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This book constitutes the refereed proceedings of the 6th International Symposium on NASA Formal Methods, NFM 2014, held in Houston, TX, USA, April 29 – May 1, 2014. The 20 revised regular papers presented together with 9 short papers were carefully reviewed and selected from 107 submissions. The topics include model checking, theorem proving, static analysis, model-based development, runtime monitoring, formal approaches to fault tolerance, applications of formal methods to aerospace systems, formal analysis of cyber-physical systems, including hybrid and embedded systems, formal methods in systems engineering, modeling, requirements and specifications, requirements generation, specification debugging, formal validation of specifications, use of formal methods in safety cases, use of formal methods in human-machine interaction analysis, formal methods for parallel hardware implementations, use of formal methods in automated software engineering and testing, correct-by-design, design for verification, and property based design techniques, techniques and algorithms for scaling formal methods, e.g., abstraction and symbolic methods, compositional techniques, parallel and distributed techniques, and application of formal methods to emerging technologies.
This book constitutes the refereed proceedings of the 13th International Conference on Formal Engineering Methods, ICFEM 2011, held in Durham, UK, October 2011. The 40 revised full papers together with 3 invited talks presented were carefully reviewed and selected from 103 submissions. The papers address all current issues in formal methods and their applications in software engineering. They are organized in topical sections on formal models; model checking and probability; specification and development; security; formal verification; cyber physical systems; event-B; verification, analysis and testing; refinement; as well as theorem proving and rewriting.
We present a framework to design and verify the behavior of stochastic systems whose parameters are not known with certainty but are instead affected by modeling uncertainties, due for example to modeling errors, non-modeled dynamics or inaccuracies in the probability estimation. Our framework can be applied to the analysis of intrinsically randomized systems (e.g., random back off schemes in wireless protocols) and of abstractions of deterministic systems whose dynamics are interpreted stochastically to simplify their representation (e.g., the forecast of wind availability). In the first part of the dissertation, we introduce the model of Convex Markov Decision Processes (Convex-MDPs) as the modeling framework to represent the behavior of stochastic systems. Convex-MDPs generalize MDPs by expressing state-transition probabilities not only with fixed realization frequencies but also with non-linear convex sets of probability distribution functions. These convex sets represent the uncertainty in the modeling process. In the second part of the dissertation, we address the problem of formally verifying properties of the execution behavior of Convex-MDPs. In particular, we aim to verify that the system behaves correctly under all valid operating conditions and under all possible resolutions of the uncertainty in the state-transition probabilities. We use Probabilistic Computation Tree Logic (PCTL) as the formal logic to express system properties. Using results on strong duality for convex programs, we present a model-checking algorithm for PCTL properties of Convex-MDPs, and prove that it runs in time polynomial in the size of the model under analysis. The developed algorithm is the first known polynomial-time algorithm for the verification of PCTL properties of Convex-MDPs. This result allows us to lower the previously known algorithmic complexity upper bound for Interval-MDPs from co-NP to P, and it is valid also for the more expressive (convex) uncertainty models supported by the Convex-MDP formalism. We apply the proposed framework and model-checking algorithm to the problem of formally verifying quantitative properties of models of the behavior of human drivers. We first propose a novel stochastic model of the driver behavior based on Convex Markov chains. The model is capable of capturing the intrinsic uncertainty in estimating the intricacies of the human behavior starting from experimentally collected data. We then formally verify properties of the model expressed in PCTL. Results show that our approach can correctly predict quantitative information about the driver behavior depending on his/her attention state, e.g., whether the driver is attentive or distracted while driving, and on the environmental conditions, e.g., the presence of an obstacle on the road. Finally, in the third part of the dissertation, we analyze the problem of synthesizing optimal control strategies for Convex-MDPs, aiming to optimize a given system performance, while guaranteeing that the system behavior fulfills a specification expressed in PCTL under all resolutions of the uncertainty in the state-transition probabilities. In particular, we focus on Markov strategies, i.e., strategies that depend only on the instantaneous execution state and not on the full execution history. We first prove that adding uncertainty in the representation of the state-transition probabilities does not increase the theoretical complexity of the synthesis problem, which remains in the class NP-complete as the analogous problem applied to MDPs, i.e., when all transition probabilities are known with certainty. We then interpret the strategy-synthesis problem as a constrained optimization problem and propose the first sound and complete algorithm to solve it. We apply the developed strategy-synthesis algorithm to the problem of generating optimal energy pricing and purchasing strategies for a for-profit energy aggregator whose portfolio of energy supplies includes renewable sources, e.g., wind. Economic incentives have been proposed to manage user demand and compensate for the intrinsic uncertainty in the prediction of the supply generation. Stochastic control techniques are however needed to maximize the economic profit for the energy aggregator while quantitatively guaranteeing quality-of-service for the users. We use Convex-MDPs to model the decision-making scenario and train the models with measured data, to quantitatively capture the uncertainty in the prediction of renewable energy generation. An experimental comparison shows that the control strategies synthesized using the proposed technique significantly increase system performance with respect to previous approaches presented in the literature.
This book constitutes the refereed proceedings of the 15th International Conference on Integrated Formal Methods, IFM 2019, held in Bergen, Norway, in December 2019. The 25 full papers and 3 short papers were carefully reviewed and selected from 95 submissions. The papers cover a broad spectrum of topics: from language design to verification and analysis techniques, to supporting tools and their integration into software engineering practice including both theoretical approaches and practical implementations. Also included are the extended abstracts of 6 "journal-first" papers.
This book constitutes the refereed proceedings of the 9th International Conference on Integrated Formal Methods, IFM 2012, held Pisa, Italy, in June 2012. The 20 revised full papers presented together with 2 invited papers were carefully reviewed and selected from 59 submissions. The papers cover the spectrum of integrated formal methods, ranging from formal and semiformal notations, semantics, proof frameworks, refinement, verification, timed systems, as well as tools and case studies.
This book constitutes the refereed proceedings of the 14th International Conference on Integrated Formal Methods, IFM 2018, held in Maynooth, Ireland, in September 2018. The 17 full papers and 5 short papers presented together with 3 invited talks were carefully reviewed and selected from 60 submissions. The conference covers a broad spectrum of topics: from language design, to verification and analysis techniques, to supporting tools and their integration into software engineering practice.
Applied Data Analysis and Modeling for Energy Engineers and Scientists fills an identified gap in engineering and science education and practice for both students and practitioners. It demonstrates how to apply concepts and methods learned in disparate courses such as mathematical modeling, probability,statistics, experimental design, regression, model building, optimization, risk analysis and decision-making to actual engineering processes and systems. The text provides a formal structure that offers a basic, broad and unified perspective,while imparting the knowledge, skills and confidence to work in data analysis and modeling. This volume uses numerous solved examples, published case studies from the author’s own research, and well-conceived problems in order to enhance comprehension levels among readers and their understanding of the “processes”along with the tools.
This book presents 8 tutorial survey papers by leading researchers who lectured at the 5th International School on Formal Methods for the Design of Computer, Communication, and Software Systems, SFM 2005, held in Bertinoro, Italy in April 2005. SFM 2005 was devoted to formal methods and tools for the design of mobile systems and mobile communication infrastructures. The 8 lectures are organized into topical sections on models and languages, scalability and performance, dynamic power management, and middleware support.