Improving Robustness in Social Fabric-based Cultural Algorithms

In this thesis, we propose two new approaches which aim at improving robustness in social fabric-based cultural algorithms. Robustness is one of the most significant issues when designing evolutionary algorithms. These algorithms should be capable of adapting themselves to various search landscapes. In the first proposed approach, we utilize the dynamics of social interactions in solving complex and multi-modal problems. In the literature of Cultural Algorithms, Social fabric has been suggested as a new method to use social phenomena to improve the search process of CAs. In this research, we introduce the Irregular Neighborhood Restructuring as a new adaptive method to allow individuals to rearrange their neighborhoods to avoid local optima or stagnation during the search process. In the second approach, we apply the concept of Confidence Interval from Inferential Statistics to improve the performance of knowledge sources in the Belief Space. This approach aims at improving the robustness and accuracy of the normative knowledge source. It is supposed to be more stable against sudden changes in the values of incoming solutions. The IEEE-CEC2015 benchmark optimization functions are used to evaluate our proposed methods against standard versions of CA and Social Fabric. IEEE-CEC2015 is a set of 15 multi-modal and hybrid functions which are used as a standard benchmark to evaluate optimization algorithms. We observed that both of the proposed approaches produce promising results on the majority of benchmark functions. Finally, we state that our proposed strategies enhance the robustness of the social fabric-based CAs against challenges such as multi-modality, copious local optima, and diverse landscapes

A comprehensive survey on cultural algorithms

Peer reviewedPostprin

The Survey, Taxonomy, and Future Directions of Trustworthy AI: A Meta Decision of Strategic Decisions

When making strategic decisions, we are often confronted with overwhelming information to process. The situation can be further complicated when some pieces of evidence are contradicted each other or paradoxical. The challenge then becomes how to determine which information is useful and which ones should be eliminated. This process is known as meta-decision. Likewise, when it comes to using Artificial Intelligence (AI) systems for strategic decision-making, placing trust in the AI itself becomes a meta-decision, given that many AI systems are viewed as opaque "black boxes" that process large amounts of data. Trusting an opaque system involves deciding on the level of Trustworthy AI (TAI). We propose a new approach to address this issue by introducing a novel taxonomy or framework of TAI, which encompasses three crucial domains: articulate, authentic, and basic for different levels of trust. To underpin these domains, we create ten dimensions to measure trust: explainability/transparency, fairness/diversity, generalizability, privacy, data governance, safety/robustness, accountability, reproducibility, reliability, and sustainability. We aim to use this taxonomy to conduct a comprehensive survey and explore different TAI approaches from a strategic decision-making perspective

Catgame: A Tool For Problem Solving In Complex Dynamic Systems Using Game Theoretic Knowledge Distribution In Cultural Algorithms, And Its Application (catneuro) To The Deep Learning Of Game Controller

Cultural Algorithms (CA) are knowledge-intensive, population-based stochastic optimization methods that are modeled after human cultures and are suited to solving problems in complex environments. The CA Belief Space stores knowledge harvested from prior generations and re-distributes it to future generations via a knowledge distribution (KD) mechanism. Each of the population individuals is then guided through the search space via the associated knowledge. Previously, CA implementations have used only competitive KD mechanisms that have performed well for problems embedded in static environments. Relatively recently, CA research has evolved to encompass dynamic problem environments. Given increasing environmental complexity, a natural question arises about whether KD mechanisms that also incorporate cooperation can perform better in such environments than purely competitive ones? Borrowing from game theory, game-based KD mechanisms are implemented and tested against the default competitive mechanism – Weighted Majority (WTD). Two different concepts of complexity are addressed – numerical optimization under dynamic environments and hierarchal, multi-objective optimization for evolving deep learning models. The former is addressed with the CATGame software system and the later with CATNeuro. CATGame implements three types of games that span both cooperation and competition for knowledge distribution, namely: Iterated Prisoner\u27s Dilemma (IPD), Stag-Hunt and Stackelberg. The performance of the three game mechanisms is compared with the aid of a dynamic problem generator called Cones World. Weighted Majority, aka “wisdom of the crowd”, the default CA competitive KD mechanism is used as the benchmark. It is shown that games that support both cooperation and competition do indeed perform better but not in all cases. The results shed light on what kinds of games are suited to problem solving in complex, dynamic environments. Specifically, games that balance exploration and exploitation using the local signal of ‘social’ rank – Stag-Hunt and IPD – perform better. Stag-Hunt which is also the most cooperative of the games tested, performed the best overall. Dynamic analysis of the ‘social’ aspects of the CA test runs shows that Stag-Hunt allocates compute resources more consistently than the others in response to environmental complexity changes. Stackelberg where the allocation decisions are centralized, like in a centrally planned economic system, is found to be the least adaptive. CATNeuro is for solving neural architecture search (NAS) problems. Contemporary ‘deep learning’ neural network models are proven effective. However, the network topologies may be complex and not immediately obvious for the problem at hand. This has given rise to the secondary field of neural architecture search. It is still nascent with many frameworks and approaches now becoming available. This paper describes a NAS method based on graph evolution pioneered by NEAT (Neuroevolution of Augmenting Topologies) but driven by the evolutionary mechanisms under Cultural Algorithms. Here CATNeuro is applied to find optimal network topologies to play a 2D fighting game called FightingICE (derived from “The Rumble Fish” video game). A policy-based, reinforcement learning method is used to create the training data for network optimization. CATNeuro is still evolving. To inform the development of CATNeuro, in this primary foray into NAS, we contrast the performance of CATNeuro with two different knowledge distribution mechanisms – the stalwart Weighted Majority and a new one based on the Stag-Hunt game from evolutionary game theory that performed the best in CATGame. The research shows that Stag-Hunt has a distinct edge over WTD in terms of game performance, model accuracy, and model size. It is therefore deemed to be the preferred mechanism for complex, hierarchical optimization tasks such as NAS and is planned to be used as the default KD mechanism in CATNeuro going forward

Evaluating the impact of an enhanced energy performance standard on load-bearing masonry domestic construction: Understanding the gap between designed and real performance: lessons from Stamford Brook.

This report is aimed at those with interests in the procurement, design and construction of new dwellings both now and in the coming years as the Government’s increasingly stringent targets for low and zero carbon housing approach. It conveys the results of a research project, carried out between 2001 and 2008, that was designed to evaluate the extent to which low carbon housing standards can be achieved in the context of a large commercial housing development. The research was led by Leeds Metropolitan University in collaboration with University College London and was based on the Stamford Brook development in Altrincham, Cheshire. The project partners were the National Trust, Redrow and Taylor Wimpey and some 60 percent of the planned 700 dwelling development has been completed up to June 2008. As the UK house building industry and its suppliers grapple with the challenges of achieving zero carbon housing by 2016, the lessons arising from this project are timely and of considerable value. Stamford Brook has demonstrated that designing masonry dwellings to achieve an enhanced energy standard is feasible and that a number of innovative approaches, particularly in the area of airtightness, can be successful. The dwellings, as built, exceed the Building Regulations requirements in force at the time but tests on the completed dwellings and longer term monitoring of performance has shown that, overall, energy consumption and carbon emissions, under standard occupancy, are around 20 to 25 percent higher than design predictions. In the case of heat loss, the discrepancy can be much higher. The report contains much evidence of considerable potential but points out that realising the design potential requires a fundamental reappraisal of processes within the industry from design and construction to the relationship with its supply chain and the development of the workforce. The researchers conclude that, even when builders try hard, current mainstream technical and organisational practices together with industry cultures present barriers to consistent delivery of low and zero carbon performance. They suggest that the underlying reasons for this are deeply embedded at all levels of the house building industry. They point out also that without fundamental change in processes and cultures, technological innovations, whether they be based on traditional construction or modern methods are unlikely to reach their full potential. The report sets out a series of wide ranging implications for new housing in the UK, which are given in Chapter 14 and concludes by firmly declaring that cooperation between government, developers, supply chains, educators and researchers will be crucial to improvement. The recommendations in this report are already being put into practice by the researchers at Leeds Metropolitan University and University College London in their teaching and in further research projects. The implications of the work have been discussed across the industry at a series of workshops undertaken in 2008 as part of the LowCarb4Real project (see http://www.leedsmet.ac.uk/as/cebe/projects/lowcarb4real/index.htm). In addition, the learning is having an impact on the work of the developers (Redrow and Taylor Wimpey) who, with remarkable foresight and enthusiasm, hosted the project. This report seeks to make the findings more widely available and is offered for consideration by everyone who has a part to play in making low and zero carbon housing a reality

Benchmarking the energy performance of the UK non-domestic stock: a schools case study

Lack of awareness of building performance is often highlighted as a key barrier to improving the operational energy efficiency of non-domestic buildings. In 2008, the Display Energy Certificate (DEC) scheme was implemented in the UK to raise awareness and encourage higher levels of energy efficiency in public sector buildings. The thesis reports a review of the energy benchmarks that underpin the DEC scheme, which reveals that they are no longer appropriate for providing useful or relevant feedback. The research therefore aims to improve understanding of the energy performance of non-domestic buildings, and to explore ways in which their operational energy efficiency can be benchmarked with greater robustness. The research comprises four phases of analysis within which data of varying granularity are analysed to acquire a holistic understanding of the patterns of energy use in English schools and the factors that influence their energy demand. First, the latest DEC records are analysed to assess the robustness of the scheme. Second, the patterns of energy use in primary and secondary schools are analysed in greater detail. Third, multiple regression analyses of energy use in relation to intrinsic building and occupant characteristics are carried out. Last, detailed information about the end-use energy consumption of a small number of modern secondary schools is analysed. The main findings reveal shortcomings of the DEC scheme. The results highlight two key issues associated with the classification system: inappropriate levels of aggregation and misclassification of buildings. Energy benchmarks are found to be inappropriate and out-of-date for the majority of benchmark categories. Correlations between intrinsic features and empirical data on the energy performance of schools were found. The research concludes that the DEC scheme lacks robustness, and that its robustness could be improved by refining the classification system based on empirical data, introducing a framework for keeping up-to-date with the latest trends in energy performance, and producing benchmarks that are relevant to the circumstances of individual buildings

Digital Deception in the Online Dating Space: A Study of Tinder

As technology continues to impart its worldview, the role of communication in the navigation of dating in online spaces has also evolved. This study examines the relationship between communication and digital deception within a selected population of Tinder users. Tinder is a geo-social, location-aware dating application that is used by millions of people around the world. There are three fundamentally specific objectives of this research, which include: first, examining the ways in which dating apps increase the possibility of digital deception; second, exploring ways in which Tinder\u27s design and functionality contribute to the occurrence of digital deception; and finally, identifying and examining the impacts of online deception, particularly in the context of dating apps, on human communication and relationship formation. To obtain first-hand perceptions of online representation and digital deception on Tinder (and as with other online social platforms), 51 Tinder users from Nigeria and Canada were surveyed through their responses to a questionnaire distributed on June 20 and July 11, 2023. The findings of this study suggest that the use of dating apps among youths has increased, leading to prevalent lying and distrust. In the context of using Tinder among the sampled population, Tinder\u27s design, functionality, and online communication in general facilitate and contribute to instances of digital deception, as its affordances only give room to do little, hence, there is often an attempt to ‘put best foot forward’ and the tendency of lying becomes imminent. Appearance influences deception, but some still trust online dating for meaningful connections; platforms should promote honesty

A panel model for predicting the diversity of internal temperatures from English dwellings

Using panel methods, a model for predicting daily mean internal temperature demand across a heterogeneous domestic building stock is developed. The model offers an important link that connects building stock models to human behaviour. It represents the first time a panel model has been used to estimate the dynamics of internal temperature demand from the natural daily fluctuations of external temperature combined with important behavioural, socio-demographic and building efficiency variables. The model is able to predict internal temperatures across a heterogeneous building stock to within ~0.71°C at 95% confidence and explain 45% of the variance of internal temperature between dwellings. The model confirms hypothesis from sociology and psychology that habitual behaviours are important drivers of home energy consumption. In addition, the model offers the possibility to quantify take-back (direct rebound effect) owing to increased internal temperatures from the installation of energy efficiency measures. The presence of thermostats or thermostatic radiator valves (TRV) are shown to reduce average internal temperatures, however, the use of an automatic timer is statistically insignificant. The number of occupants, household income and occupant age are all important factors that explain a proportion of internal temperature demand. Households with children or retired occupants are shown to have higher average internal temperatures than households who do not. As expected, building typology, building age, roof insulation thickness, wall U-value and the proportion of double glazing all have positive and statistically significant effects on daily mean internal temperature. In summary, the model can be used as a tool to predict internal temperatures or for making statistical inferences. However, its primary contribution offers the ability to calibrate existing building stock models to account for behaviour and socio-demographic effects making it possible to back-out more accurate predictions of domestic energy demand

Ono: an open platform for social robotics

In recent times, the focal point of research in robotics has shifted from industrial ro- bots toward robots that interact with humans in an intuitive and safe manner. This evolution has resulted in the subfield of social robotics, which pertains to robots that function in a human environment and that can communicate with humans in an int- uitive way, e.g. with facial expressions. Social robots have the potential to impact many different aspects of our lives, but one particularly promising application is the use of robots in therapy, such as the treatment of children with autism. Unfortunately, many of the existing social robots are neither suited for practical use in therapy nor for large scale studies, mainly because they are expensive, one-of-a-kind robots that are hard to modify to suit a specific need. We created Ono, a social robotics platform, to tackle these issues. Ono is composed entirely from off-the-shelf components and cheap materials, and can be built at a local FabLab at the fraction of the cost of other robots. Ono is also entirely open source and the modular design further encourages modification and reuse of parts of the platform