Efficiency and fairness of resource utilisation under uncertainty

The efficient use of resources is a crucial problem of our time. Besides the constraints of efficient usage of scarce resources, in real-world problems the ubiquitous constraint of uncertainty further affects the use or distribution of most resources. Solution approaches are problem-dependent and have various benefits and difficulties. In this work we examine these benefits and difficulties in two different settings of uncertainty, both with their own benefits and difficulties. Moreover, we address the two problems using different techniques applicable to other settings. In the first problem the uncertainty is with respect to the resource itself. In the well-studied problem of fair multi-agent resource allocation it is generally assumed that the quantity of each resource is known a priori. However, in many real-world problems, such as the production of renewable energy which is typically weather-dependent, the exact amount of each resource may not be known at the time of decision making. This work investigates the fair division of a homogeneous, divisible resource where the available amount is given by a probability distribution. Specifically, the notion of ex-ante envy-freeness, where, in expectation, agents weakly prefer their allocation over every other agent’s allocation is considered. This work shows how uncertainty changes the relationship of fairness and efficiency, how the solution space is affected, how difficult the problem becomes, and gives algorithms for the case of two agents with utility function that are linear up to a maximal value. This is achieved by showing how in expectation a higher efficiency can be attained; the worst case might still affect the results; and that the problem is strongly NP-hard. Additionally, we provide two variants of a greedy algorithm for the case of two agents. One variant is optimal for the case of uniform probability distributions over the events. For the case of arbitrary probability distributions, we show that this problems is also NP-hard. Accordingly, we address the possibility of approximation. We show that one variant is not able to approximate all instances. Nevertheless, we show empirically that for realistic instances both variants of the algorithm can approximate the optimal solution. Hence the work lays the foundation for further research into homogeneous resources under uncertainty. In the second problem, the uncertainty comes from the behaviour of an agent and this behaviour is countered by random strategies. In full-knowledge multi-robot adversarial patrolling, a group of robots have to detect an adversary who knows the robots’ strategy. The adversary can easily take advantage of any deterministic patrolling strategy, which necessitates the employment of a randomised strategy. Previous algorithms have to be repeated to calculate the solution for different instances and lack insight into the strategy space. In comparison, this work shows how enumerative combinatorics can be used to provide the closed formulae of the probabilities of detecting the adversary. Hence, it facilitates characterising optimal random defence strategies in comparison to formerly used iterative black-box models. We provide the probability functions for four cases based on open and closed polylines using two different robot movement patterns. Moreover, we show how analysing the structure of the strategy space can further reduce the runtime. Hence, the work introduces a new technique into adversarial patrolling that can be used to improve runtime and foster further research. In conclusion, the work provides progress in two established research areas, and highlights the potential and importance of the consideration of the effects of uncertainty. Foremost, including uncertainty opens up research which is more attuned to real-world problems. Additionally, addressing these problems, including with novel approaches, allows finding (computationally) more efficient solution

Multi-robot adversarial patrolling strategies via lattice paths

In full-knowledge multi-robot adversarial patrolling, a group of robots has to detect an adversary who knows the robots' strategy. The adversary can easily take advantage of any deterministic patrolling strategy, which necessitates the employment of a randomised strategy. While the Markov decision process has been the dominant methodology in computing the penetration detection probabilities on polylines, we apply enumerative combinatorics to characterise the penetration detection probabilities for four penetration configurations. It allows us to provide the closed formulae of these probabilities and facilitates characterising optimal random defence strategies. Comparing to iteratively updating the Markov transition matrices, we empirically show that our method reduces the runtime by up to several hours. This allows us extensive simulations on the two dominant robot movement types for patrolling a perimeter showing that a movement with direction is up to 0.4 more likely to detect an adversary. Therefore, our approach greatly benefits the theoretical and empirical analysis of optimal patrolling strategies with extendability to more complicated attacks and other structured environments

Balancing EV demand at charging stations using multi-agent reinforcement learning

This paper proposes a method for optimising the routing of electric vehicles (EVs) to charging stations via a multi-agent reinforcement learning (MARL) demand balancing system in order to reduce queuing time. This is achieved through simulations via the SUMO simulator to train and test agents to reduce demand by applying reinforcement learning algorithms. Q-learning, PPO and DQN experiments have been conducted to determine a suitable algorithm. The approaches were run on multiple test road networks and a real-world Berlin network with ten charging stations to validate the findings. Varied learning strategies are also explored to determine the appropriate behaviour patterns between the agents, including competitive and cooperative learning as well as a mix of the two. The results of the most promising DQN cooperative implementation applied to the Berlin network achieved an 88.09% reduction in the mean wait times when compared with a greedy approach. The findings of this paper demonstrate the potential for practical benefits of applying MARL systems to real-world environments

A System for Mixed-Reality Holographic Overlays of Real-Time Rendered 3D-Reconstructed Imaging Using a Video Pass-through Head-Mounted Display—A Pathway to Future Navigation in Chest Wall Surgery

Background: Three-dimensional reconstructions of state-of-the-art high-resolution imaging are progressively being used more for preprocedural assessment in thoracic surgery. It is a promising tool that aims to improve patient-specific treatment planning, for example, for minimally invasive or robotic-assisted lung resections. Increasingly available mixed-reality hardware based on video pass-through technology enables the projection of image data as a hologram onto the patient. We describe the novel method of real-time 3D surgical planning in a mixed-reality setting by presenting three representative cases utilizing volume rendering. Materials: A mixed-reality system was set up using a high-performance workstation running a video pass-through-based head-mounted display. Image data from computer tomography were imported and volume-rendered in real-time to be customized through live editing. The image-based hologram was projected onto the patient, highlighting the regions of interest. Results: Three oncological cases were selected to explore the potentials of the mixed-reality system. Two of them presented large tumor masses in the thoracic cavity, while a third case presented an unclear lesion of the chest wall. We aligned real-time rendered 3D holographic image data onto the patient allowing us to investigate the relationship between anatomical structures and their respective body position. Conclusions: The exploration of holographic overlay has proven to be promising in improving preprocedural surgical planning, particularly for complex oncological tasks in the thoracic surgical field. Further studies on outcome-related surgical planning and navigation should therefore be conducted. Ongoing technological progress of extended reality hardware and intelligent software features will most likely enhance applicability and the range of use in surgical fields within the near future

Adaptive incentive engineering in citizen-centric AI

Adaptive incentives are a valuable tool shown to improve the efficiency of complex multiagent systems and could produce win-win situations for all stakeholders. However, their application usage is very limited, partly due to a significant gap between the literature and practice. We argue that overcoming this gap requires addressing four open research challenges. First, the dynamic, volatile and uncertain nature of environments needs to be fully considered. Second, social factors including user acceptance, fairness, ethical considerations and trust have to match end users' expectations and needs. Third, the evaluation of mechanisms and systems has to be robust and focused on real-world outcomes and stakeholder requirements. Finally, all this has to be built on a reliable theoretical foundation. In order to overcome these open challenges in adaptive incentive engineering, tools from the fields of mechanism design and game theory can be used. This will help to achieve the opportunities adaptive incentives can provide to real-world practical environments, producing better AI systems for the benefit of all

An agent-based simulator for maritime transport decarbonisation: Demonstration track

Greenhouse gas (GHG) emission reduction is an important and necessary goal; currently, different policies to reduce GHG emissions in maritime transport are being discussed. Amongst policies, like carbon taxes or carbon intensity targets, it is hard to determine which policies can successfully reduce GHG emissions while allowing the industry to be profitable. We introduce an agent-based maritime transport simulator to investigate the effectiveness of two decarbonisation policies by simulating a maritime transport operator’s trade pattern and fleet make-up changes as a reaction to taxation and fixed targets. This first of its kind simulator allows to compare and quantify the difference of carbon reduction policies and how they affect shipping operations

Electrodiagnosis of Guillain-Barre syndrome in the International GBS Outcome Study: Differences in methods and reference values

Objective: To describe the heterogeneity of electrodiagnostic (EDx) studies in Guillain-Barré syndrome (GBS) patients collected as part of the International GBS Outcome Study (IGOS). Methods: Prospectively collected clinical and EDx data were available in 957 IGOS patients from 115 centers. Only the first EDx study was included in the current analysis. Results: Median timing of the EDx study was 7 days (interquartile range 4–11) from symptom onset. Methodology varied between centers, countries and regions. Reference values from the responding 103 centers were derived locally in 49%, from publications in 37% and from a combination of these in the remaining 15%. Amplitude measurement in the EDx studies (baseline-to-peak or peak-to-peak) differed from the way this was done in the reference values, in 22% of motor and 39% of sensory conduction. There was marked variability in both motor and sensory reference values, although only a few outliers accounted for this. Conclusions: Our study showed extensive variation in the clinical practice of EDx in GBS patients among IGOS centers across the regions. Significance: Besides EDx variation in GBS patients participating in IGOS, this diversity is likely to be present in other neuromuscular disorders and centers. This underlines the need for standardization of EDx in future multinational GBS studies

Electrodiagnosis of Guillain-Barre syndrome in the International GBS Outcome Study: Differences in methods and reference values.

OBJECTIVE: To describe the heterogeneity of electrodiagnostic (EDx) studies in Guillain-Barré syndrome (GBS) patients collected as part of the International GBS Outcome Study (IGOS). METHODS: Prospectively collected clinical and EDx data were available in 957 IGOS patients from 115 centers. Only the first EDx study was included in the current analysis. RESULTS: Median timing of the EDx study was 7 days (interquartile range 4-11) from symptom onset. Methodology varied between centers, countries and regions. Reference values from the responding 103 centers were derived locally in 49%, from publications in 37% and from a combination of these in the remaining 15%. Amplitude measurement in the EDx studies (baseline-to-peak or peak-to-peak) differed from the way this was done in the reference values, in 22% of motor and 39% of sensory conduction. There was marked variability in both motor and sensory reference values, although only a few outliers accounted for this. CONCLUSIONS: Our study showed extensive variation in the clinical practice of EDx in GBS patients among IGOS centers across the regions. SIGNIFICANCE: Besides EDx variation in GBS patients participating in IGOS, this diversity is likely to be present in other neuromuscular disorders and centers. This underlines the need for standardization of EDx in future multinational GBS studies

Electrodiagnosis of Guillain-Barre syndrome in the International GBS Outcome Study: Differences in methods and reference values.

OBJECTIVE: To describe the heterogeneity of electrodiagnostic (EDx) studies in Guillain-Barré syndrome (GBS) patients collected as part of the International GBS Outcome Study (IGOS). METHODS: Prospectively collected clinical and EDx data were available in 957 IGOS patients from 115 centers. Only the first EDx study was included in the current analysis. RESULTS: Median timing of the EDx study was 7 days (interquartile range 4-11) from symptom onset. Methodology varied between centers, countries and regions. Reference values from the responding 103 centers were derived locally in 49%, from publications in 37% and from a combination of these in the remaining 15%. Amplitude measurement in the EDx studies (baseline-to-peak or peak-to-peak) differed from the way this was done in the reference values, in 22% of motor and 39% of sensory conduction. There was marked variability in both motor and sensory reference values, although only a few outliers accounted for this. CONCLUSIONS: Our study showed extensive variation in the clinical practice of EDx in GBS patients among IGOS centers across the regions. SIGNIFICANCE: Besides EDx variation in GBS patients participating in IGOS, this diversity is likely to be present in other neuromuscular disorders and centers. This underlines the need for standardization of EDx in future multinational GBS studies