers could develop interventions looking for volitional cybersecurity behaviour change. Also, it provides knowledge that can be useful in the design of self-paced cybersecurity tools. VCS explains why the new self-paced cybersecurity tool needs specific features. The findings of this dissertation have been subsequently applied to the follow-up project design. Further, it has implications for practitioners and service providers to reach out to the potential end-users of their solutions