Automating Verification of State Machines with Reactive Designs and Isabelle/UTP

State-machine based notations are ubiquitous in the description of component systems, particularly in the robotic domain. To ensure these systems are safe and predictable, formal verification techniques are important, and can be cost-effective if they are both automated and scalable. In this paper, we present a verification approach for a diagrammatic state machine language that utilises theorem proving and a denotational semantics based on Unifying Theories of Programming (UTP). We provide the necessary theory to underpin state machines (including induction theorems for iterative processes), mechanise an action language for states and transitions, and use these to formalise the semantics. We then describe the verification approach, which supports infinite state systems, and exemplify it with a fully automated deadlock-freedom check. The work has been mechanised in our proof tool, Isabelle/UTP, and so also illustrates the use of UTP to build practical verification tools.Comment: 18 pages, 16th Intl. Conf. on Formal Aspects of Component Software (FACS 2018), October 2018, Pohang, South Kore

Agent programming in the cognitive era

It is claimed that, in the nascent ‘Cognitive Era’, intelligent systems will be trained using machine learning techniques rather than programmed by software developers. A contrary point of view argues that machine learning has limitations, and, taken in isolation, cannot form the basis of autonomous systems capable of intelligent behaviour in complex environments. In this paper, we explore the contributions that agent-oriented programming can make to the development of future intelligent systems. We briefly review the state of the art in agent programming, focussing particularly on BDI-based agent programming languages, and discuss previous work on integrating AI techniques (including machine learning) in agent-oriented programming. We argue that the unique strengths of BDI agent languages provide an ideal framework for integrating the wide range of AI capabilities necessary for progress towards the next-generation of intelligent systems. We identify a range of possible approaches to integrating AI into a BDI agent architecture. Some of these approaches, e.g., ‘AI as a service’, exploit immediate synergies between rapidly maturing AI techniques and agent programming, while others, e.g., ‘AI embedded into agents’ raise more fundamental research questions, and we sketch a programme of research directed towards identifying the most appropriate ways of integrating AI capabilities into agent programs

Model Checking Real-Time Properties on the Functional Layer of Autonomous Robots

International audienceSoftware is an essential part of robotic systems. As robots and autonomous systems are more and more deployed in human environments, we need to use elaborate validation and verification techniques in order to gain a higher level of trust in our systems. This motivates our determination to apply formal verification methods to robotics software. In this paper, we describe our results obtained using model-checking on the functional layer of an autonomous robot. We implement an automatic translation from GenoM, a robotics model-based software engineering framework, to the formal specification language Fiacre. This translation takes into account the semantics of the robotics middleware. TINA, our model-checking toolbox, can be used on the synthesized models to prove real-time properties of the functional modules implementation on the robot. We illustrate our approach using a realistic autonomous navigation example