Formal verification of complex systems: model-based and data-driven - PowerPoint PPT Presentation

Formal verification of complex systems: model-based and data-driven methods Alessandro Abate Department of Computer Science, University of Oxford Alan Turing Institute - Jan 12, 2018 Alessandro Abate, CS, Oxford Model-based and data-driven verification slide 1 /20

Automated formal verification: successes and frontiers automated, sound, formal Alessandro Abate, CS, Oxford Model-based and data-driven verification slide 2 /20

Automated formal verification: successes and frontiers automated, sound, formal industrial impact in verification of protocols, hardware circuits, and software Alessandro Abate, CS, Oxford Model-based and data-driven verification slide 2 /20

Automated formal verification: successes and frontiers automated, sound, formal industrial impact in verification of protocols, hardware circuits, and software asserts properties over given model of a system scalable and useful on “unsophisticated” models Alessandro Abate, CS, Oxford Model-based and data-driven verification slide 2 /20

Automated formal verification: pushing the envelope verification of physical systems (cyber-physical systems) dynamical models with uncertainty, noise (for CPS) bridging the gap between data and models principled integration of learning and verification Alessandro Abate, CS, Oxford Model-based and data-driven verification slide 3 /20

Building automation systems: an exemplar of CPS cyber-physical systems: integration of physical/analogue with cyber/digital building automation systems as a CPS exemplar Alessandro Abate, CS, Oxford Model-based and data-driven verification slide 4 /20

Building automation systems: an exemplar of CPS cyber-physical systems: integration of physical/analogue with cyber/digital building automation systems as a CPS exemplar smart energy initiatives at Oxford CS Alessandro Abate, CS, Oxford Model-based and data-driven verification slide 4 /20

Building automation systems - a CPS exemplar Building automation system setup in rooms 478/9 at Oxford CS advanced modelling for smart buildings application: certifiable energy management control of temperature, humidity, CO 2 1 model-based predictive maintenance of devices 2 fault-tolerant control 3 demand-response over smart grids 4 Alessandro Abate, CS, Oxford Model-based and data-driven verification slide 5 /20

Building automation systems - a CPS exemplar Building automation system setup in rooms 478/9 at Oxford CS advanced modelling for smart buildings application: certifiable energy management control of temperature, humidity, CO 2 1 model-based predictive maintenance of devices 2 fault-tolerant control 3 demand-response over smart grids 4 Alessandro Abate, CS, Oxford Model-based and data-driven verification slide 5 /20

Building automation systems - a CPS exemplar Building automation system setup in rooms 478/9 at Oxford CS advanced modelling for smart buildings application: certifiable energy management control of temperature, humidity, CO 2 1 model-based predictive maintenance of devices 2 fault-tolerant control 3 demand-response over smart grids 4 Alessandro Abate, CS, Oxford Model-based and data-driven verification slide 5 /20

Building automation systems - problem setup model CO 2 dynamics, under the effect of occupants: room full (F)/empty (E) 1 window: open (O)/closed (C) 2 air circulation: ON/OFF 3 x k + 1 = x k + ∆ � � − 1 ON mx k + µ { O , C } ( C out − x k ) + 1 F C occ V x - zone CO 2 level (F,C) (F,O) ∆ - sampling time V - zone volume m - air inflow (when ON) (E,C) (E,O) µ O - air exchange with outside (when O) µ C - air leakage with outside (when C) C out - outside CO 2 level C occ - CO 2 by occupants (when F) Alessandro Abate, CS, Oxford Model-based and data-driven verification slide 6 /20

Building automation systems - problem setup model CO 2 dynamics, under the effect of occupants: room full (F)/empty (E) 1 window: open (O)/closed (C) 2 air circulation: ON/OFF 3 x k + 1 = x k + ∆ � � − 1 ON mx k + µ { O , C } ( C out − x k ) + 1 F C occ V (F,C) (F,O) Parameter Value ∆ 15 min 288 m 3 V 0.25 m 3 /min m (E,C) (E,O) 0.1667 m 3 /min µ O 0.01 m 3 /min µ C 375 ppm C out C occ 0.4 ppm/min Alessandro Abate, CS, Oxford Model-based and data-driven verification slide 6 /20

Building automation systems - problem setup model CO 2 dynamics, under the effect of occupants: room empty E 1 window: closed C 2 air circulation: ON 3 x k + 1 = x k + ∆ V ( − mx k + µ C ( C out − x k )) + 0 · C occ CO 2 levels Fan (on, off) 600 1 (F,C) (F,O) 500 400 300 200 100 0 0 12 0 12 0 12 0 12 0 0 12 0 12 0 12 0 12 0 (E,C) (E,O) Occupancy (occupied, empty) Windows (open, closed) 1 1 0 0 0 12 0 12 0 12 0 12 0 0 12 0 12 0 12 0 12 0 Alessandro Abate, CS, Oxford Model-based and data-driven verification slide 6 /20

Building automation systems - problem setup model CO 2 dynamics, under the effect of occupants: room full F 1 window: closed C 2 air circulation: ON 3 x k + 1 = x k + ∆ V ( − mx k + µ C ( C out − x k )) + C occ CO 2 levels Fan (on, off) 600 1 (F,C) (F,O) 500 400 300 200 100 0 0 12 0 12 0 12 0 12 0 0 12 0 12 0 12 0 12 0 (E,C) (E,O) Occupancy (occupied, empty) Windows (open, closed) 1 1 0 0 0 12 0 12 0 12 0 12 0 0 12 0 12 0 12 0 12 0 Alessandro Abate, CS, Oxford Model-based and data-driven verification slide 6 /20

Building automation systems - problem setup model CO 2 dynamics, under the effect of occupants: room full F 1 window: open O 2 air circulation: ON 3 x k + 1 = x k + ∆ V ( − mx k + µ O ( C out − x k )) + C occ CO 2 levels Fan (on, off) 600 1 (F,C) (F,O) 500 400 300 200 100 0 0 12 0 12 0 12 0 12 0 0 12 0 12 0 12 0 12 0 (E,C) (E,O) Occupancy (occupied, empty) Windows (open, closed) 1 1 0 0 0 12 0 12 0 12 0 12 0 0 12 0 12 0 12 0 12 0 Alessandro Abate, CS, Oxford Model-based and data-driven verification slide 6 /20

Building automation systems - problem setup model CO 2 dynamics, under the effect of occupants: room empty E 1 window: closed C 2 air circulation: ON 3 x k + 1 = x k + ∆ V ( − mx k + µ O ( C out − x k )) CO 2 levels Fan (on, off) 600 1 (F,C) (F,O) 500 400 300 200 100 0 0 12 0 12 0 12 0 12 0 0 12 0 12 0 12 0 12 0 (E,C) (E,O) Occupancy (occupied, empty) Windows (open, closed) 1 1 0 0 0 12 0 12 0 12 0 12 0 0 12 0 12 0 12 0 12 0 Alessandro Abate, CS, Oxford Model-based and data-driven verification slide 6 /20

Building automation systems - problem setup model CO 2 dynamics, under the effect of occupants: room full (F)/empty (E) 1 window: open (O)/closed (C) 2 air circulation: ON 3 model with hybrid dynamics CO 2 levels Fan (on, off) 600 1 (F,C) (F,O) 500 400 300 200 100 0 0 12 0 12 0 12 0 12 0 0 12 0 12 0 12 0 12 0 (E,C) (E,O) Occupancy (occupied, empty) Windows (open, closed) 1 1 0 0 0 12 0 12 0 12 0 12 0 0 12 0 12 0 12 0 12 0 Alessandro Abate, CS, Oxford Model-based and data-driven verification slide 6 /20

Building automation systems - problem setup model CO 2 dynamics, under the effect of occupants: room full (F)/empty (E) 1 window: open (O)/closed (C) 2 air circulation: OFF 3 model with hybrid dynamics CO 2 levels Fan (on, off) 1,400 1 (F,C) (F,O) 1,200 1,000 800 600 0 0 12 0 12 0 12 0 12 0 0 12 0 12 0 12 0 12 0 (E,C) (E,O) Occupancy (occupied, empty) Windows (open, closed) 1 1 0 0 0 12 0 12 0 12 0 12 0 0 12 0 12 0 12 0 12 0 Alessandro Abate, CS, Oxford Model-based and data-driven verification slide 6 /20

Learning and verification: state of art and objective data noise noise inputs outputs system data-driven analysis Alessandro Abate, CS, Oxford Model-based and data-driven verification slide 7 /20

Learning and verification: state of art and objective data noise noise outputs inputs system model data-driven analysis model learning (with data), and model-based verification Alessandro Abate, CS, Oxford Model-based and data-driven verification slide 7 /20

Learning and verification: state of art and objective noise data noise inputs outputs system model disconnect between data-driven learning and model-based verification Alessandro Abate, CS, Oxford Model-based and data-driven verification slide 7 /20

Learning and verification: state of art and objective data noise noise outputs inputs system model disconnect between data-driven learning and model-based verification principled integration of learning and verification Alessandro Abate, CS, Oxford Model-based and data-driven verification slide 7 /20

Overview of method property φ model pMC data from system S D parameter Bayesian inference synthesis over parameters p ( θ | D ) Θ φ confidence C = P ( S | = φ ) computation Alessandro Abate, CS, Oxford Model-based and data-driven verification slide 8 /20

Parametric Markov chains property φ model pMC data from system S D parameter Bayesian inference synthesis over parameters p ( θ | D ) Θ φ confidence C = P ( S | = φ ) computation Alessandro Abate, CS, Oxford Model-based and data-driven verification slide 9 /20