Academic research in dynamic optimisation uses benchmark generators to artificially simulate controlled and reproducible changing-environments to systematically compare algorithmic performance under uncertainty. However, due to the scarcity or difficulty in acquiring real-world data, benchmarks often fail to incorporate real-world features, such as problem constraints or the time-linkage property, where previously made decisions influence future events. This study introduces a Gaussian Copula-based real-world data-driven synthetic data generation model for Dynamic Truck and Trailer Scheduling Problem (DTTSP). The model offers a realistic, privacy-preserving DTTSP benchmark instance generator, which can be used to recreate the dynamism, constraints, heterogeneity, and time-linkage of logistics and supply chain operations. This work examines the utility, fidelity, and privacy of the suggested model in four workday case studies from a local transportation company. The conducted experiments demonstrate the systematical application of Gaussian Copulas to produce accurate, useful, and secure DTTSP benchmark instances that capture the statistical properties and correlation of variables, as well as the temporal patterns, in the original annual data. Nevertheless, the utility analysis of the conditional sampling indicates that there is still room for improvement in the modelling process.

Developing accurate algorithms for learning structures of probabilistic graphical models is an important problem within modern AI research. Here we focus on score-based structure learning for Bayesian networks as arguably the most central class of graphical models. A successful generic approach to optimal Bayesian network structure learning (BNSL), based on integer programming (IP), is implemented in the Gobnilp system. Despite the recent algorithmic advances, current understanding of foundational aspects underlying the IP based approach to BNSL is still somewhat lacking. In this paper, we provide theoretical contributions towards understanding fundamental aspects of cutting planes and the related separation problem in this context, ranging from NP-hardness results to analysis of polytopes and the related facets in connection to BNSL.

The challenging task of learning structures of probabilistic graphical models is an important problem within modern AI research. Recent years have witnessed several major algorithmic advances in structure learning for Bayesian networks - arguably the most central class of graphical models - especially in what is known as the score-based setting. A successful generic approach to optimal Bayesian network structure learning (BNSL), based on integer programming (IP), is implemented in the GOBNILP system. Despite the recent algorithmic advances, current understanding of foundational aspects underlying the IP based approach to BNSL is still somewhat lacking. Understanding fundamental aspects of cutting planes and the related separation problem is important not only from a purely theoretical perspective, but also since it holds out the promise of further improving the efficiency of state-of-the-art approaches to solving BNSL exactly. In this paper, we make several theoretical contributions towards these goals: (i) we study the computational complexity of the separation problem, proving that the problem is NP-hard; (ii) we formalise and analyse the relationship between three key polytopes underlying the IP-based approach to BNSL; (iii) we study the facets of the three polytopes both from the theoretical and practical perspective, providing, via exhaustive computation, a complete enumeration of facets for low-dimensional family-variable polytopes; and, furthermore, (iv) we establish a tight connection of the BNSL problem to the acyclic subgraph problem.

Current approaches to instruction cache analysis for determining worst-case execution time rely on building a mathematical model of the cache that tracks its contents at all points in the program. This requires perfect knowledge of the functional behaviour of the cache and may result in extreme complexity and pessimism if many alternative paths through code sections are possible. To overcome these issues, this paper proposes a new hybrid approach in which information obtained from program traces is used to automate the construction of a model of how the cache is used. The resulting model involves the learning of a Bayesian network that predicts which instructions result in cache misses as a function of previously taken paths. The model can then be utilised to predict cache misses for previously unseen inputs and paths. The accuracy of this learned model is assessed against real benchmarks and an established statistical approach to illustrate its benefits.

Storing instructions in caches has led to dramatic increases in the speed at which programs can execute. However, this has also made it harder to reason about the time needed for execution in those domains where temporal behaviour of code is important. This paper presents a novel approach to predicting which instructions will be found in the cache when required using machine learning. More specifically, we demonstrate a method in which a Bayesian network is inferred from examples of a program running and is then used to predict the presence of instructions in the cache when the same program is run with unknown inputs.

As modern processors can execute instructions at far greater rates than these instructions can be retrieved from main memory, computer systems commonly include caches that speed up access times. While these improve average execution times, they introduce additional complexity in determining the Worst Case Execution Times crucial for Real-Time Systems. In this paper, an approach is presented that utilises Bayesian Networks in order to more accurately estimate the worst-case caching behaviour of programs. With this method, a Bayesian Network is learned from traces of program execution that allows both constructive and destructive dependencies between instructions to be determined and a joint distribution over the number of cache hits to be found. Attention is given to the question of how the accuracy of the network depends on both the number of observations used for learning and the cardinality of the set of potential parents considered by the learning algorithm.

Determination of accurate estimates for the Worst-Case Execution Time of a program is essential for guaranteeing the correct temporal behavior of any Real-Time System. Of particular importance is tightly bounding the number of iterations of loops in the program or excessive undue pessimism can result. This paper presents a novel approach to determining the number of iterations of a loop for such analysis. Program traces are collected and analyzed allowing the number of loop executions to be parametrically determined safely and precisely under certain conditions. The approach is mathematically proved to be safe and its practicality is demonstrated on a series of benchmarks.

Static analysis can be used to determine safe estimates of Worst Case Execution Time. However, overestimation of the number of loop iterations, particularly in nested loops, can result in substantial pessimism in the overall estimate. This paper presents a method of determining exact parametric values of the number of loop iterations for a particular class of arbitrarily deeply nested loops. It is proven that values are guaranteed to be correct using information obtainable from a finite and quantifiable number of program traces. Using the results of this proof, a tool is constructed and its scalability assessed.

This article suggests that the parser underlying human syntax may have originally evolved to assist navigation, a claim supported by computational simulations as well as evidence from neuroscience and psychology. We discuss two independent conjectures about the way in which navigation could have supported the emergence of this aspect of the human language faculty: firstly, by promoting the development of a parser; and secondly, by possibly providing a topic of discussion to which this parser could have been applied with minimum effort. The paper summarizes our previously published experiments and provides original results in support of the evolutionary advantages this type of communication can provide, compared with other foraging strategies. Another aspect studied in the experiments is the combination and range of environmental factors that make communication beneficial, focusing on the availability and volatility of resources. We suggest that the parser evolved for navigation might initially have been limited to handling regular languages, and describe a mechanism that may have created selective pressure for a context-free parser.