Behavior Of Adaptive Systems Research Articles

Requirements development for IoT systems with UCM4IoT

The engineering of IoT (Internet of Things) systems brings about various challenges due to the inherent complexities associated with such adaptive systems. Addressing the adaptive nature of IoT systems in the early stages of the development life cycle is essential for developing a complete and precise system specification. In this paper, we propose a use case-based modelling language, UCM4IoT, to support requirements elicitation and specification of IoT systems. UCM4IoT takes into account the heterogeneity of IoT systems and provides domain-specific language constructs to model the different facets of IoT systems. The language also incorporates the notion of exceptional situations and adaptive system behaviour. Our language is supported with a textual modelling environment to assist modellers in writing use cases. The environment supports syntax-directed editing, validation of use case models, and requirements analysis. The proposed language and tool is demonstrated and evaluated with two case studies: smart store system and smart fire alarm system.

Framework for qualifying exoskeletons as adaptive support technology.

The fifth industrial revolution and the accompanying influences of digitalization are presenting enterprises with significant challenges. Regardless of the trend, however, humans will remain a central resource in future factories and will continue to be required to perform manual tasks. Against the backdrop of, e.g., societal and demographic changes and skills shortage, future-oriented support technologies such as exoskeletons represent a promising opportunity to support workers. Accordingly, the increasing interconnection of human operators, devices, and the environment, especially in human-centered work processes, requires improved human-machine interaction and further qualification of support systems to smart devices. In order to meet these requirements and enable exoskeletons as a future-proof technology, this article presents a framework for the future-oriented qualification of exoskeletons, which reveals potential in terms of user-individual and context-dependent adaptivity of support systems. In this context, a framework has been developed, allowing different support situations to be classified based on elementary functions. Using these support function dependencies and characteristics, it becomes possible to describe adaptive system behavior for human-centered support systems such as exoskeletons as a central aspect. For practical illustration, it is shown for an exemplary active exoskeleton using the example of user-individuality and context-specificity how the support characteristics of exoskeletons in the form of different support characteristics can bring about a purposeful and needs-based application for users and can contribute valuably to design future workplaces.

Modeling and Analysis of Explanation for Secure Industrial Control Systems

Many self-adaptive systems benefit from human involvement and oversight, where a human operator can provide expertise not available to the system and detect problems that the system is unaware of. One way of achieving this synergy is by placing the human operator on the loop —i.e., providing supervisory oversight and intervening in the case of questionable adaptation decisions. To make such interaction effective, an explanation can play an important role in allowing the human operator to understand why the system is making certain decisions and improve the level of knowledge that the operator has about the system. This, in turn, may improve the operator’s capability to intervene and, if necessary, override the decisions being made by the system. However, explanations may incur costs, in terms of delay in actions and the possibility that a human may make a bad judgment. Hence, it is not always obvious whether an explanation will improve overall utility and, if so, then what kind of explanation should be provided to the operator. In this work, we define a formal framework for reasoning about explanations of adaptive system behaviors and the conditions under which they are warranted. Specifically, we characterize explanations in terms of explanation content , effect , and cost . We then present a dynamic system adaptation approach that leverages a probabilistic reasoning technique to determine when an explanation should be used to improve overall system utility. We evaluate our explanation framework in the context of a realistic industrial control system with adaptive behaviors.

A-101 Correspondence between ABAS-II and VABS-II Composite Scales in a Cohort of Adults with Down Syndrome

Abstract Objective: Informant ratings of adaptive functioning are a valuable source of information that can support diagnosis and treatment planning and monitor change. The Adaptive Behavior Assessment System (ABAS-II) and Vineland Adaptive Behavior Scales (VABS-II) are well-known adaptive functioning questionnaires used in clinical practice. This study assessed the correspondence between ABAS-II and VABS-II composite scales in adults with Down Syndrome (DS). Method: Participants were selected from a cohort of 110 adults with DS involved in a longitudinal study examining Alzheimer’s risk. Participants were selected if they had informant ratings on both the ABAS-II and VABS-II at the same measurement wave (n = 85; M = 41.9 years-old, SD = 10.8; 55.3% female; 96.5% White). Due to non-normality, non-parametric correlations examined convergent validity of the ABAS-II and VABS-II standard scores. Results: The composite scores of similar constructs from each measure correlated significantly (ps < 0.001) with large effect sizes: ABAS-II Conceptual with VABS-II Communication (rho = 0.53), ABAS-II Social with VABS-II Socialization (rho = 0.83), ABAS-II Practical with VABS-II Daily Living Skills (rho = 0.84), and ABAS-II Global Adaptive Composite (GAC) with VABS-II Adaptive Behavior Composite (ABC) (rho = 0.88). There was 85.2% correspondence between ABAS-II and VABS-II ratings of low adaptive functioning (SS < 70 on the GAC or ABC, respectively), with just 14.8% of participants (n = 12) categorized as low on VABS-II but not ABAS-II. Conclusion(s): The ABAS-II and VABS-II composites were strongly correlated, capturing similar adaptive domains. However, there was a greater chance of being rated as low functioning on the VABS-II compared to the ABAS-II, which may have diagnostic implications for clinical and research work in DS.

Language and Aggression in Preschool Children. Gender Differences

The present study has two objectives. Firstly to analyze the gender differences in language development in preschool children and secondly to analyze the relationships between language development on the one hand and aggression, prosocial behavior on the other hand. The study involved 155 preschool children aged between 36 and 71 months (M = 52.61, SD = 10.40) enrolled in a kindergarten in Bucharest. The instruments used were the Adaptive Behavior Assessment System, ABAS-II (Harrison et al., 2012) and Preschool Social Behavior Scale - Teacher Form (Crick, 1996). To determine the gender differences, a series of independent samples t tests and Mann-Whitney U nonparametric test were performed and for the relationships among variables, regression and moderation analyzes were performed. The results showed that girls have higher levels of language development than boys, in all measured dimensions. At the same time, the level of language development was negatively associated with physical and relational aggression and positively with prosocial behavior. Contrary to our expectations, gender has not moderated the relationship between language development and aggression or prosocial behavior. The practical implications of the study address the need to capture and treat early delays in language development in order to prevent aggressive behaviors among preschoolers.

Adaptive skills and mental health in children and adolescents with neuromuscular diseases.

Adaptive skills represent the ways that children and adolescents meet their basic needs for self-care, decision making, communication, and learning in their daily life. Having a neuromuscular disease (NMD) not only presents mental health issues, but also impacts these skills. Our study aimed to compare the adaptive skills and mental health of paediatric patients with the most common NMDs with their healthy peers and assess how NMDs shape the way patients form relationships with others, engage in leisure activities and take care of their daily living needs. We used the Adaptive Behaviour Assessment System (ABAS-3) and Achenbach Child Behaviour Checklist (CBCL) to compare the adaptive skills and mental health symptoms of 50 NMD patients to a demographically-matched control group of 298 peers. We examined specific outcomes of having myotonic dystrophy (DM), Duchenne muscular dystrophy (DMD), Becker muscular dystrophy (BMD), spinal muscular atrophy (SMA) or a mixed group of other NMDs. All NMD patients displayed poor practical adaptive skills. When the disease was more likely to involve the central nervous system (DM, DMD) they also showed additional deficits in their conceptual and social skills. Contrary to previous research no increased rate of psychopathological symptoms was found in NMD patients, with the exception of difficulties in the social domain among patients with DM. Although most children with NMDs displayed more limited practical skills, the specific profile of adaptive skills for each patient group needs to be taken into consideration when planning school support and other psychosocial interventions.

Managing complex adaptive systems: A resource/agent qualitative modelling perspective

Complex adaptive systems are systems where those managing the system, the agents, interact with other competing agents and key resources available to the system. The behaviour of the agents and the resources are constantly changing over time thus resulting in complex systems of evolving problem configurations. Managing such a system can be very challenging, particularly when attempting to manage rather than simplify complexity. One particular problem is the need to take a comprehensive perspective of the complex system in order to manage it effectively. Resource structure and agent behaviour are interdependent and both interconnected components need to be considered in order to support optimal decision making. Due to the lack of an appropriate technique in the literature to achieve a comprehensive qualitative appreciation of resource/agent complex adaptive system behaviour, this paper describes the development of a novel qualitative modelling tool, a Resource/Agent Map, that aims to map and analyse both resources and agents interactive behaviour. We show how this modelling tool can help achieve a holistic appreciation of the resource/agent perspectives and generate scenario alternatives to inform policy decision making in respect to system management and regulation. A pharmaceutical example is used to demonstrate the modelling tool.

An event-based approach for formally verifying runtime adaptive real-time systems

Real-time and embedded systems are required to adapt their behavior and structure to runtime unpredicted changes in order to maintain their feasibility and usefulness. These systems are generally more difficult to specify and verify owning to their execution complexity. Hence, ensuring the high-level design and the early verification of system adaptation at runtime is very crucial. However, existing runtime model-based approaches for adaptive real-time and embedded systems suffer from shortcoming linked to efficiently and correctly managing the adaptive system behavior, especially that a formal verification is not allowed by modeling languages such as UML and MARTE profile. Moreover, reasoning about the correctness and the precision of high-level models is a complex task without the appropriate tool support. In this work, we propose an MDE-based framework for the specification and the verification of runtime adaptive real-time and embedded systems. Our approach stands for Event-B method to formally verify resources behavior and real-time constraints. In fact, thanks to MDE M2T transformations, our proposal translates runtime models into Event-B specifications to ensure the correctness of runtime adaptive system properties, temporal constrains and nonfunctional properties using Rodin platform. A flood prediction system case study is adopted for the validation of our proposal.

Scen@rist: an approach for verifying self-adaptive systems using runtime scenarios

Traditional runtime quantitative verification approaches for self-adaptive systems usually rely on the use of state-transition models to describe the system behaviour and check property violation at runtime. More recently, some approaches have advocated the use of scenarios as a strategy for representing both the normal and adaptive system behaviour. However, the use of scenarios as a runtime entity that drives the system adaptation is still a challenge since many research issues regarding the use of scenarios to support analysis for enabling self-adjustment at runtime in software systems remain open. The aim of this paper is twofold. Firstly, we investigate the use of scenario-based approaches in self-adaptive systems via a systematic mapping study. Secondly, we introduce Scen@rist, an approach that uses scenarios as runtime entities for verifying self-adaptive systems. The approach consists of monitoring a running self-adaptive system, annotating its scenario-based behaviour specification with the probability of transitions between scenarios, and then verifying whether a set of reachability properties hold. This is performed by translating the scenario-based models and properties in their probabilistic state-based counterparts and applying a model checking technique. The applicability of the proposed tool has been demonstrated by two self-adaptive service-based systems taken from the literature.

A runtime model-based framework for specifying and verifying adaptive RTE systems

Adaptive Real-Time Embedded Systems (RTES) may execute in an unpredictable context that is impossible to definitely consider in the development time. Therefore, these systems are required to adapt their behaviour to unpredicted changes at runtime in order to maintain their feasibility and usefulness. Their design requires effective runtime modelling formalisms for monitoring, reconfiguration planning and adaptive system analysis. In this context, software designers need to evaluate, refine and validate runtime models at early stages of development via adaptation tools, in particular for runtime adaptive systems to avoid execution problems. In the present paper, we propose a runtime model-based framework that allows the modelling and simulation as well as the traceability of adaptive RTES. Our proposal starts by a high-level specification based on the UML/MARTE profile, which describes an adaptive system and supports the reasoning about its behaviour and structure at runtime. The runtime UML/MARTE models are translated into an adaptive one that instantiates MAPE patterns for the control and the traceability of the runtime system. Then Model-to-Text (M2T) transformations allow us to generate simulation scripts for the analysis of adaptive system behaviour at runtime and evaluate its real-time constraints.

A runtime model-based framework for specifying and verifying adaptive RTE systems

Adaptive Real-Time Embedded Systems (RTES) may execute in an unpredictable context that is impossible to definitely consider in the development time. Therefore, these systems are required to adapt their behaviour to unpredicted changes at runtime in order to maintain their feasibility and usefulness. Their design requires effective runtime modelling formalisms for monitoring, reconfiguration planning and adaptive system analysis. In this context, software designers need to evaluate, refine and validate runtime models at early stages of development via adaptation tools, in particular for runtime adaptive systems to avoid execution problems. In the present paper, we propose a runtime model-based framework that allows the modelling and simulation as well as the traceability of adaptive RTES. Our proposal starts by a high-level specification based on the UML/MARTE profile, which describes an adaptive system and supports the reasoning about its behaviour and structure at runtime. The runtime UML/MARTE models are translated into an adaptive one that instantiates MAPE patterns for the control and the traceability of the runtime system. Then Model-to-Text (M2T) transformations allow us to generate simulation scripts for the analysis of adaptive system behaviour at runtime and evaluate its real-time constraints.

Public Reform Paradoxes and the Old Effectiveness–Efficiency Problem

Although effectiveness and efficiency are old comrades of public administrations, they still often cause unintended consequences. The relation between (absent) effectiveness and (overly emphasised) efficiency remains unresolved. The paper shows that effectiveness and efficiency are still used interchangeably, and despite the presence of negative effects, it comes as a surprise that important documents still address these terms without procedure or methodology to provide the content whereby they could be more clearly elaborated. Not only is the goal to achieve clearer meaning, but to accomplish results with the fewest possible negative effects. Alongside different management reforms, decision-makers must not lose sight of the whole; all reforms are only specific answers to inadequate previous ones, and it could be valuable to take a step back to see how/why different reforms emerge. The paper addresses the success/failure of reforms and the outcomes thereof. It claims the core problem of rational decision-making lies not in rationality per se, but in a lack of concept and/or insufficient attention to the behaviour of complex adaptive systems. With the help of complex adaptive systems, cybernetics, and combinations of effectiveness and efficiency, the paper presents the essential elements for adaptive (human) decision-making (such as diversity, variation, selection, adaptation, and integration) as the framework whereby unintended, reverse, and neutral effects can be reduced. New rules/decisions should be based on different levels of planning and adaptation, and on moving from the general to the more specific, in accordance with context specificity and unplanned, emergent things. It seems the hardest thing to address is the human character that does not (want to) recognise a situation as the situation in which some things must be spotted, evaluated, and changed if needed.

A multi-physics approach for modeling hygroscopic behavior in wood low-tech architectural adaptive systems

Wood is a natural engineering material that has traditionally been exploited in design for a wide variety of applications. The recent demand for sustainable material and construction processes in the construction industry has triggered a renewed interest and research in the inherent properties of wood and their derived applications, and specifically for developing low-tech architectural adaptive systems. This paper focuses on the physical and computational modeling of the morphing behavior of wood through hygroscopic expansion or contraction to a high degree of precision. The amount of stress related to the hygroscopic shrinking or swelling ranges from almost zero to high values, and its prediction is fundamental to alleviate any fatigue challenges. The capability of designing wood composite whose stress state remains limited under changes of the environmental humidity is beneficial for any engineering application subjected to a repeated reversal of loading such as adaptive systems. In this paper, a mechanical model, together with its numerical implementation, is presented; the model is benchmarked against some prototypical experiments, performed by using real material parameters. The control parameter in the model is the relative moisture change in wood that determines the orthotropic swelling/de-swelling phenomenon, and is coupled with the elastic behavior of wood. This model is integrated into a programmable matter design approach that combines physical and computational exploration. The approach is illustrated for a hygro-morphic building façade panel. The approaches and algorithms presented in this paper have further applications for computer-aided design of smart materials and systems with interchanging functionalities.

Dealing with Complexity through Advanced Control Techniques

The control of complex dynamic systems, both in their behavior and in their mission, goes through the implementation of multi-loop control architectures based on information about the system internal state and from the environment, as well as on the mission plan state. This results in systems that are becoming increasingly autonomous, for which requirements in terms of safety and reliability, as well as expected performance, are increasingly high. Research works developed at ONERA in the field of control for autonomous systems cover all levels of the control architectures, which are basically structured with respect to temporal aspects, as well as the level of abstraction that they entail for the system dynamic. We will consider them in this paper by increasing level. We will discuss the advances achieved recently in the robust control techniques of uncertain dynamic systems generally implemented at the lower control level and we will discuss their extensions to consider input and output constraints, as well as the hybrid nature of most of the systems considered. To design task level control primitives that take place just above the previous control loops, we will introduce sensor-based robust and non-linear control techniques. These are based on information on the environment extracted from exteroceptive sensors, to adapt system behavior to uncertainties and perturbations. Multi-sensor and/or multi-objective controls will be discussed in this particular context. We will also present several recent results in the field of trajectory tracking based on visual navigation techniques in complex environments, which combine objectives and constraints within the same control architecture. We will discuss how model predictive control (MPC) techniques and advanced optimization techniques can be used for solving the resulting control problems. In addition, we will discuss several ongoing developments of these methods by exploiting distributed model predictive control techniques (DMPC) and predictive control of hybrid systems. Finally, integration with the control architectures at the upper level of reactive, predictive and distributed planning capabilities will be proposed to accommodate time constraints and uncertainties in decision.

Formulation of reliability-related objective functions for design of intelligent mechatronic systems

State-of-the-art mechatronic systems offer inherent intelligence that enables them to autonomously adapt their behavior to current environmental conditions and to their own system state. This autonomous behavior adaptation is made possible by software in combination with complex sensor and actuator systems and by sophisticated information processing, all of which make these systems increasingly complex. This increasing complexity makes the design process a challenging task and brings new complex possibilities for operation and maintenance. However, with the risk of increased system complexity also comes the chance to adapt system behavior based on current reliability, which in turn increases reliability. The development of such an adaption strategy requires appropriate methods to evaluate reliability based on currently selected system behavior. A common approach to implement such adaptivity is to base system behavior on different working points that are obtained using multiobjective optimization. During operation, selection among these allows a changed operating strategy. To allow for multiobjective optimization, an accurate system model including system reliability is required. This model is repeatedly evaluated by the optimization algorithm. At present, modeling of system reliability and synchronization of the models of behavior and reliability is a laborious manual task and thus very error-prone. Since system behavior is crucial for system reliability, an integrated model is introduced that integrates system behavior and system reliability. The proposed approach is used to formulate reliability-related objective functions for a clutch test rig that are used to compute feasible working points using multiobjective optimization.

To eliminate or absorb supply chain complexity: a conceptual model and case study

PurposeExisting works in the supply chain complexity area have either focused on the overall behavior of multi-firm complex adaptive systems or on listing specific tools and techniques that business units (BUs) can use to manage supply chain complexity but without providing a thorough discussion about when and why they should be deployed. This research aims to address this gap by developing a conceptually sound model, based on the literature, regarding how an individual BU should reduce versus absorb supply chain complexity.Design/methodology/approachThis research synthesizes the supply chain complexity and organizational design literature to present a conceptual model of how a BU should respond to supply chain complexity. The authors illustrate the model through a longitudinal case study analysis of a packaged foods manufacturer.FindingsRegardless of its type or origin, supply chain complexity can arise because of the strategic business requirements of the BU (strategic) or because of suboptimal business practices (dysfunctional complexity). Consistent with the proposed conceptual model, the illustrative case study showed that a firm must first distinguish between strategic and dysfunctional drivers prior to choosing an organizational response. Furthermore, it was found that efforts to address supply chain complexity can reveal other system weaknesses that lie dormant until the system is stressed.Research limitations/implicationsThe case study provides empirical support for the literature-derived conceptual model. Nevertheless, any findings derived from a single, in-depth case study require further research to produce generalizable results.Practical implicationsThe conceptual model presented here provides a more granular view of supply chain complexity and how an individual BU should respond, than what can be found in the existing literature. The model recognizes that an individual BU can simultaneously face both strategic and dysfunctional complexity drivers, each requiring a different organizational response.Originality/valueThere are no other research works that have synthesized the supply chain complexity and organizational design literature to present a conceptual model of how an individual BU should respond to supply chain complexity. As such, this paper improves the understanding of supply chain complexity effects and provides a basis for future research, as well as guidance for BUs facing complexity challenges.

Synthesis of searchless adaptive system based on lyapunov function using state observer

Searchless adaptive control system was studied and synthesized based on a Lyapunov function by a transient object under noisiness of measured values using state observer. The system adapts to the control object, regardless of whether the control object has static or dynamic characteristics. Additionally, the noise effect on the adaptive system behavior in the long run was investigated. The designed adaptive inference correction controller allows to adjust the control object inference and eliminates the static error of inference. State observer, which is based on the reference model restores the control object state vector. Even with significant object root deviations and random variables, adaptive system adapts the control action values and sets the control object inference to the desired value with the desired transition process time. The model was developed in the MATLAB/Simulink software package. Based on simulation, study of efficiency of the proposed model with different changes in the control object parameters was carried out.

High-level design space exploration for adaptive applications on multiprocessor systems-on-chip

This paper presents an abstract design and analysis framework for applications on multiprocessor systems-on-chip (MPSoCs). The aim is to allow for faster and cost-effective implementation decisions. This framework is intended as an intermediate reasoning support to deal with important design decisions in the early design stage. Our framework enables design space exploration in order to identify adequate parallelism levels for hardware/software mappings in presence of adaptive system behaviors. For this purpose, we use an activation clock-based encoding in which an application is represented according to its event occurrences with their precedence relations. Then, different mapping and scheduling scenarios of applications on MPSoC are analyzed via clock traces representing system simulations. Among properties of interest are functional behavioral correctness, temporal performance and energy consumption. A number of experiments are performed to compare with state-of-the-art techniques based on synchronous dataflow graphs. Experimental results show that our framework provides a similar level of precision and efficiency. In addition, our framework is more flexible to deal with both application and platform adaptive behaviors, while allowing customized communication and computation behavior simulations.

Advantages of reliability-adaptive system operation for maintenance planning

Intelligent mechatronic systems offer the possibility to adapt system behavior to current dependability. This can be used to assure reliability by controlling system behavior to reach a pre-defined lifetime. By using such closed loop control, the margin of error of useful lifetime of an individual system is lowered. It is also possible to change the pre-defined lifetime during operation, by adapting system behavior to derate component usage. When planning maintenance actions, the remaining useful lifetime of each individual system has to be taken into account. Usually, stochastic properties of a fleet of systems are analyzed to create maintenance plans. Among these, the main factor is the probability of an individual system to last until maintenance. If condition-based maintenance is used, this is updated for each individual system using available information about its current state. By lowering the margin of error of useful lifetime, which directly corresponds to the time until maintenance, extended maintenance periods are made possible. Also using reliability-adaptive operation, a reversal of degradation driven maintenance planning is possible where a maintenance plan is setup not only according to system properties, but mainly to requirements imposed by maintenance personnel or infrastructure. Each system then adapts its behavior accordingly and fails according to the maintenance plan, making better use of maintenance personnel and system capabilities at the same time. In this contribution, the potential of maintenance plan driven system behavior adaptation is shown. A model including adaptation process and maintenance actions is simulated over full system lifetime to assess the advantages gained.

Applying Systems Thinking in shaping South Africa's Electricity System

AbstractThe paper analyses the current situation in respect of South Africa's electricity situation and asks the question if Systems Thinking, more specifically as it pertains to the theory of Complex Adaptive Systems, would make a contribution to improved Electrical Energy decision making.The authors applied a specific branch of Complex Adaptive Systems (namely “edge of chaos” theory) by following an example based argumentative approach in their research. This led to the following observations: Information in respect of Electrical Energy and South Africa's Gross Domestic Product (GDP) as presented in the National Development Plan, Integrated Energy Plan and Integrated Resource Plan appears to be inconsistent. The sensitivity of Energy Intensity, the (lack of) accuracy with which it can be forecasted, and its alignment with expected growth sectors in the South African economy should be well understood and carefully monitored in shaping the future of the country's Electricity System.The paper concludes that the mental framework which Systems Thinking provides, and specifically the theory which explains the behavior of Complex Adaptive Systems, could make a positive contribution towards achieving quicker coherence in respect of South Africa's future Electricity System. Furthermore that such coherence will lead to quicker emergence of the single most important characteristic of the “RSA (Incorporated) system” ‐ its economic growth.