Proceedings of the Workshop on FORmal methods for the quantitative Evaluation of Collective Adaptive SysTems, FORECAST@STAF 2016, Vienna, Austria, 8 July 2016

Collective Adaptive Systems (CAS) consist of a large number of spatially distributed heterogeneous entities with decentralised control and varying degrees of complex autonomous behaviour that may be competing for shared resources even when collaborating to reach common goals. It is important to carry out thorough quantitative modelling and analysis and verification of their design to investigate all aspects of their behaviour before they are put into operation. This requires combinations of formal methods and applied mathematics which moreover scale to large-scale CAS. The primary goal of FORECAST is to raise awareness in the software engineering and formal methods communities of the particularities of CAS and the design and control problems which they bring.

[1]  G J Barker,et al.  Diffusion tensor imaging detects corticospinal tract involvement at multiple levels in amyotrophic lateral sclerosis , 2003, Journal of neurology, neurosurgery, and psychiatry.

[2]  Kim G. Larsen,et al.  Statistical Model Checking: Past, Present, and Future , 2016, ISoLA.

[3]  Jean-Yves Le Boudec,et al.  A Generic Mean Field Convergence Result for Systems of Interacting Objects , 2007, Fourth International Conference on the Quantitative Evaluation of Systems (QEST 2007).

[4]  Vincenzo Ciancia,et al.  Specifying and Verifying Properties of Space , 2014, IFIP TCS.

[5]  Vincenzo Ciancia,et al.  An Experimental Spatio-Temporal Model Checker , 2015, SEFM Workshops.

[6]  Luca Bortolussi,et al.  Model checking single agent behaviours by fluid approximation , 2015, Inf. Comput..

[7]  Vincenzo Ciancia,et al.  Spatio-temporal model checking of vehicular movement in public transport systems , 2018, International Journal on Software Tools for Technology Transfer.

[8]  Luca Bortolussi,et al.  Specifying and Monitoring Properties of Stochastic Spatio-Temporal Systems in Signal Temporal Logic , 2014, VALUETOOLS.

[9]  David Gilbert,et al.  A Novel Method to Verify Multilevel Computational Models of Biological Systems Using Multiscale Spatio-Temporal Meta Model Checking , 2016, PloS one.

[10]  Diego Latella,et al.  On-the-fly Fluid Model Checking via Discrete Time Population Models , 2015, EPEW.

[11]  Vincenzo Ciancia,et al.  Qualitative and Quantitative Monitoring of Spatio-Temporal Properties , 2015, RV.

[12]  Mieke Massink,et al.  The SCEL Language: Design, Implementation, Verification , 2015, The ASCENS Approach.

[13]  Calin Belta,et al.  SpaTeL: a novel spatial-temporal logic and its applications to networked systems , 2015, HSCC.

[14]  T. Jaspan,et al.  Metrics and Textural Features of MRI Diffusion to Improve Classification of Pediatric Posterior Fossa Tumors , 2014, American Journal of Neuroradiology.

[15]  Cliff B. Jones,et al.  Thinking Tools for the Future of Computing Science , 2001, Informatics.

[16]  Vincenzo Ciancia,et al.  Spatial Logic and Spatial Model Checking for Closure Spaces , 2016, SFM.

[17]  Boudewijn R. Haverkort,et al.  A logic for model-checking mean-field models , 2013, 2013 43rd Annual IEEE/IFIP International Conference on Dependable Systems and Networks (DSN).

[18]  Luca Bortolussi,et al.  Fluid Model Checking , 2012, CONCUR.

[19]  Vincenzo Ciancia,et al.  Model Checking Spatial Logics for Closure Spaces , 2016, Log. Methods Comput. Sci..

[20]  Robert K. Brayton,et al.  Model-checking continuous-time Markov chains , 2000, TOCL.

[21]  Vincenzo Ciancia,et al.  A Tool-Chain for Statistical Spatio-Temporal Model Checking of Bike Sharing Systems , 2016, ISoLA.

[22]  Antony Galton,et al.  A generalized topological view of motion in discrete space , 2003, Theor. Comput. Sci..

[23]  D. Willshaw,et al.  Cerebral Cortex doi:10.1093/cercor/bhr221 Cerebral Cortex Advance Access published September 21, 2011 Similarity-Based Extraction of Individual Networks from Gray Matter MRI Scans , 2022 .

[24]  Bud Mishra,et al.  Histological Image Processing Features Induce a Quantitative Characterization of Chronic Tumor Hypoxia , 2016, PloS one.

[25]  Calvin R. Maurer,et al.  A Linear Time Algorithm for Computing Exact Euclidean Distance Transforms of Binary Images in Arbitrary Dimensions , 2003, IEEE Trans. Pattern Anal. Mach. Intell..

[26]  Jane Hillston,et al.  Modelling and Analysis of Collective Adaptive Systems with CARMA and its Tools , 2016, SFM.

[27]  Christel Baier,et al.  Model-Checking Algorithms for Continuous-Time Markov Chains , 2002, IEEE Trans. Software Eng..

[28]  Vincenzo Ciancia,et al.  Data Verification for Collective Adaptive Systems: Spatial Model-Checking of Vehicle Location Data , 2014, 2014 IEEE Eighth International Conference on Self-Adaptive and Self-Organizing Systems Workshops.

[29]  Bengt Jonsson,et al.  A logic for reasoning about time and reliability , 1990, Formal Aspects of Computing.

[30]  Neeraj Sharma,et al.  Segmentation and classification of medical images using texture-primitive features: Application of BAM-type artificial neural network , 2008, Journal of medical physics.

[31]  Vincenzo Ciancia,et al.  On-the-Fly Mean-Field Model-Checking for Attribute-Based Coordination , 2016, COORDINATION.

[32]  G. N. Srinivasan,et al.  Statistical Texture Analysis , 2008 .

[33]  Ezio Bartocci,et al.  Learning and detecting emergent behavior in networks of cardiac myocytes , 2008, CACM.

[34]  Diego Latella,et al.  On-the-fly Fast Mean-Field Model-Checking , 2013, TGC.