Strategy Space Research Articles

A VNS-based metaheuristic approach for escape interdiction on transportation networks

Containment of dynamic crimes such as kidnapping, bank robbery, hit-and-run, etc., is a challenging issue for law enforcement agencies as the criminal changes his location with time. This type of scenario is generally modelled as an escape interdiction game. We consider two types of players, namely multiple defenders and a single attacker. The defenders choose a sequence of junctions to protect, while the attacker chooses an escape path and the travel time on distinct edges along the path. Hence, both the defender strategy space and the continuous attacker strategy space suffer an exponential growth. This leads to major challenges in the computation of the optimal solution with respect to each player. We develop a double oracle Variable Neighborhood Search (VNS) based meta-heuristic algorithm for finding near-optimal defender and attacker strategies in a time efficient manner. We test the efficiency of this approach against an established exact algorithm named Escape Interdiction Game Solver (EIGS). We use EIGS to generate the benchmark results to check the performance of our heuristic. We also show that the performance of our approach is reasonably good under strict time limitations, as observed in practice.

Smart(Sampling)Augment: Optimal and Efficient Data Augmentation for Semantic Segmentation

Data augmentation methods enrich datasets with augmented data to improve the performance of neural networks. Recently, automated data augmentation methods have emerged, which automatically design augmentation strategies. The existing work focuses on image classification and object detection, whereas we provide the first study on semantic image segmentation and introduce two new approaches: SmartAugment and SmartSamplingAugment. SmartAugment uses Bayesian Optimization to search a rich space of augmentation strategies and achieves new state-of-the-art performance in all semantic segmentation tasks we consider. SmartSamplingAugment, a simple parameter-free approach with a fixed augmentation strategy, competes in performance with the existing resource-intensive approaches and outperforms cheap state-of-the-art data augmentation methods. Furthermore, we analyze the impact, interaction, and importance of data augmentation hyperparameters and perform ablation studies, which confirm our design choices behind SmartAugment and SmartSamplingAugment. Lastly, we will provide our source code for reproducibility and to facilitate further research.

Strategic decision making to achieve a path to net zero

With continued changes to global policy and implementation of greenhouse gas emissions reductions, organisations are increasingly creating goals for net zero emissions. Not knowing where carbon policy is heading and what incentives and penalties will be makes it difficult to make decisions on projects while trying to achieve a path to net zero. Many avenues are currently being investigated from carbon capture and storage (CCS) to operational equipment replacement and leak reduction. There are no standard business models, and this presents significant challenges which need to be understood. Though new, they are not novel, as the industry knows how to face challenges which include evolving policies and tax implications. This paper showcases how energy companies can structure, evaluate and communicate CCS decision making using the Decision Quality process. The fundamentals of Decision Quality are applied to describe a case study which focuses on the challenge for organisations with greenhouse gas emissions, considering which facilities to prioritise, which technologies to cost-effectively capture, how to transport and ultimately how to reuse or store the emissions. The Decision Quality – Scenario Thinking process will be instrumental in understanding the uncertain scenarios in the global carbon arena. We will define the solution space of viable alternative strategies and provide unique insight into the impact of the key risks and uncertainties on the alternatives in play – answering the question, under what terms and conditions might each alternative be viable? Using structured decision techniques, insights into CCS strategies and what it will take to achieve will be gained.

The effectiveness of decentralised electro-thermal load shifting strategies in low voltage network violation management

The IPCC 1.5 °C report highlights the need to reduce UK carbon emissions by 80% 2050, and so it is essential to examine the routes that could be taken to achieve this goal. Replacement of traditional boiler systems with heat pumps (using electricity from clean sources) in residences may aid decarbonization of the energy sector. However, typical LV distribution networks are not designed to carry the increased loads will result from this change, and so it is necessary to explore strategies to mitigate any adverse effects of such loads.In this study, we examine the effect of air source heat pumps on voltage and thermal violations experienced by a typical UK LV network. The effect of decentralized heating system operational strategies (pre-heating, thermostat setback, and space heat buffering) on results is then investigated. Finally, the sensitivity of results to external temperature, network topology, and building fabric standard are examined.It is found that no mixture of strategies is sufficient to significantly improve localized voltage conditions at remote/branch points on LV feeders, as a result of the much greater sensitivity of branch node voltages to power demand of localized loads. Even where EN 50160 voltage standards are applied, elimination of violations is rarely possible.

A sensory-material study of everyday strategies and tactics in the kitchen

This article explores the idea of cooking as an everyday spatial practice which occurs in a sensorial and material way within the kitchen. Rather than focusing on the physical arrangement and the efficient workflow, the kitchen exists as a space of strategy and tactics in cooking. Cooking is a practice that involves material transformation driven by sensorial experience, which further shapes the spatial strategies and tactics performed within a kitchen. This study explores a routine noodle-cooking practice, observing the participant’s sensory experience and material transformation to demonstrate the kitchen as an everyday space of strategies and tactics. The kitchen becomes a spatial arrangement that celebrates the intertwining between the transformation of material with sensory experience. Such intertwine governed operations of cooking strategies and tactics, arranging the timing of movements, altering sequence of activities, and manipulation techniques of material. Such operation arguably insinuates the kitchen as an idea constructed by the intertwined layers of sensory and material transformation, contributing to expanding the idea of the kitchen from an everyday perspective.

Measuring the Non-Transitivity in Chess

In this paper, we quantify the non-transitivity in chess using human game data. Specifically, we perform non-transitivity quantification in two ways—Nash clustering and counting the number of rock–paper–scissor cycles—on over one billion matches from the Lichess and FICS databases. Our findings indicate that the strategy space of real-world chess strategies has a spinning top geometry and that there exists a strong connection between the degree of non-transitivity and the progression of a chess player’s rating. Particularly, high degrees of non-transitivity tend to prevent human players from making progress in their Elo ratings. We also investigate the implications of non-transitivity for population-based training methods. By considering fixed-memory fictitious play as a proxy, we conclude that maintaining large and diverse populations of strategies is imperative to training effective AI agents for solving chess.

Resonance as a Design Strategy for AI and Social Robots.

Resonance, a powerful and pervasive phenomenon, appears to play a major role in human interactions. This article investigates the relationship between the physical mechanism of resonance and the human experience of resonance, and considers possibilities for enhancing the experience of resonance within human–robot interactions. We first introduce resonance as a widespread cultural and scientific metaphor. Then, we review the nature of “sympathetic resonance” as a physical mechanism. Following this introduction, the remainder of the article is organized in two parts. In part one, we review the role of resonance (including synchronization and rhythmic entrainment) in human cognition and social interactions. Then, in part two, we review resonance-related phenomena in robotics and artificial intelligence (AI). These two reviews serve as ground for the introduction of a design strategy and combinatorial design space for shaping resonant interactions with robots and AI. We conclude by posing hypotheses and research questions for future empirical studies and discuss a range of ethical and aesthetic issues associated with resonance in human–robot interactions.

Reliability-based inspection and maintenance planning of a nuclear feeder piping system

Inspection and maintenance (I&M) is essential to ensure the integrity of feeder pipes, which are parts of the primary heat transport system in a nuclear power plant. The pipes are subject to flow accelerated corrosion (FAC), which can compromise the integrity of the piping system and lead to high repair costs. We explore the opportunity for improving I&M strategies while ensuring that the system still maintains an acceptable level of reliability. To this aim, a reliability-based planning framework is proposed, in which every pipe in the system meets the minimum thickness requirement at a specified annual probability. With this planning framework we can a) evaluate the performance of any I&M strategy constrained to a fixed reliability criterion, without requiring this strategy to be specifically designed for such a criterion; and b) find an I&M strategy optimized for this reliability level using a heuristic description of the strategy space. We demonstrate the framework with a case study, where the wall thinning due to FAC is modeled as a Gamma process with uncertain parameters. We compare the expected life-cycle cost of multiple strategies for I&M of a feeder system with 480 pipes. The proposed approach is compared with an I&M strategy currently used by the industry, which highlights the efficiency of the proposed optimization method.

What is the extent of a frequency-dependent social learning strategy space?

Models of frequency-dependent social learning posit that individuals respond to the commonality of behaviours without additional variables modifying this. Such strategies bring important trade-offs, e.g. conformity is beneficial when observing people facing the same task but harmful when observing those facing a different task. Instead of rigidly responding to frequencies, however, social learners might modulate their response given additional information. To see, we ran an incentivised experiment where participants played either a game against nature or a coordination game. There were three types of information: (a) choice frequencies in a group of demonstrators; (b) an indication of whether these demonstrators learned in a similar or different environment; and (c) an indication about the reliability of this similarity information. Similarity information was either reliably correct, uninformative or reliably incorrect, where reliably correct and reliably incorrect treatments provided participants with equivalent earning opportunities. Participants adjusted their decision-making to all three types of information. Adjustments, however, were asymmetric, with participants doing especially well when conforming to demonstrators who were reliably similar to them. The overall response, however, was more fluid and complex than this one case. This flexibility should attenuate the trade-offs commonly assumed to shape the evolution of frequency-dependent social learning strategies.

Optimizing Dosage-Specific Treatments in a Multi-Scale Model of a Tumor Growth.

The emergence of cell resistance in cancer treatment is a complex phenomenon that emerges from the interplay of processes that occur at different scales. For instance, molecular mechanisms and population-level dynamics such as competition and cell–cell variability have been described as playing a key role in the emergence and evolution of cell resistances. Multi-scale models are a useful tool for studying biology at very different times and spatial scales, as they can integrate different processes occurring at the molecular, cellular, and intercellular levels. In the present work, we use an extended hybrid multi-scale model of 3T3 fibroblast spheroid to perform a deep exploration of the parameter space of effective treatment strategies based on TNF pulses. To explore the parameter space of effective treatments in different scenarios and conditions, we have developed an HPC-optimized model exploration workflow based on EMEWS. We first studied the effect of the cells’ spatial distribution in the values of the treatment parameters by optimizing the supply strategies in 2D monolayers and 3D spheroids of different sizes. We later study the robustness of the effective treatments when heterogeneous populations of cells are considered. We found that our model exploration workflow can find effective treatments in all the studied conditions. Our results show that cells’ spatial geometry and population variability should be considered when optimizing treatment strategies in order to find robust parameter sets.

Assessment of the Interrelationships of Soil Nutrient Balances with the Agricultural Soil Emissions and Food Production

Sustainable and adjusted soil management practices are crucial for soil quality, namely in terms of the nutrient budget. On the other hand, soil characteristics are interlinked with agricultural sustainability and food supply. In other words, soil quality influences agricultural performance and food chains, but it is also impacted by agricultural activities. In this context, this research aims to evaluate the spatial correlations of the soil nutrient balance around the world and analyse how this variable is interrelated with agricultural soil emissions, agricultural output, and food supply. To achieve these goals, data from the FAOSTAT database were considered. This statistical information was analysed with spatial autocorrelation approaches to identify spatial clusters around the world that can be considered as a basis for designing common policies. To perform panel data regressions to identify marginal effects between variables, data were first evaluated using correlation matrices and factor analysis. The results highlight that there is space for common strategies worldwide to preserve soil quality, as in some parts of the world the problems are similar. In these frameworks, the international organizations may have a determinant contribution.

Why don't asset managers accelerate ESG investing? A sentiment analysis based on 13,000 messages from finance professionals

AbstractMitigating climate change effects requires investors (and their proxies, fund managers) to shift their business‐as‐usual strategies. This article analyzes Environmental, Social, and Governance (ESG) investing behavior through more than 13,000 messages exchanged by finance professionals from 2017 to 2020. There is a consensus that low and high ESG firms' discrimination is a necessary but not sufficient condition for ESG investing. Moreover, it is one thing for fund managers to claim that they are integrating ESG factors in their portfolios and another to do it properly. The restriction of the strategy space, internal and external transaction costs, and data quality are still viewed as overwhelming obstacles for seamlessly integrating ESG into financial portfolios. Sentiment analysis indicates that asset managers hold a negative view of ESG investing, surprising as ethical investing is becoming increasingly common. Unlocking the potential positive externalities from ESG investing may require regulators and investors' actions to improve the quality of information disclosure and pressure fund managers into incorporating nonfinancial criteria in their investment models.

Intersectional Organizing and Educational Justice: How Lived Experience Influences Community Organizers’ Understanding and Practice of Intersectional Organizing

Recently, education organizers working with youth and parents have taken intersectional approaches. Little research, however, considers how personal experience informs these understandings and the approaches organizers take. The purpose of this study is to understand how social locations like gender identity and race inform organizers’ understanding and practice of intersectional organizing. We interviewed eight community, parent, and youth organizers with a variety of racial and gender identities. The organizers are members of the People’s Think Tank (PTT), an idea and strategy space that includes organizers and activist scholars working together to strengthen and expand the educational justice movement. We found three different practices of intersectional organizing. One subgroup of organizers understands intersectional organizing as a tool for interrogating power and privilege. A second subgroup of organizers understands intersectional organizing as centering the lives of the most marginalized. Finally, the third subgroup of organizers stated that terms like “interconnected” and “intergenerational” are preferred terms when talking about intersectional organizing. Our analysis finds that an individuals’ lived experience impacts how they understand intersectional organizing and that engagement in intersectional organizing helps individuals better understand their social identities. We discuss the relationship between these distinct approaches and how they relate to intersectional organizing.

Diversity Effects on Canopy Structure Change throughout a Growing Season in Experimental Grassland Communities

Increasing plant diversity commonly enhances standing biomass and other ecosystem functions (i.e., carbon fluxes, water use efficiency, herbivory). The standing biomass is correlated with vegetation volume, which describes plant biomass allocation within a complex canopy structure. As the canopy structure of plant communities is not static throughout time, it is expected that its changes also control diversity effects on ecosystem functioning. Yet, most studies are based on one or two measures of ecosystem function per year. Here, we examine the temporal effects of diversity of grassland communities on canopy structural components in high temporal (bi-weekly throughout the growing season) and spatial resolutions as a proxy for ecosystem functioning. Using terrestrial laser scanning, we estimate metrics of vertical structure, such as biomass distribution (evenness) and highest biomass allocation (center of gravity) along height strata. For horizontal metrics, we calculated community stand gaps and canopy surface variation. Our findings show that species-rich communities start filling the vertical space (evenness) earlier in the growing season, suggesting a more extended period of resource use (i.e., light-harvesting). Moreover, more diverse communities raised their center of gravity only at the peak of biomass in spring, likely triggered by higher interspecific competition inducing higher biomass allocation at upper layers of the canopy. Furthermore, richer communities were clumpier only after mowing, revealing species-specific differences in regrowth. Lastly, species richness strongly affected canopy variation when the phenology status and height differences were maximal, suggesting differences in plant functional strategies (space to grow, resource use, and flowering phenology). Therefore, the effects of diversity on ecosystem functions depending on those structural components such as biomass production, decomposition, and herbivory, may also change throughout the season due to various mechanisms, such as niche differences, increased complementarity, and temporal and spatial variation in biological activity.

Fast and Simple Solutions of Blotto Games

The Colonel Blotto game (initially introduced by Borel in 1921) is commonly used for analyzing a wide range of applications from the U.S.Ppresidential election to innovative technology competitions to advertising, sports, and politics. After around a century Ahmadinejad et al. provided the first polynomial-time algorithm for computing the Nash equilibria in Colonel Blotto games. However, their algorithm consists of an exponential-size LP solved by the ellipsoid method, which is highly impractical. In “Fast and Simple Solutions of Blotto Games,” Behnezhad, Dehghani, Derakhshan, Hajighayi, and Seddighin provide the first polynomial-size LP formulation of the optimal strategies for the Colonel Blotto game using linear extension techniques. They use this polynomial-size LP to provide a simpler and significantly faster algorithm for finding optimal strategies of the Colonel Blotto game. They further show this representation is asymptotically tight, which means there exists no other linear representation of the strategy space with fewer constraints.

Fuzzy Kakutani-Fan-Glicksberg fixed point theorem and existence of Nash equilibria for fuzzy games

In this paper, we give fuzzy generalizations to the well-known Brouwer fixed point theorem and Kakutani-Fan-Glicksberg fixed point theorem. As applications, we apply the fuzzy Kakutani-Fan-Glicksberg fixed point theorem to derive an existence theorem for Nash equilibria in generalized fuzzy games with locally convex Hausdorff topological vector spaces for strategy spaces and/or discontinuous payoff functions which generalizes existence theorems for Nash equilibria in generalized games.

Equilibrium stacks for a three-person game on a product of staircase-function continuous and finite strategy spaces

Abstract A method of solving a three-person game defined on a product of staircase-function strategy spaces is presented. The spaces can be finite and continuous. The method is based on stacking equilibria of “short” three-person games, each defined on an interval where the pure strategy value is constant. In the case of finite three-person games, which factually are trimatrix games, the equilibria are considered in general terms, so they can be in mixed strategies as well. The stack is any interval-wise combination (succession) of the respective equilibria of the “short” trimatrix games. Apart from the stack, there are no other equilibria in this “long” trimatrix game. An example is presented to show how the stacking is fulfilled for a case of when every “short” trimatrix game has a pure-strategy equilibrium. The presented method, further “breaking” the initial “long” game defined on a product of staircase-function finite spaces, is far more tractable than a straightforward approach to solving directly the “long” trimatrix game would be.

Cognitive WSN Control Optimization for Unmanned Farms Under the Two-Layer Game

With the deep integration of modern technology and agricultural production, the development of unmanned precision agriculture has become a breakthrough for agricultural upgrading. Unmanned farms rely on multi-source data provided by dense wireless sensor networks (WSN), which causes greater pressure on the scarce spectrum resources. Combining fuzzy clustering and game theory, this paper studies a joint optimization method for cognitive wireless sensor networks (CWSN) to optimize agricultural data transmission and make full use of network resources. Two objective functions are designed to maximize the network energy efficiency and minimize the transmit power, so as to optimize the transmit power and transmission channel in CWSN. Different optimization objectives are regarded as different players, which turns the multi-objective optimization into a game decision-making problem. By defining the influence model of design variables on different objective functions, fuzzy clustering is adopted to divide the strategy subspace for each player. Then, this paper studies a two-layer cooperative game algorithm. Each node chooses the transmit power and channel in the strategy space based on their price function. The game scheme is obtained by a multi-round two-layer cooperative game. The experimental results show that compared with NSGA-ò, the reinforcement learning (RL) algorithm, and the non-cooperative game method, the joint optimization algorithm can reduce network interference and improve network QoS.

Constrained Bayesian Optimization for Minimum-Time Charging of Lithium-Ion Batteries

The fast charging of lithium-ion batteries while minimizing battery degradation is a key challenge to battery community. Difficulties in this optimization are the high dimensionality of parameter space of charging strategies, significant variability between batteries (even from the same production line), and limited quantitative information on battery degradation mechanisms. Current approaches to addressing these challenges are model-based optimization and grid search. Model-based methods are limited by the insufficient complexity and accuracy of electrochemical models – especially in the early stage of development when a new battery chemistry is being introduced to the market – and grid search methods are expensive in terms of testing time and cells. This article proposes a data-driven Bayesian optimization (BO) approach for minimum charging time problem, in which an acquisition function of constrained expected improvement is employed to explicitly handle constraints that limit degradation. In addition, continuous-varied-current charging protocols are introduced into the proposed BO approach by utilizing the technique of polynomial function expansions. The effectiveness of the proposed approach is demonstrated on the LIONSIMBA, a porous electrode theory-based battery simulator. The simulation results show that the proposed BO-based charging approach with continuous current profile outperforms the commonly used constant current constant voltage (CC-CV) method for the minimum charging time problem. Moreover, the decrease in the minimum charging time and increase in its variance with increasing number of degrees of freedom used in charging protocols is also quantified.

Neighborhood strong superiority and evolutionary stability of polymorphic profiles in asymmetric games

<p style='text-indent:20px;'>In symmetric evolutionary games with continuous strategy spaces, Cressman [<xref ref-type="bibr" rid="b6">6</xref>] has proved an interesting stability result for the associated replicator dynamics relating the concepts of neighborhood superiority and neighborhood attracting for polymorphic states with respect to the weak topology. Similar stability results are also established for monomorphic profiles in 2-player asymmetric games [<xref ref-type="bibr" rid="b8">8</xref>]. In the present paper, we use the model of asymmetric evolutionary games introduced by Mendoza-Palacios and Hernández-Lerma [<xref ref-type="bibr" rid="b17">17</xref>] and obtain a stability result for polymorphic profiles in <inline-formula><tex-math id="M1">\begin{document}$ n $\end{document}</tex-math></inline-formula>-player asymmetric evolutionary games with continuous action spaces using the concept of neighborhood strong superiority (Definition 2.3). In particular, we prove that neighborhood strong superior polymorphic profiles are neighborhood attracting. It is also shown that a polymorphic neighborhood strong superior profile is in fact a vector of Dirac measures. Moreover, we establish that the notion of neighborhood strong superiority does not imply strong uninvadability and vice-versa.</p>