Transfer learning involves leveraging knowledge gained from solving one task and then using that knowledge to improve performance and reduce subsequent training time on a different but related task. Despite its advantages, recent attention has been directed towards a critical concern relating to the fairness of models trained with transfer learning. A previous study has demonstrated that transfer learning can preserve biases (that are intentionally planted) from the source task, transferring them to the target task. In this paper, we question a different but equally critical problem: whether transfer learning can introduce new biases or lead to greater biases in the target task compared to models trained from scratch. Our investigation reveals that transfer learning has the potential to introduce varying degrees of bias in the target task that were not originally present in the source task. Specifically, in an Alzheimer’s Disease classification task, we show that the use of transfer learning introduces greater bias with respect to sex and age, compared to an equivalent non-transfer learning approach and a simpler model, both trained from scratch and almost as accurate. These findings underscore the need for a comprehensive understanding of the inherent limitations and risks associated with the application of transfer learning, particularly in high-risk applications, e.g. healthcare. This result also suggests the need for further research into how and when transfer learning introduces and amplifies bias.

Despite the extensive research on digital consumer engagement, the dimension of trust in user-generated content (UGC) on social media has been relatively less studied. This study examines the psychological and content-based factors that affect the trustworthiness of UGC on a food brand’s Instagram page, based on data analysis spanning seven years. Employing natural language processing (NLP) techniques, we identified key marketing aspects in both UGC and promotional texts. Specifically, BERT (Bidirectional Encoder Representations from Transformers) was utilized for sentiment analysis, while RoBERTa (A Robustly Optimized BERT Pretraining Approach) was employed for analyzing promotional texts. Through these analytic techniques, we were able to discern subtle nuances in how UGC is perceived and its direct impact on the perceived trustworthiness of the content itself. However, a divergence was observed in the focus areas. Promotional texts leaned towards product features, while UGC highlighted personal experiences and emotional connections. This underscores the importance of brand authenticity and emotional engagement for building trust through UGC. Furthermore, a hierarchical framework of consumer motivations was derived from the UGC through the analysis of promotional messages and the integration of Aristotle’s rhetorical appeals with Trust Theory. This framework reveals a complex interplay between reflective, formative, and overall motives behind consumer engagement.

Abstract Extracting named entities text forms the basis for many crucial tasks such as information retrieval and extraction, machine translation, opinion mining, sentiment analysis and question answering. This paper presents a survey of the research literature on named entity linking, including named entity recognition and disambiguation. We present 200 works by focusing on 43 papers (5 surveys and 38 research works). We also describe and classify 56 resources, including 25 tools and 31 corpora. We focus on the most recent papers, where more than 95% of the described research works are after 2015. To show the efficiency of our construction methodology and the importance of this state of the art, we compare it to other surveys presented in the research literature, which were based on different criteria (such as the domain, novelty and presented models and resources). We also present a set of open issues (including the dominance of the English language in the proposed studies and the frequent use of NER rather than the end-to-end systems proposing NED and EL) related to entity linking based on the research questions that this survey aims to answer.

Self-aware computing systems enable computing systems to reflect on their actions and behavior. This becomes even more relevant in Cyber-Physical Systems where computing systems have to control and interact with elements in the real world. This paper reports on recent advances made in computational self-awareness for cyber-physical systems.

Technological systems have been a part of human life since prehistory. Although they initially took the form of passive tools, such as axes and spoons, the Industrial Revolution saw the advent of powered, mechanized technology, operating “under it’s own steam,” without direct human control over every action. By integrating more complex information processing machinery, automation evolved into autonomy as decision-making and self-regulation became features of modern technology. Now, so-called intelligent systems, embodying techniques from the field of artificial intelligence (AI), are designed with the explicit intention of replicating rational behaviors and the sorts of things that minds do, inside technological systems.At the same time, the study of Artificial Life (ALife) (Langton, 1987) has explored the properties of living systems, both as they are found in nature, as they might be, and as humans can build them. This has exposed a large variety of mechanisms that produce qualities typically associated with life. Examples include self-organization, homeostasis, self-replication, evolution, learning, self-awareness, and many others besides.The Lifelike Computing Systems initiative (Stein et al., 2021b) aims to learn from the study of life and living systems to develop new, useful, “lifelike” systems; a further aim is to identify when such features are of value. The focus of this research direction is primarily on engineered technological systems broadly within the domain of computing.The notion of “lifelike computing” is not intended to separate itself from or replace previous initiatives; in a large number of cases, there are already technologies and research efforts that strongly lean toward lifelike computing systems in specific aspects. Building on a long and highly successful tradition in biologically inspired computing, the “lifelike” vision not only seeks inspiration in the living world but also seeks to replicate its qualities explicitly in technological systems. Indeed, we cannot claim that all bio-inspired systems remain lifelike, nor is this in general even always a desirable outcome for those designing bio-inspired systems. The agenda also goes beyond fundamental ALife research, often rightly exploratory in nature, because it focuses explicitly on building purposeful and reliable technological systems for people, based on ALife principles. Therefore the vision of explicit replication of lifelike qualities in technological systems of value to humanity marks a sharpening of focus.This special issue is a follow-up to the workshop series “Lifelike Computing Systems,” held at the International Conference on Artificial Life in 2020 and 2021 (Stein et al., 2021a), and again in 2022 (Stein et al., 2023). The workshop series hosted diverse talks showcasing early-stage research and work in progress, with topics ranging from plasticity in technical systems to artificial DNA, from self-explaining systems to realistic humanoid and animal robots.We have therefore solicited papers that explore and contribute to the discussion on research questions we deem key to be further explored: Which qualities of life are of high relevance and benefit for the engineering of lifelike computing systems useful to people? Why? How?How can we integrate and combine insights and methodological approaches from existing, related research initiatives, such as cybernetics, self-aware computing, organic computing, and autonomic computing?Which methods from domains like artificial life, bio-inspired computing, artificial intelligence, and self-adaptive and self-organizing systems contribute to achieving lifelike features of computing systems?When is more “lifelike” technology appropriate? What are the challenges associated with embedding technology that is more “lifelike” in society? How can these be tackled?This special issue represents an opportunity for more mature work emerging from this line of research to be presented. It contains four papers that together provide a review, analysis, and critique of the integration of lifelike properties into engineered systems, in many cases proposing concrete recommendations for future research directions and methods.In “Lessons from the Evolutionary Computation Bestiary,” Campelo and Aranha explore and critique the explosion of metaphor-centered metaheuristic methods that have been published in recent years and that claim to be inspired by—in their view—increasingly absurd natural phenomena. Examples surveyed include several different types of birds, mammals, fish, and invertebrates; soccer and volleyball; and even reincarnation, zombies, and gods. The authors acknowledge that metaphors can be powerful inspiration and explanatory tools and that, indeed, the field of metaheuristics has a long history of finding inspiration in natural systems, starting from evolution strategies, genetic algorithms, and ant colony optimization. However, they question the value of the emergence of hundreds of highly similar variants of essentially the same algorithm under different labels. The authors have curated a “bestiary” of such variants over the years, and their article in this issue reviews this, arguing that this proliferation has been counterproductive to scientific progress in the field. They argue that it does little to improve our ability to understand and simulate biological systems and that it can actively impede an improved understanding of how to design and analyze global optimization techniques. The article discusses why this social phenomenon in research may have occurred in recent years and its negative consequences, ending with a call to improve the scientific soundness of metaheuristic research.In “Does the Field of Nature-Inspired Computing Contribute to Achieving Lifelike Features?,” Tzanetos asks whether all nature-inspired algorithms remain lifelike. The article considers the history of evolutionary computation and, as in the first article, the proliferation of many so-called nature-inspired techniques in recent years. The author juxtaposes the value of such techniques in solving hard problems with an analysis to support an argument that the mathematics of these techniques often does not match the source behavior faithfully. In these cases, can it be said that the algorithms are indeed “lifelike,” and if not, does that matter, so long as they provide value in terms of their ability to solve problems intelligently? The article argues that historically, there was greater alignment between the algorithmic models and source behaviors, but this is often not seen in more recent attempts. The article ends by discussing if there is a need for new lifelike features of algorithms, concluding that this is not helpful—instead presenting recommendations for future research in nature-inspired computing, which, the authors argue, would move the field in “the right direction.”In “Assessing Model Requirements for Explainable AI: A Template and Exemplary Case Study,” Heider et al. explore the explainability of decision support systems that use evolutionary rule-based machine learning techniques, more precisely, learning classifier systems (LCSs). Self-adaptive and self-optimizing systems are necessarily dynamic, yet for them to be accepted by people in sociotechnical settings, explanations for machine-made decisions are often essential. The authors argue that rule-based machine learning models, such as LCSs, present an opportunity for transparent machine learning models that naturally support access to explanations. To assist with designing and evaluating such models, they also propose a generic and thus broadly applicable questionnaire template. The template is demonstrated to provide valuable insights for the design of such LCS models in specific scenarios. The approach is illustrated in a manufacturing case study.Finally, in “Artificial Collective Intelligence Engineering: A Survey of Concepts and Perspectives,” Casadei surveys computational techniques based on or harnessing “collectiveness,” often seen in many living systems, to produce capabilities beyond what can be achieved with individual or monolithic systems. A key concept common to these techniques is that such systems can exploit a large number of individuals to produce intelligent collective behavior out of not-so-intelligent components. The article argues that there is a trend in some areas of engineering toward this way of designing technological systems, citing examples such as the Internet of Things, swarm robotics, and crowd computing and emphasizing that these technologies span many techniques, systems, and application areas. An essential finding of the review is that there is substantial fragmentation of this research, however, and that the so-called “verticality” of research communities makes a common fundamental understanding of such systems challenging to achieve. The author argues that an important challenge is identifying, placing in a common structure, and ultimately connecting the different areas and methods addressing intelligent collectives. As such, the article presents a set of questions aimed at mapping out collective intelligence research. It uses this to develop a set of preliminary notions, concepts, and perspectives, as well as associated research opportunities, to develop a more fundamental understanding of computational collective intelligence engineering.The guest editors thank the authors of papers submitted to the “Lifelike Computing Systems” special issue as well as the reviewers, who gave valuable feedback to all the authors. We would also like to thank the organizers of the ALife conferences that hosted the Lifelike Computing Systems workshops as well as all the speakers and participants who contributed to many vibrant debates that informed the direction of the final set of articles in this issue. Last, we thank the Board of Editors of Artificial Life for supporting this special issue.

Self-awareness is a crucial capability of autonomous service-based systems that enables them to self-adapt. There are different types of self-awareness whereby certain types of knowledge are captured at various levels. We argue that effective management of the trade-offs of dependability requirements can be achieved through “seamless” switching between different levels of awareness. However, the assessment of the quality of knowledge to enable dynamic switching between self-awareness levels has not been tackled yet. We propose a general architecture that exploits symbiotic simulation in order to tackle the complexity of assessing the quality of knowledge and attaining the meta-self-awareness property, wherein the system can reflect on its different levels of awareness. We conduct a thorough real-world study in the context of volunteer services. We conclude that a system made meta-self-aware using our approach achieves optimal performance by activating the most suitable awareness level. This comes at the cost of a modest computational overhead.

The research initiative “self-improving system integration” (SISSY) was established with the goal to master the ever-changing demands of system organisation in the presence of autonomous subsystems, evolving architectures, and highly-dynamic open environments. It aims to move integration-related decisions from design-time to run-time, implying a further shift of expertise and responsibility from human engineers to autonomous systems . This introduces a qualitative shift from existing self-adaptive and self-organising systems, moving from self-adaptation based on predefined variation types, towards more open contexts involving novel autonomous subsystems, collaborative behaviours, and emerging goals. In this article, we revisit existing SISSY research efforts and establish a corresponding terminology focusing on how SISSY relates to the broad field of integration sciences. We then investigate SISSY-related research efforts and derive a taxonomy of SISSY technology. This is concluded by establishing a research road-map for developing operational self-improving self-integrating systems.

Abstract In this article, we explore the online multiobject k ‐coverage problem in visual sensor networks. This problem combines k ‐coverage and the cooperative multirobot observation of multiple moving targets problem, and thereby captures key features of rapidly deployed camera networks, including redundancy and team‐based tracking of evasive or unpredictable targets. The benefits of using mobile cameras are demonstrated and we explore the balance of autonomy between cameras generating new subgoals, and those responders able to fulfill them. We show that higher performance against global goals is achieved when decisions are delegated to potential responders who treat subgoals as optional, rather than as obligations that override existing goals without question. This is because responders have up‐to‐date knowledge of their own state and progress toward goals where they are situated, which is typically old or incomplete at locations remote from them. Examining the extent to which approaches overprovision or underprovision coverage, we find that being well suited for achieving 1‐coverage does not imply good performance at k ‐coverage. Depending on the structure of the environment, the problems of 1‐coverage and k ‐coverage are not necessarily aligned and that there is often a trade‐off to be made between standard coverage maximization and achieving k ‐coverage.

Neural networks have been widely used in agent learning architectures; however, learnings for one task might nullify learnings for another. Behavioural plasticity enables humans and animals alike to respond to environmental changes without degrading learned knowledge; this can be achieved by regulating behaviour with neuromodulation—a biological process found in the brain. We demonstrate that by modulating activity-propagating signals, neurally trained agents evolving to solve tasks in dynamic environments that are prone to change can expect a significantly higher fitness than non-modulatory agents and also achieve their goals more often. Further, we show that while behavioural plasticity can help agents to achieve goals in these variable environments, this ability to overcome environmental changes with greater success comes at the cost of highly volatile evolution.

In this article, we make the case for the new class of Self-aware Cyber-physical Systems. By bringing together the two established fields of cyber-physical systems and self-aware computing, we aim at creating systems with strongly increased yet managed autonomy, which is a main requirement for many emerging and future applications and technologies. Self-aware cyber-physical systems are situated in a physical environment and constrained in their resources, and they understand their own state and environment and, based on that understanding, are able to make decisions autonomously at runtime in a self-explanatory way. In an attempt to lay out a research agenda, we bring up and elaborate on five key challenges for future self-aware cyber-physical systems: (i) How can we build resource-sensitive yet self-aware systems? (ii) How to acknowledge situatedness and subjectivity? (iii) What are effective infrastructures for implementing self-awareness processes? (iv) How can we verify self-aware cyber-physical systems and, in particular, which guarantees can we give? (v) What novel development processes will be required to engineer self-aware cyber-physical systems? We review each of these challenges in some detail and emphasize that addressing all of them requires the system to make a comprehensive assessment of the situation and a continual introspection of its own state to sensibly balance diverse requirements, constraints, short-term and long-term objectives. Throughout, we draw on three examples of cyber-physical systems that may benefit from self-awareness: a multi-processor system-on-chip, a Mars rover, and an implanted insulin pump. These three very different systems nevertheless have similar characteristics: limited resources, complex unforeseeable environmental dynamics, high expectations on their reliability, and substantial levels of risk associated with malfunctioning. Using these examples, we discuss the potential role of self-awareness in both highly complex and rather more simple systems, and as a main conclusion we highlight the need for research on above listed topics.

No abstract available.

At the current time, there are several fundamental changes in the way computing systems are being developed, deployed and used. They are becoming increasingly large, heterogeneous, uncertain, dynamic and decentralised. These complexities lead to behaviours during run time that are difficult to understand or predict. One vision for how to rise to this challenge is to endow computing systems with increased self-awareness, in order to enable advanced autonomous adaptive behaviour. A desire for self-awareness has arisen in a variety of areas of computer science and engineering over the last two decades, and more recently a more fundamental understanding of what self-awareness concepts might mean for the design and operation of computing systems has been developed. This draws on self-awareness theories from psychology and other related fields, and has led to a number of contributions in terms of definitions, architectures, algorithms and case studies. This paper introduces some of the main aspects of self-awareness from psychology, that have been used in developing associated notions in computing. It then describes how these concepts have been translated to the computing domain, and provides examples of how their explicit consideration can lead to systems better able to manage trade-offs between conflicting goals at run time in the context of a complex environment, while reducing the need for a priori domain modelling at design or deployment time.

Volunteered Service Composition (VSC) refers to the process of composing volunteered services and resources. These services are typically published to a pool of voluntary resources. Selection and composition decisions tend to encounter numerous uncertainties: service consumers and applications have little control of these services and tend to be uncertain about their level of support for the desired functionalities and non-functionalities. In this paper, we contribute to a self-awareness framework that implements two levels of awareness, Stimulus-awareness and Time-awareness. The former responds to basic changes in the environment while the latter takes into consideration the historical performance of the services. We have used volunteer service computing as an example to demonstrate the benefits that self-awareness can introduce to self-adaptation. We have compared the Stimulus- and Time-awareness approaches with a recent Ranking approach from the literature. The results show that the Time-awareness level has the advantage of satisfying higher number of requests with lower time cost.

We study heterogeneity among nodes in self-organizing smart camera networks, which use strategies based on social and economic knowledge to target communication activity efficiently. We compare homogeneous configurations, when cameras use the same strategy, with heterogeneous configurations, when cameras use different strategies. Our first contribution is to establish that static heterogeneity leads to new outcomes that are more efficient than those possible with homogeneity. Next, two forms of dynamic heterogeneity are investigated: nonadaptive mixed strategies and adaptive strategies, which learn online. Our second contribution is to show that mixed strategies offer Pareto efficiency consistently comparable with the most efficient static heterogeneous configurations. Since the particular configuration required for high Pareto efficiency in a scenario will not be known in advance, our third contribution is to show how decentralized online learning can lead to more efficient outcomes than the homogeneous case. In some cases, outcomes from online learning were more efficient than all other evaluated configuration types. Our fourth contribution is to show that online learning typically leads to outcomes more evenly spread over the objective space. Our results provide insight into the relationship between static, dynamic, and adaptive heterogeneity, suggesting that all have a key role in achieving efficient self-organization.

Dynamic Optimization Problems (DOPs) have been widely studied using Evolutionary Algorithms (EAs). Yet, a clear and rigorous definition of DOPs is lacking in the Evolutionary Dynamic Optimization (EDO) community. In this paper, we propose a unified definition of DOPs based on the idea of multiple-decision-making discussed in the Reinforcement Learning (RL) community. We draw a connection between EDO and RL by arguing that both of them are studying DOPs according to our definition of DOPs. We point out that existing EDO or RL research has been mainly focused on some types of DOPs. A conceptualized benchmark problem, which is aimed at the systematic study of various DOPs, is then developed. Some interesting experimental studies on the benchmark reveal that EDO and RL methods are specialized in certain types of DOPs and more importantly new algorithms for DOPs can be developed by combining the strength of both EDO and RL methods.

To solve multi-objective problems, multiple reward signals are often scalarized into a single value and further processed using established single-objective problem solving techniques. While the field of multi-objective optimization has made many advances in applying scalarization techniques to obtain good solution trade-offs, the utility of applying these techniques in the multi-objective multi-agent learning domain has not yet been thoroughly investigated. Agents learn the value of their decisions by linearly scalarizing their reward signals at the local level, while acceptable system wide behaviour results. However, the non-linear relationship between weighting parameters of the scalarization function and the learned policy makes the discovery of system wide trade-offs time consuming. Our first contribution is a thorough analysis of well known scalarization schemes within the multi-objective multi-agent reinforcement learning setup. The analysed approaches intelligently explore the weight-space in order to find a wider range of system trade-offs. In our second contribution, we propose a novel adaptive weight algorithm which interacts with the underlying local multi-objective solvers and allows for a better coverage of the Pareto front. Our third contribution is the experimental validation of our approach by learning bi-objective policies in self-organising smart camera networks. We note that our algorithm (i) explores the objective space faster on many problem instances, (ii) obtained solutions that exhibit a larger hypervolume, while (iii) acquiring a greater spread in the objective space.

Contemporary software systems are becoming increasingly large, heterogeneous, and decentralised. They operate in dynamic environments and their architectures exhibit complex trade-offs across dimensions of goals, time, and interaction, which emerges internally from the systems and externally from their environment. This gives rise to the vision of self-aware architecture, where design decisions and execution strategies for these concerns are dynamically analysed and seamlessly managed at run-time. Drawing on the concept of self-awareness from psychology, this paper extends the foundation of software architecture styles for self-adaptive systems to arrive at a new principled approach for architecting self-aware systems. We demonstrate the added value and applicability of the approach in the context of service provisioning to cloud-reliant service-based applications.

In this article we present an approach to object tracking handover in a network of smart cameras, based on self-interested autonomous agents, which exchange responsibility for tracking objects in a market mechanism, in order to maximise their own utility. A novel ant-colony inspired mechanism is used to learn the vision graph, that is, the camera neighbourhood relations, during runtime, which may then be used to optimise communication between cameras. The key benefits of our completely decentralised approach are on the one hand generating the vision graph online, enabling efficient deployment in unknown scenarios and camera network topologies, and on the other hand relying only on local information, increasing the robustness of the system. Since our market-based approach does not rely on a priori topology information, the need for any multicamera calibration can be avoided. We have evaluated our approach both in a simulation study and in network of real distributed smart cameras.

Market mechanisms are a means by which resources in contention can be allocated between contending parties, both in human economies and those populated by software agents. Designing such mechanisms has traditionally been carried out by hand, and more recently by automation. Assessing these mechanisms typically involves them being evaluated with respect to multiple conflicting objectives, which can often be nonlinear, noisy, and expensive to compute. For typical performance objectives, it is known that designed mechanisms often fall short on being optimal across all objectives simultaneously. However, in all previous automated approaches, either only a single objective is considered, or else the multiple performance objectives are combined into a single objective. In this paper we do not aggregate objectives, instead considering a direct, novel application of multi-objective evolutionary algorithms (MOEAs) to the problem of automated mechanism design. This allows the automatic discovery of trade-offs that such objectives impose on mechanisms. We pose the problem of mechanism design, specifically for the class of linear redistribution mechanisms, as a naturally existing multi-objective optimisation problem. We apply a modified version of NSGA-II in order to design mechanisms within this class, given economically relevant objectives such as welfare and fairness. This application of NSGA-II exposes tradeoffs between objectives, revealing relationships between them that were otherwise unknown for this mechanism class. The understanding of the trade-off gained from the application of MOEAs can thus help practitioners with an insightful application of discovered mechanisms in their respective real/artificial markets.

Markets are useful mechanisms for performing resource allocation in fully decentralised computational and other systems, since they can possess a range of desirable properties, such as efficiency, decentralisation, robustness and scalability. In this paper we investigate the behaviour of co-evolving evolutionary market agents as adaptive offer generators for sellers in a multi-attribute posted-offer market. We demonstrate that the evolutionary approach enables sellers to automatically position themselves in market niches, created by heterogeneous buyers. We find that a trade-off exists for the evolutionary sellers between maintaining high population diversity to facilitate movement between niches and low diversity to exploit the current niche and maximise cumulative payoff. We characterise the trade-off from the perspective of the system as a whole, and subsequently from that of an individual seller. Our results highlight a decision on risk aversion for resource providers, but crucially we show that rational self-interested sellers would not adopt the behaviour likely to lead to the ideal result from the system point of view.

Computational performance increasingly depends on parallelism, and many systems rely on heterogeneous resources such as GPUs and FPGAs to accelerate computationally intensive applications. However, implementations for such heterogeneous systems are often hand-crafted and optimised to one computation scenario, and it can be challenging to maintain high performance when application parameters change. In this paper, we demonstrate that machine learning can help to dynamically choose parameters for task scheduling and load-balancing based on changing characteristics of the incoming workload. We use a financial option pricing application as a case study. We propose a simulation of processing financial tasks on a heterogeneous system with GPUs and FPGAs, and show how dynamic, on-line optimisations could improve such a system. We compare on-line and batch processing algorithms, and we also consider cases with no dynamic optimisations.

In future massively distributed service-based computational systems, resources will span many locations, organisations and platforms. In such systems, the ability to allocate resources in a desired configuration, in a scalable and robust manner, will be essential.We build upon a previous evolutionary market-based approach to achieving resource allocation in decentralised systems, by considering heterogeneous providers. In such scenarios, providers may be said to value their resources differently. We demonstrate how, given such valuations, the outcome allocation may be predicted. Furthermore, we describe how the approach may be used to achieve a stable, uneven load-balance of our choosing. We analyse the system's expected behaviour, and validate our predictions in simulation. Our approach is fully decentralised; no part of the system is weaker than any other. No cooperation between nodes is assumed; only self-interest is relied upon. A particular desired allocation is achieved transparently to users, as no modification to the buyers is required.