Generalized asset integrity games

Generalized assets represent a class of multi-scale adaptive state-transition systems with domain-oblivious performance criteria. The governance of such assets must proceed without exact specifications, objectives, or constraints. Decision making must rapidly scale in the presence of uncertainty, complexity, and intelligent adversaries. This thesis formulates an architecture for generalized asset planning. Assets are modelled as dynamical graph structures which admit topological performance indicators, such as dependability, resilience, and efficiency. These metrics are used to construct robust model configurations. A normalized compression distance (NCD) is computed between a given active/live asset model and a reference configuration to produce an integrity score. The utility derived from the asset is monotonically proportional to this integrity score, which represents the proximity to ideal conditions. The present work considers the situation between an asset manager and an intelligent adversary, who act within a stochastic environment to control the integrity state of the asset. A generalized asset integrity game engine (GAIGE) is developed, which implements anytime algorithms to solve a stochastically perturbed two-player zero-sum game. The resulting planning strategies seek to stabilize deviations from minimax trajectories of the integrity score. Results demonstrate the performance and scalability of the GAIGE. This approach represents a first-step towards domain-oblivious architectures for complex asset governance and anytime planning

The Boltzmann Machine: a Connectionist Model for Supra-Classical Logic

This thesis moves towards reconciliation of two of the major paradigms of artificial intelligence: by exploring the representation of symbolic logic in an artificial neural network. Previous attempts at the machine representation of classical logic are reviewed. We however, consider the requirements of inference in the broader realm of supra-classical, non-monotonic logic. This logic is concerned with the tolerance of exceptions, thought to be associated with common-sense reasoning. Biological plausibility extends these requirements in the context of human cognition. The thesis identifies the requirements of supra-classical, non-monotonic logic in relation to the properties of candidate neural networks. Previous research has theoretically identified the Boltzmann machine as a potential candidate. We provide experimental evidence supporting a version of the Boltzmann machine as a practical representation of this logic. The theme is pursued by looking at the benefits of utilising the relationship between the logic and the Boltzmann machine in two areas. We report adaptations to the machine architecture which select for different information distributions. These distributions correspond to state preference in traditional logic versus the concept of atomic typicality in contemporary approaches to logic. We also show that the learning algorithm of the Boltzmann machine can be adapted to implement pseudo-rehearsal during retraining. The results of machine retraining are then utilised to consider the plausibility of some current theories of belief revision in logic. Furthermore, we propose an alternative approach to belief revision based on the experimental results of retraining the Boltzmann machine

Methodology for inference on the Markov modulated Poisson process and theory for optimal scaling of the random walk Metropolis

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Applications and Interviews. A Structural Analysis of Two-Sided Simultaneous Search

Much of the job search literature assumes bilateral meetings between workers and firms. This ignores the frictions that arise when meetings are actually multilateral. I analyze the magnitude of these frictions by presenting an equilibrium job search model with an endogenous number of contacts. Workers contact firms by applying to vacancies, whereas firms contact applicants by interviewing them. Sending applications and interviewing applicants are costly activities but increase the probability to match. In equilibrium, contract dispersion arises and workers spread their applications over the different contract types. Estimation of the model on the Employment Opportunities Pilot Projects data set provides values for the cost of an application, the cost of an interview, and the value of non-market time. Frictions on the worker and the firm side are estimated to each cause approximately half of the 4.7% output loss compared to a Walrasian world. I show that in the estimated equilibrium welfare is improved if unemployed workers increase their search intensity.Directed Search, Recruitment, Stable Matching, Labor Market Frictions, Structural Estimation, Efficiency, Policy Analysis