2,388 research outputs found
Adaptability Checking in Multi-Level Complex Systems
A hierarchical model for multi-level adaptive systems is built on two basic
levels: a lower behavioural level B accounting for the actual behaviour of the
system and an upper structural level S describing the adaptation dynamics of
the system. The behavioural level is modelled as a state machine and the
structural level as a higher-order system whose states have associated logical
formulas (constraints) over observables of the behavioural level. S is used to
capture the global and stable features of B, by a defining set of allowed
behaviours. The adaptation semantics is such that the upper S level imposes
constraints on the lower B level, which has to adapt whenever it no longer can
satisfy them. In this context, we introduce weak and strong adaptabil- ity,
i.e. the ability of a system to adapt for some evolution paths or for all
possible evolutions, respectively. We provide a relational characterisation for
these two notions and we show that adaptability checking, i.e. deciding if a
system is weak or strong adaptable, can be reduced to a CTL model checking
problem. We apply the model and the theoretical results to the case study of
motion control of autonomous transport vehicles.Comment: 57 page, 10 figures, research papaer, submitte
Bioaccumulation modelling and sensitivity analysis for discovering key players in contaminated food webs: the case study of PCBs in the Adriatic Sea
Modelling bioaccumulation processes at the food web level is the main step to analyse the effects of pollutants at the global
ecosystem level. A crucial question is understanding which species play a key role in the trophic transfer of contaminants to
disclose the contribution of feeding linkages and the importance of trophic dependencies in bioaccumulation dynamics. In this
work we present a computational framework to model the bioaccumulation of organic chemicals in aquatic food webs, and to
discover key species in polluted ecosystems. As a result, we reconstruct the first PCBs bioaccumulation model of the Adriatic food
web, estimated after an extensive review of published concentration data. We define a novel index aimed to identify the key species
in contaminated networks, Sensitivity Centrality, and based on sensitivity analysis. The index is computed from a dynamic ODE
model parametrised from the estimated PCBs bioaccumulation model and compared with a set of established trophic indices of
centrality. Results evidence the occurrence of PCBs biomagnification in the Adriatic food web, and highlight the dependence of
bioaccumulation on trophic dynamics and external factors like fishing activity. We demonstrate the effectiveness of the introduced
Sensitivity Centrality in identifying the set of species with the highest impact on the total contaminant flows and on the efficiency
of contaminant transport within the food web
Conformal Quantitative Predictive Monitoring of STL Requirements for Stochastic Processes
We consider the problem of predictive monitoring (PM), i.e., predicting at
runtime the satisfaction of a desired property from the current system's state.
Due to its relevance for runtime safety assurance and online control, PM
methods need to be efficient to enable timely interventions against predicted
violations, while providing correctness guarantees. We introduce
\textit{quantitative predictive monitoring (QPM)}, the first PM method to
support stochastic processes and rich specifications given in Signal Temporal
Logic (STL). Unlike most of the existing PM techniques that predict whether or
not some property is satisfied, QPM provides a quantitative measure of
satisfaction by predicting the quantitative (aka robust) STL semantics of
. QPM derives prediction intervals that are highly efficient to compute
and with probabilistic guarantees, in that the intervals cover with arbitrary
probability the STL robustness values relative to the stochastic evolution of
the system. To do so, we take a machine-learning approach and leverage recent
advances in conformal inference for quantile regression, thereby avoiding
expensive Monte-Carlo simulations at runtime to estimate the intervals. We also
show how our monitors can be combined in a compositional manner to handle
composite formulas, without retraining the predictors nor sacrificing the
guarantees. We demonstrate the effectiveness and scalability of QPM over a
benchmark of four discrete-time stochastic processes with varying degrees of
complexity
A Deep Reinforcement Learning Approach to Concurrent Bilateral Negotiation
We present a novel negotiation model that allows an agent to learn how to
negotiate during concurrent bilateral negotiations in unknown and dynamic
e-markets. The agent uses an actor-critic architecture with model-free
reinforcement learning to learn a strategy expressed as a deep neural network.
We pre-train the strategy by supervision from synthetic market data, thereby
decreasing the exploration time required for learning during negotiation. As a
result, we can build automated agents for concurrent negotiations that can
adapt to different e-market settings without the need to be pre-programmed. Our
experimental evaluation shows that our deep reinforcement learning-based agents
outperform two existing well-known negotiation strategies in one-to-many
concurrent bilateral negotiations for a range of e-market settings
An STL-based Formulation of Resilience in Cyber-Physical Systems
Resiliency is the ability to quickly recover from a violation and avoid
future violations for as long as possible. Such a property is of fundamental
importance for Cyber-Physical Systems (CPS), and yet, to date, there is no
widely agreed-upon formal treatment of CPS resiliency. We present an STL-based
framework for reasoning about resiliency in CPS in which resiliency has a
syntactic characterization in the form of an STL-based Resiliency Specification
(SRS). Given an arbitrary STL formula , time bounds and
, the SRS of , , is the STL formula
, specifying
that recovery from a violation of occur within time
(recoverability), and subsequently that be maintained for duration
(durability). These -expressions, which are atoms in our SRS logic,
can be combined using STL operators, allowing one to express composite
resiliency specifications, e.g., multiple SRSs must hold simultaneously, or the
system must eventually be resilient. We define a quantitative semantics for
SRSs in the form of a Resilience Satisfaction Value (ReSV) function and
prove its soundness and completeness w.r.t. STL's Boolean semantics. The
-value for atoms is a singleton set containing a
pair quantifying recoverability and durability. The -value for a composite
SRS formula results in a set of non-dominated recoverability-durability pairs,
given that the ReSVs of subformulas might not be directly comparable (e.g., one
subformula has superior durability but worse recoverability than another). To
the best of our knowledge, this is the first multi-dimensional quantitative
semantics for an STL-based logic. Two case studies demonstrate the practical
utility of our approach.Comment: 16 pages excluding references and appendix (23 pages in total), 6
figure
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