Fostering resilient execution of multi-agent plans through self-organisation

Traditional multi-agent planning addresses the coordination of multiple agents towards common goals, by producing an integrated plan of actions for each of those agents. For systems made of large numbers of cooperating agents, however, the execution and monitoring of a plan should enhance its high-level steps, possibly involving entire sub-teams, with a flexible and adaptable lower-level behaviour of the individual agents. In order to achieve such a goal, we need to integrate the behaviour dictated by a multi-agent plan with self-organizing, swarm-based approaches, capable of automatically adapting their behaviour based on the contingent situation, departing from the predetermined plan whenever needed. Moreover, in order to deal with multiple domains and unpredictable situations, the system should, as far as possible, exhibit such capabilities without hard-coding the agents behaviour and interactions. In this paper, we investigate the relationship between multi-agent planning and self-organisation through the combination of two representative approaches both enjoying declarativity. We consider a functional approach to self-organising systems development, called Aggregate Programming (AP), and propose to exploit collective adaptive behaviour to carry out plan revisions. We describe preliminary results in this direction on a case study of execution monitoring and repair of a Multi-Agent PDDL plan

Case studies for a new IoT programming paradigm: Fluidware

A number of scientific and technological advancements enabled turning the Internet of Things vision into reality. However, there is still a bottleneck in designing and developing IoT applications and services: each device has to be programmed individually, and services are deployed to specific devices. The Fluidware approach advocates that to truly scale and raise the level of abstraction a novel perspective is needed, focussing on device ensembles and dynamic allocation of resources. In this paper, we motivate the need for such a paradigm shift through three case studies emphasising a mismatch between state of art solutions and desired properties to achieve

Self-stabilising target counting in wireless sensor networks using Euler integration

Target counting is an established challenge for sensor networks: given a set of sensors that can count (but not identify) targets, how many targets are there? The problem is complicated because of the need to disambiguate duplicate observations of the same target by different sensors. A number of approaches have been proposed in the literature, and in this paper we take an existing technique based on Euler integration and develop a fully-distributed, self-stabilising solution. We derive our algorithm within the field calculus from the centralised presentation of the underlying integration technique, and analyse the precision of the counting through simulation of several network configurations.Postprin

Aggregate Drone Monitoring of Wildfires

Wildfires are an ever-increasing problem, exacerbated by the current global warming trends. Accordingly, it is becoming more and more relevant to monitor factors influencing their outbreaks and spreading to preemptively act on the riskiest areas and guide interventions in case an outbreak occurs. Different approaches have been proposed during the decades tackling this issue, which however require large datasets that are difficult and expensive to gather. In this paper, we propose to address the management of wildfires by empowering existing centralised models with a decentralised component. Leveraging dedicated monitoring drones together with smartphones held by experts and intervention corps, a decentralised system could both enhance data collection and assist interventions. As conditions near wildfires require strong fault-tolerance guarantees, we propose to develop such an application through aggregate programming, a novel approach to the resilient programming of decentralised systems

Engineering Resilient Collective Adaptive Systems by Self-Stabilisation

Collective adaptive systems are an emerging class of networked computational systems, particularly suited in application domains such as smart cities, complex sensor networks, and the Internet of Things. These systems tend to feature large scale, heterogeneity of communication model (including opportunistic peer-to-peer wireless interaction), and require inherent self-adaptiveness properties to address unforeseen changes in operating conditions. In this context, it is extremely difficult (if not seemingly intractable) to engineer reusable pieces of distributed behaviour so as to make them provably correct and smoothly composable. Building on the field calculus, a computational model (and associated toolchain) capturing the notion of aggregate network-level computation, we address this problem with an engineering methodology coupling formal theory and computer simulation. On the one hand, functional properties are addressed by identifying the largest-to-date field calculus fragment generating self-stabilising behaviour, guaranteed to eventually attain a correct and stable final state despite any transient perturbation in state or topology, and including highly reusable building blocks for information spreading, aggregation, and time evolution. On the other hand, dynamical properties are addressed by simulation, empirically evaluating the different performances that can be obtained by switching between implementations of building blocks with provably equivalent functional properties. Overall, our methodology sheds light on how to identify core building blocks of collective behaviour, and how to select implementations that improve system performance while leaving overall system function and resiliency properties unchanged.Comment: To appear on ACM Transactions on Modeling and Computer Simulatio

Resilient distributed collection through information speed thresholds

Part 6: Large-Scale Decentralised SystemsInternational audienceOne of the key coordination problems in physically-deployed distributed systems, such as mobile robots, wireless sensor networks, and IoT systems in general, is to provide notions of “distributed sensing” achieved by the strict, continuous cooperation and interaction among individual devices. An archetypal operation of distributed sensing is data summarisation over a region of space, by which several higher-level problems can be addressed: counting items, measuring space, averaging environmental values, and so on. A typical coordination strategy to perform data summarisation in a peer-to-peer scenario, where devices can communicate only with a neighbourhood, is to progressively accumulate information towards one or more collector devices, though this typically exhibits problems of reactivity and fragility, especially in scenarios featuring high mobility. In this paper, we propose coordination strategies for data summarisation involving both idempotent and arithmetic aggregation operators, with the idea of controlling the minimum information propagation speed, so as to improve the reactivity to input changes. Given suitable assumptions on the network model, and under the restriction of no data loss, these algorithms achieve optimal reactivity. By empirical evaluation via simulation, accounting for various sources of volatility, and comparing to other existing implementations of data summarisation algorithms, we show that our algorithms are able to retain adequate accuracy even in high-variability scenarios where all other algorithms are significantly diverging from correct estimations

Efficient compensation handling via subjective updates

Programming abstractions for compensation handling and dynamic update are crucial in specifying reliable interacting systems, such as Collective Adaptive Systems (CAS). Compensations and updates both specify how a system reacts in response to exceptional events. Prior work showed that different semantics for compensation handling can be encoded into a calculus of adaptable processes with objective updates, in which a process is reconfigured by its context. This paper goes further by considering subjective updates, in which, intuitively, a process reconfigures itself. A calculus of adaptable processes with subjective update its introduced, and its expressivity is assessed by encoding two semantics for compensation handling. The resulting encodings are more efficient than those using objective updates: they require less computational steps