ATOM: an object-based formal method for real-time systems

An object based formal method for the development of real-time systems, called ATOM, is presented. The method is an integration of the real-time formal technique TAM (Temporal Agent Model) with an industry-strength structured methodology known as HRT-HOOD. ATOM is a systematic formal approach based on the refinement calculus. Within ATOM, a formal specification (or abstract description statement) contains Interval Temporal Logic (ITL) description of the timing, functional, and communication behavior of the proposed real-time system. This formal specification can be analyzed and then refined into concrete statements through successive applications of sound refinement laws. Both abstract and concrete statements are allowed to freely intermix. The semantics of the concrete statements in ATOM are defined denotationally in specification-oriented style using ITL.Funding received from the UK Engineering and Physical Sciences Research Council (EPSRC) through the Research Grant GR/M/0258

Designing a provably correct robot control system using a "lean" formal method

A development method for the construction of provably correct robot control systems together with its supporting tool environment are described. The method consists of four stages: 1. specification, 2. refinement, 3. simulation and 4. code. The method is centered around the notion of wide-spectrum formalism within which an abstract Interval Temporal Logic (ITL) representation is intermixed freely with the concrete Temporal Agent Model (TAM) representation of the system under consideration. The method with its associated tool support is applied to the design of a robot control system.Funded by EPSRC Research Grant GR/K25922: A compositional approach to the specification of systems using ITL and Tempura

Synthesizing an Agent-Based Heterogeneous Population Model for Epidemic Surveillance

In this paper we propose a probabilistic approach to synthesize an agent-based heterogeneous population interaction model to study the spatio-temporal dynamics of an air-born epidemic, such as influenza, in a metropolitan area. The methodology is generic in nature and can generate a baseline population for cities for which detailed population summary tables are not available. The joint probabilities of population demographics are estimated using the International Public Use Microsimulation Data (IPUMS) sample data set. Agents, are assigned various activities based on several characteristics. The agent-based model for the city of Lahore, Pakistan is synthesized and a rule based disease spread model of influenza is simulated. The simulation results are visualized to analyze the spatio-temporal dynamics of the epidemic. The results show that the proposed model can be used by officials and medical experts to simulate an outbreak

A Framework for Synthesizing Agent-Based Heterogeneous Population Model for Epidemic Simulation

Social interactions play an important role in spread of a disease. In this thesis we propose a probabilistic approach to synthesize an agent-based heterogeneous population interaction model to study the spatio-temporal dynamics of an air-born epidemic, such as influenza, in a metropolitan area. The proposed methodology is generic in nature and can generate a baseline population for the cities for which detailed population summary tables are not available. The joint probabilities of population demographics are estimated using the International Public Use Microsimulation Data (IPUMS) sample data set. Based on the population density and the socio-economic status, the population is divided into three types of residential areas. Agents, representing individuals, are assigned various activities based on their education, age, and gender. Since transportation can also influence the spread of a disease, this activity, with a finite time span, is also assigned to individuals. The proposed approach is used for the city of Lahore, Pakistan. The agent-based model for Lahore is synthesized and a rule based disease spread model of influenza is simulated for the city population. The simulation results are visualized to analyze the spatio-temporal dynamics of an influenza epidemic for Lahore

Using PVS for Interval Temporal Logic proofs, part 1: The syntactic and semantic encoding

Interval temporal logic (ITL) is a logic that is used to specify and reason about systems. The logic has a powerful proof system but rather than doing proofs by hand, which is tedious and error prone, we want a tool that can check each proof step. Instead of developing a new tool we will use the existing prototype verification system (PVS) as a basic tool. The specification language of PVS is used to encode interval temporal logic semantically and syntactically. With this we can encode the ITL proof system within PVS. Several examples of proofs in ITL that are done per hand are checked with PVS.Funded by EPSRC Research Grant GR/K2592

A sequential real-time refinement calculus

We present a comprehensive refinement calculus for the development of sequential, real-time programs from real-time specifications. A specification may include not only execution time limits, but also requirements on the behaviour of outputs over the duration of the execution of the program. The approach allows refinement steps that separate timing constraints and functional requirements. New rules are provided for handling timing constraints, but the refinement of components implementing functional requirements is essentially the same as in the standard refinement calculus. The product of the refinement process is a program in the target programming language extended with timing deadline directives. The extended language is a machine-independent, real-time programming language. To provide valid machine code for a particular model of machine, the machine code produced by a compiler must be analysed to guarantee that it meets the specified timing deadlines

Statistical abstraction for multi-scale spatio-temporal systems

Spatio-temporal systems exhibiting multi-scale behaviour are common in applications ranging from cyber-physical systems to systems biology, yet they present formidable challenges for computational modelling and analysis. Here we consider a prototypic scenario where spatially distributed agents decide their movement based on external inputs and a fast-equilibrating internal computation. We propose a generally applicable strategy based on statistically abstracting the internal system using Gaussian Processes, a powerful class of non-parametric regression techniques from Bayesian Machine Learning. We show on a running example of bacterial chemotaxis that this approach leads to accurate and much faster simulations in a variety of scenarios.Comment: 14th International Conference on Quantitative Evaluation of SysTems (QEST 2017