A Parameterisation of Algorithms for Distributed Constraint Optimisation via Potential Games

This paper introduces a parameterisation of learning algorithms for distributed constraint optimisation problems (DCOPs). This parameterisation encompasses many algorithms developed in both the computer science and game theory literatures. It is built on our insight that when formulated as noncooperative games, DCOPs form a subset of the class of potential games. This result allows us to prove convergence properties of algorithms developed in the computer science literature using game theoretic methods. Furthermore, our parameterisation can assist system designers by making the pros and cons of, and the synergies between, the various DCOP algorithm components clear

Learn While You Earn: Two Approaches to Learning Auction Parameters in Take-it-or-leave-it Auctions

Much of the research in auction theory assumes that the auctioneer knows the distribution of participants ’ valuations with complete certainty. However, this is unrealistic. Thus, we analyse cases in which the auctioneer is uncertain about the valuation distributions; specifically, we consider a repeated auction setting in which the auctioneer can learn these distributions. Using take-it-or-leave-it auctions (Sandholm and Gilpin, 2006) as an exemplar auction format, we consider two auction design criteria. Firstly, an auctioneer could maximise expected revenue each time the auction is held. Secondly, an auctioneer could maximise the information gained in earlier auctions (as measured by the Kullback-Liebler divergence between its posterior and prior) to develop good estimates of the unknowns, which are later exploited to improve the revenue earned in the long-run. Simulation results comparing the two criteria indicate that setting offers to maximise revenue does not significantly detract from learning performance, but optimising offers for information gain substantially reduces expected revenue while not producing significantly better parameter estimates

Protein resurfacing to identify macromolecular assemblies

Includes bibliographical references.2016 Summer.Protein engineering is an emerging discipline that dovetails modern molecular biology techniques with high-throughput screening, laboratory evolution technologies, and computational approaches to modify sequence, structure, and in some cases, function and properties of proteins. The ultimate goal is to develop new proteins with improved or designer functions for use in biotechnology, medicine and basic research. One way to engineer proteins is to change their solvent exposed regions through focused or random 'protein resurfacing'. Here, I describe several approaches towards the development of synthetic proteins with new properties and function, including resistance to aggregation, increased solubility, and potent and selective macromolecule recognition. The first part of this thesis describes the use of protein supercharging to develop a split-superpositive GFP reassembly assay that is more efficient, faster, and more robust than previously described variants, largely due to increased resistance to aggregation. The second part of this thesis describes the use of shape complementarity, protein resurfacing, and high-throughput screening to evolve the first potent and selective protein-based inhibitor of the oncoprotein gankyrin. Concomitant with this work, I also describe a protein grafting strategy to identify a soluble mimic of S6 ATPase, which is subsequently used to characterize the S6 ATPase/gankyrin interaction by isothermal titration calorimetry

Knapsack based Optimal Policies for Budget-Limited Multi-Armed Bandits

In budget-limited multi-armed bandit (MAB) problems, the learner's actions are costly and constrained by a fixed budget. Consequently, an optimal exploitation policy may not be to pull the optimal arm repeatedly, as is the case in other variants of MAB, but rather to pull the sequence of different arms that maximises the agent's total reward within the budget. This difference from existing MABs means that new approaches to maximising the total reward are required. Given this, we develop two pulling policies, namely: (i) KUBE; and (ii) fractional KUBE. Whereas the former provides better performance up to 40% in our experimental settings, the latter is computationally less expensive. We also prove logarithmic upper bounds for the regret of both policies, and show that these bounds are asymptotically optimal (i.e. they only differ from the best possible regret by a constant factor)

Rules of Engagement: design attributes for social interactions

We present a taxonomy for the design of workplace “break” spaces. The taxonomy can be used to identify aspects of current spaces that are either successful or problematic. From this analysis, we demonstrate how the taxonomy can be used to identify opportunities for computer mediated augmentation of spaces, and how such designs can be validated against this taxonomy

On the Existence of Pure Strategy Nash Equilibria in Integer-Splittable Weighted Congestion Games

We study the existence of pure strategy Nash equilibria (PSNE) in integer–splittable weighted congestion games (ISWCGs), where agents can strategically assign different amounts of demand to different resources, but must distribute this demand in fixed-size parts. Such scenarios arise in a wide range of application domains, including job scheduling and network routing, where agents have to allocate multiple tasks and can assign a number of tasks to a particular selected resource. Specifically, in an ISWCG, an agent has a certain total demand (aka weight) that it needs to satisfy, and can do so by requesting one or more integer units of each resource from an element of a given collection of feasible subsets. Each resource is associated with a unit–cost function of its level of congestion; as such, the cost to an agent for using a particular resource is the product of the resource unit–cost and the number of units the agent requests.While general ISWCGs do not admit PSNE [(Rosenthal, 1973b)], the restricted subclass of these games with linear unit–cost functions has been shown to possess a potential function [(Meyers, 2006)], and hence, PSNE. However, the linearity of costs may not be necessary for the existence of equilibria in pure strategies. Thus, in this paper we prove that PSNE always exist for a larger class of convex and monotonically increasing unit–costs. On the other hand, our result is accompanied by a limiting assumption on the structure of agents’ strategy sets: specifically, each agent is associated with its set of accessible resources, and can distribute its demand across any subset of these resources.Importantly, we show that neither monotonicity nor convexity on its own guarantees this result. Moreover, we give a counterexample with monotone and semi–convex cost functions, thus distinguishing ISWCGs from the class of infinitely–splittable congestion games for which the conditions of monotonicity and semi–convexity have been shown to be sufficient for PSNE existence [(Rosen, 1965)]. Furthermore, we demonstrate that the finite improvement path property (FIP) does not hold for convex increasing ISWCGs. Thus, in contrast to the case with linear costs, a potential function argument cannot be used to prove our result. Instead, we provide a procedure that converges to an equilibrium from an arbitrary initial strategy profile, and in doing so show that ISWCGs with convex increasing unit–cost functions are weakly acyclic

Assessing the bioavailability of the radionuclides technetium-99, selenium-79 and uranium-238 in contaminated soils using the diffusive gradients in thin-films (DGT) technique

The increasing global inventory of 99Tc, 79Se and 238U as components of nuclear waste, coupled with the legacy of contaminated sites, means that increasing attention has been centred on understanding the environmental behaviour of these radionuclides. Over the last 15 years, the technique of diffusive gradients in thin-films (DGT) has emerged as a promising tool for assessing bioavailability based on its demonstrated ability to successfully predict plant uptake for a range of metals and metalloids in soil (Zhang and Davison, 2015). It is therefore utilised in this thesis to address a substantial knowledge gap regarding the bioavailability and aging of Tc, Se and U in soils. Initially, the performance of a Chelex-ferrihydrite mixed binding layer (MBL) DGT for the novel simultaneous measurement of Se and U was investigated to validate its suitability for subsequent use. An assessment of the availability and aging of all three elements was centred on an 18-month laboratory incubation of a suite of spiked soils, throughout which time a series of DGT deployments were made. Lastly, the ability of DGT to predict ryegrass uptake of Tc across a range of soil types was investigated. The experimental work presented in this thesis reveals that the MBL DGT is an acceptable method for determining labile Se and U in soil, although at higher pH (> 7) and with increasing concentrations of HCO3 - , uptake of both elements was impaired. The availability and aging of Se and Tc within soil is governed by soil organic carbon, in addition to the Al and Fe oxide content for Se. Quantitatively, the aging of Tc could be best described by a pseudo-secondorder model, yet a natural exponential function provided the best fit for Se. U was found to be particularly resilient to aging within soils exposed to a fluctuating wet-dry moisture regime, where changes in pH were hypothesised to alter the solubility of key U binding phases, namely Fe and Al oxides and dissolved carbonate ligands. DGT was unable to reliably predict the extent of uptake of Tc within ryegrass across a range of soil types, although in situ deployments whilst the ryegrass was actively growing did yield encouraging results. Remedial efforts to reduce the soil-to-plant radionuclide transfer following a contamination event should look to utilise the complexing capacity of Corg in the case of Tc and Se, or prevent the exposure of the soil to wetting/drying events in the case of U. Where possible, DGT measurements of bioavailability within Tc-contaminated soils should be made in situ, although in a field setting this is logistically challenging and introduces significant uncertainties to the data. In practice, to reliably ascertain the bioavailability of Tc, it would seem that the best approach is to directly measure the accumulated activity within the plant