The development of industrial control systems (ICS) has led to security vulnerabilities in ICS protocols, which have caused significant threats to these systems. Fuzzing is a highly effective technique for detecting vulnerabilities by exposing the target program to a large volume of malformed and unexpected input. Traditional fuzzing methods are time-consuming and have low acceptance rates and coverage due to the manual extraction of specifications, lack of guidance, and blind mutations, limiting their efficiency in discovering vulnerabilities. Currently, the Generative Adversarial Network (GAN)-based fuzzing methods are used to learn the syntax and format of the input data and generate valid test cases. However, they still suffer from low diversity in the generated test cases, which can affect the potential of triggering vulnerabilities. We have proposed a transformerbased fuzzing framework called TFVDFuzzer to solve these issues. In this framework, we leverage the Transformer model to automate the learning of ICS protocol specifications, especially Modbus, significantly speeding up the fuzzing process and enhancing the test case acceptance rate. Furthermore, we used a deep learning model that employs an attention mechanism to determine the appropriate bytes for mutations to enhance the diversity of the test cases. The TFVDFuzzer is assessed using a benchmark Modbus dataset. It outperforms Peach, Fuzzowski, and NCMFuzzer with significant improvements, achieving a Test Case Reception Rate (TCRR) of $\mathbf{9 8. 1 5 \%}$ compared to their highest values of $\mathbf{5 1. 4 2 \%, ~} \mathbf{6 9. 0 2 \%}$, and $\mathbf{9 4. 2 5 \%}$, respectively, and a Test System Anomaly Rate (TSAR) of $3.1 \%$ relative to their highest values of $0.445 \%, 0.475 \%$, and $0.96 \%$, respectively.

Multi-agent literature explores personifying artificial agents with personality, emotions or cognitive biases to produce “typical”, believable agents. In this study, we demonstrate the potential of endowing artificial agents with a motivation, using human implicit motivation psychology theory that introduces 3 motive profiles - power, achievement and affiliation, to create diverse, risk-aware agents. We first devise a framework to model these motivated agents (or agents with any inherent behavior), that can activate different strategies depending on the circumstances. We conduct experiments on a fire-fighting task domain, evaluate how motivated teams perform, and draw conclusions on appropriate team compositions to be deployed in environments with different risk levels. Our framework generates predictable agents as their resulting behaviors align with the inherent characteristics of their motives. We find that motivational diversity within teams is beneficial in dynamic collaborative environments, especially as the task risk level increases. Furthermore, we observed that the best composition in terms of the performance metrics used to evaluate team compositions, does not remain the same as the collaboration level required to achieve goals changes. These results have implications for future designs of risk-aware autonomous teams and Human-AI teams, as they highlight the prospects of creating better artificial teammates and performance gains that could be achieved through anthropomorphized motivated agents.

In day-to-day life, stress can arise due to various factors including work–life demands, external situations, and health issues. Stress becomes a concern when it affects a person's mental health, and sometimes it can even result in other chronic illnesses. Currently, stress-detection studies in the literature are often limited to laboratory studies or to specific situations. However, daily stressors are ongoing, and therefore, detection of stress in everyday life (outside the laboratory environments) is important to improve the wellbeing of individuals. Computer games are entertainment media that can be found in almost every household in the modern society. Statistics show that people spend hours playing computer games daily. The amount of data that gameplay generates and interactivity they provide via various human computer interfaces have a lot of potential in identifying behavior patterns of the players that could assist in the process of stress detection. As such, this survey attempts to identify the extent to which computer games can be used as a medium for stress detection. Toward this end, this survey reviews the existing stress-detection studies, both laboratory techniques as well as the techniques that can be used in a home-based environment. Finally, it summarizes the stress-detection techniques that can be used within games in order to make it an everyday technology that can be used to detect and monitor stress. In addition, it is expected that development of such a technology will be useful in providing objective data to the healthcare professionals for intervention and management. Such a technology is even more required in the current unprecedented situation the world has faced due to the COVID-19 pandemic as it can be developed as a technology to manage mental health issues people are facing due to home isolation.

This paper presents a grammar-based evolutionary model to facilitate autonomous emergence of task allocation for intelligent multi-agent systems. The approach adopts a context-free grammar to determine the behaviour rule syntax. This allows for flexibility in evolving task allocation under multiple and dynamic constraints without manual rule design and parameter tuning. Experimental evaluations conducted with a target discovery simulation illustrate that the grammar-based model performs successfully in both dynamic and non-dynamic conditions. A statistically significant performance improvement is shown compared to an algorithm developed with the broadcast of local eligibility mechanism and a genetic programming mechanism. Grammatical evolution can achieve near-optimal solutions under restrictions applied on the number of agents, targets and the time allowed. Further, analysis of the evolved rule structures shows that grammatical evolution can identify less complex rule structures for behaviours while maintaining the expected level of performance. The results infer that the proposed model is a promising alternative for dynamic task allocation with human interactions in complex real-world domains.

This paper presents a grammar-based evolutionary approach that incorporates abstractions to learn complex collective behaviours through their simpler representations. We propose modifications to the grammar syntax design and genome structure to facilitate evolution of abstractions in separate genome partitions. Two abstraction techniques based on behavioural decomposition and environmental scaffolding are presented to derive these simpler representations. Parallel and incremental learning architectures incorporated with grammatical evolution (GE) are investigated with three complex problems to evaluate their potential in generating collective multi-agent behaviours. The results infer that both learning architectures surpass a generic GE model in performance for evolving complex behaviours. Furthermore, using environmental scaffolding reduces the robustness of the model than when only the behavioural decomposition technique is used. However, it has more potential to generate solutions with better fitness than when scaffoldings are not used. The evaluations suggest that, by incorporating abstraction learning architectures with grammar-based evolution can significantly improve the performance of an agent system in complex problem domains.

Trust has been widely recognized as particularly significant among the factors influencing team performance. Trust directly impacts team performance as it plays a pivotal role in the decisions that each team member (each agent) makes regarding their own actions and their interactions with fellow team members (other agents). Hence, determining the appropriate actions of each agent in the team based on perceived trust information is critical to ensure optimal team performance. However, there is no generic mechanism dedicated to such a problem. This paper addresses this problem by proposing a novel trust architecture which integrates differentiated trust with response strategies. Differentiated trust is multidimensional trust with each trust dimension representing trust in an agent's abilities to perform the task associated with that dimension. With differentiated trust, an agent can differentiate the trustworthiness of another agent in performing different subtasks (a secondary task or a portion of the primary task). To further fulfill the transition from perceptual trust to practical actions, responses strategies are introduced. Each response strategy associates trust levels with the available actions through a distinct deterministic strategy. The high dimensional trust enabled by the differentiated trust is used as the input of a response strategy for more nuanced manipulation of the interactions. The impact of the proposed trust architecture is demonstrated through an experimental investigation. A platform simulating team performance is built based on a food foraging task. Scenarios embodying different types and proportions of a priori flawed agents are introduced to enable the system to distinguish between agents in terms of trust; thus, providing a potential to optimize team performance through the trust architecture. It is demonstrated that the proposed trust architecture enables optimized team performance in various scenarios involving agents with different trustworthiness by the appropriate determination of each agent's actions in the interactive teamwork.

Active video games have proven to be an effective means of coupling gaming and exercise, while also providing a more enjoyable experience. In this article, we investigate two elliptical walker-based exertion systems for active video game locomotion. The impacts on heart rate, respiratory rate, and sense of presence were investigated as a result of using different locomotion systems by employing 20 subjects. Four configurations of elliptical walker-based exertion systems were compared with two commonly used sedentary game controllers. The results showed that participants' mean heart and respiratory rate were significantly increased as a result of using the active locomotion systems. Seventy percent of participants stated they would likely use the exertion systems at home, with a mean estimated weekly use time of 4.2 h. It is expected that the ongoing use of the exertion systems would exceed the physical activity guidelines recommended by the World Health Organisation. While participants subjectively did not feel more present, they were more involved with the virtual experience when using the exertion systems compared to the sedentary configurations. It was found that familiarity with the controls, and weekly time spent playing video games both had a significant impact on player exertion and subjective presence.

With the continuous technological advancement in the game industry, millions of players engage in various online games everyday. Player population size of games ebb and flow through time as a complex series. Analyzing these player population numbers in a shorter time window, such as weekly, could help generate insights that enrich the understanding about low-level population fluctuation patterns of online games. However, this area of game data analytics still has space for further enhancement. This study focuses on discovering patterns of weekly player population fluctuations, that could aid in comprehending how the population of various kind of games change within a framing window of a week. We use player population time series of 1963 games available on Steam. Utilizing several trend removal techniques and conducting seasonality detection we identify that 77% of games display a recurring weekly pattern in player population fluctuations. Moreover, our dynamic time warping based cluster analysis shows that there are 9 diverse weekly player population fluctuation patterns. Among these 9, the governing pattern visible in the majority of games displays that the player population is higher towards the weekend. Finally, we scrutinize the tags, age requirements and overall population size of games in each cluster associated with the diverse patterns to generate insights about the characteristics of games associated with each weekly population pattern.

This paper presents an evolutionary computing based approach to automatically synthesise swarm behavioural rules from their atomic components, thus making a step forward in trying to mitigate human bias from the rule generation process, and leverage the full potential of swarm systems in the real world by modelling more complex behaviours. We identify four components that make-up the structure of a rule: control structures, parameters, logical/relational connectives and preliminary actions, which form the rule space for the proposed approach. A boids simulation system is employed to evaluate the approach with grammatical evolution and genetic programming techniques using the rule space determined. While statistical analysis of the results demonstrates that both methods successfully evolve desired complex behaviours from their atomic components, the grammatical evolution model shows more potential in generating complex behaviours in a modularised approach. Furthermore, an analysis of the structure of the evolved rules implies that the genetic programming approach only derives non-reusable rules composed of a group of actions that is combined to result in emergent behaviour. In contrast, the grammatical evolution approach synthesises sound and stable behavioural rules which can be extracted and reused, hence making it applicable in complex application domains where manual design is infeasible.

With the fast evolution in the area of processing units and sensors, wearable devices are becoming more popular among people of all ages. Recently, there has been renewed interest in exploiting the capabilities of wearable sensors for person recognition while undertaking their normal daily activities. In this paper, we focus on utilizing motion information of known daily activities gathered from wearable sensors for recognizing the person. The analysis of the results demonstrates that different fundamental classification factors have an impact on person recognition success-rates. Furthermore, the results of comparison among subjects prove that some subjects have high classification results and can be easily identifiable compared to other subjects which have high confusability rates. Lastly, a significant improvement in subject classification success rate was found for activities with little or no movement which can successfully distinguish among persons and hence producing higher classification results compared to activities with large movement.

Modern video games come with highly realistic graphics enabling the players to interact with visually rich virtual worlds. Realistic (life-like) animation of nonplayer characters (NPCs) in such virtual worlds is particularly important to enhance the gaming experience. Multiagent systems are one effective approach to synthesize life-like behaviors and interactions by codifying simple rules into NPCs (each NPC as an autonomous agent). However, such behaviors generally come at the cost of increasing computational expense and complexity in terms of aspects such as number of rules and parameters. Therefore, the desire for high fidelity (highly realistic) behaviors is often in conflict with the drive for low complexity. Multiobjective evolutionary algorithms provide a sophisticated mechanism to optimize two or more conflicting objectives simultaneously. However, evolutionary computing techniques need an appropriate objective function to drive the exploration in the correct direction. Pairing of evolutionary techniques and multiagent systems is challenging in the classes of problems in which the fitness is evaluated based on human aesthetic judgment rather than on objective forms of measurements. In this study, we present a multiobjective evolutionary framework to evolve low complexity and high fidelity multiagent systems by utilizing a machine learning system trained by bootstrapping human aesthetic judgment. We have gathered empirical data in three problem areas-simulation of conversational group dynamics, sheepdog herding behaviors, and traffic dynamics, and show the effectiveness of our approach in deriving low complexity and high fidelity multiagent systems. Further, we have identified common properties of the Pareto-optimal frontiers in the three problem areas that can ultimately lead to an understanding of a relationship between simulation model complexity and behavior fidelity. This understanding will be useful in deciding which level of behavioral fidelity is required for the characters in video games based on the distance to the camera, importance to the scene, and available computational resources.

Collaboration or teaming is a powerful strategy in which a group of agents work towards one or more common goals. Teaming imposes a base-load cognitive burden upon a participating agent - to be an effective team member the agent must maintain awareness of fellow team-members and employ that information in its decision-making processes. Via a multi-objective evolutionary approach (NSGA-II) the paper explores the Situational Awareness (SA) and computational processing costs necessary for an agent to participate effectively in a proto-team - a flock. Results show that relatively low levels of the available SA information or computational processing (frequency of application of the rules to determine desired velocity) are sufficient to ensure flocking is achieved and maintained. The proposed methodology is expandable to include human teams, human-machine hybrid teams, and exploration of centralised versus decentralised control.

Sheepdog herding behaviors demonstrate an interesting form of interactions between two classes of agents - sheep and the dog. The nature of the interactions between sheep and the dog takes a special form of competition which is different to the traditional prey-predator interactions where the success of prey depends on the failure of the predator and vice versa. In consequent, the development of an appropriate objective function to efficiently co-evolve successful sheepdog herding behaviors becomes challenging. This paper presents a framework to efficiently co-evolve sheepdog herding behaviors utilizing the simple rule based agent approach in order to derive high fidelity behavior dynamics and discusses the challenges involved in the process.

Simple rule based Multi Agent Systems are widely used in the fields of social simulations and game artificial intelligence in order to incorporate the complexity and richness of action and interaction into the characters in the virtual environments while keeping computational cost low. This paper presents an approach to synthesize the spatio-temporal dynamics of groups in standing conversation: four simple spatial rules form the building-blocks and a framework to automatically evolve rule and the parameter space by bootstrapping a-priori human judgment on the aesthetic quality of the simulations is introduced. The framework consists of a Genetic Algorithm and a scorer (fitness function) developed based on a machine learning system trained using human evaluations. The results of the study suggest that the framework is capable of deriving optimal rule and parameter combinations utilizing only a relatively small set of human scored training data. Further, the relationship between rule-complexity and visual fidelity is explored.