Agent Based Approaches to Engineering Autonomous Space Software

Current approaches to the engineering of space software such as satellite control systems are based around the development of feedback controllers using packages such as MatLab's Simulink toolbox. These provide powerful tools for engineering real time systems that adapt to changes in the environment but are limited when the controller itself needs to be adapted. We are investigating ways in which ideas from temporal logics and agent programming can be integrated with the use of such control systems to provide a more powerful layer of autonomous decision making. This paper will discuss our initial approaches to the engineering of such systems.Comment: 3 pages, 1 Figure, Formal Methods in Aerospac

Towards the Verification of Pervasive Systems

Pervasive systems, that is roughly speaking systems that can interact with their environment, are increasingly common. In such systems, there are many dimensions to assess: security and reliability, safety and liveness, real-time response, etc. So far modelling and formalizing attempts have been very piecemeal approaches. This paper describes our analysis of a pervasive case study (MATCH, a homecare application) and our proposal for formal (particularly verification) approaches. Our goal is to see to what extent current state of the art formal methods are capable of coping with the verification demand introduced by pervasive systems, and to point out their limitations

Combining SLiVER with CADP to Analyze Multi-agent Systems

International audienceWe present an automated workflow for the analysis of multi-agent systems described in a simple specification language. The procedure is based on a structural encoding of the input system and the property of interest into an LNT program, and relies on the CADP software toolbox to either verify the given property or simulate the encoded system. Counterexamples to properties under verification, as well as simulation traces, are translated into a syntax similar to that of the input language: therefore, no knowledge of CADP is required. The workflow is implemented as a module of the verification tool SLiVER. We present the input specification language, describe the analysis workflow, and show how to invoke SLiVER to verify or simulate two example systems. Then, we provide details on the LNT encoding and the verification procedure

Verifying Multi-Agent Systems by Model Checking Three-valued Abstractions

ABSTRACT We develop the theoretical foundations of a predicate abstraction methodology for the verification of multi-agent systems. We put forward a specification language based on epistemic logic and a weak variant of the logic ATL interpreted on a three-valued semantics. We show that the model checking problem for multi-agent systems in this setting is tractable by giving a provably correct procedure which admits a PTime bound. We give a constructive technique for generating abstract approximations of concrete multiagent systems models and show that the truth values are preserved between abstract and concrete models. We evaluate the effectiveness of the methodology on a variant of the bit-transmission problem

Safety assurance of an industrial robotic control system using hardware/software co-verification

As a general trend in industrial robotics, an increasing number of safety functions are being developed or re-engineered to be handled in software rather than by physical hardware such as safety relays or interlock circuits. This trend reinforces the importance of supplementing traditional, input-based testing and quality procedures which are widely used in industry today, with formal verification and model-checking methods. To this end, this paper focuses on a representative safety-critical system in an ABB industrial paint robot, namely the High-Voltage electrostatic Control system (HVC). The practical convergence of the high-voltage produced by the HVC, essential for safe operation, is formally verified using a novel and general co-verification framework where hardware and software models are related via platform mappings. This approach enables the pragmatic combination of highly diverse and specialised tools. The paper's main contribution includes details on how hardware abstraction and verification results can be transferred between tools in order to verify system-level safety properties. It is noteworthy that the HVC application considered in this paper has a rather generic form of a feedback controller. Hence, the co-verification framework and experiences reported here are also highly relevant for any cyber-physical system tracking a setpoint reference

A neural-symbolic system for temporal reasoning with application to model verification and learning

The effective integration of knowledge representation, reasoning and learning into a robust computational model is one of the key challenges in Computer Science and Artificial Intelligence. In particular, temporal models have been fundamental in describing the behaviour of Computational and Neural-Symbolic Systems. Furthermore, knowledge acquisition of correct descriptions of the desired system’s behaviour is a complex task in several domains. Several efforts have been directed towards the development of tools that are capable of learning, describing and evolving software models. This thesis contributes to two major areas of Computer Science, namely Artificial Intelligence (AI) and Software Engineering. Under an AI perspective, we present a novel neural-symbolic computational model capable of representing and learning temporal knowledge in recurrent networks. The model works in integrated fashion. It enables the effective representation of temporal knowledge, the adaptation of temporal models to a set of desirable system properties and effective learning from examples, which in turn can lead to symbolic temporal knowledge extraction from the corresponding trained neural networks. The model is sound, from a theoretical standpoint, but is also tested in a number of case studies. An extension to the framework is shown to tackle aspects of verification and adaptation under the SE perspective. As regards verification, we make use of established techniques for model checking, which allow the verification of properties described as temporal models and return counter-examples whenever the properties are not satisfied. Our neural-symbolic framework is then extended to deal with different sources of information. This includes the translation of model descriptions into the neural structure, the evolution of such descriptions by the application of learning of counter examples, and also the learning of new models from simple observation of their behaviour. In summary, we believe the thesis describes a principled methodology for temporal knowledge representation, learning and extraction, shedding new light on predictive temporal models, not only from a theoretical standpoint, but also with respect to a potentially large number of applications in AI, Neural Computation and Software Engineering, where temporal knowledge plays a fundamental role

Proceedings of The Multi-Agent Logics, Languages, and Organisations Federated Workshops (MALLOW 2010)

http://ceur-ws.org/Vol-627/allproceedings.pdfInternational audienceMALLOW-2010 is a third edition of a series initiated in 2007 in Durham, and pursued in 2009 in Turin. The objective, as initially stated, is to "provide a venue where: the cost of participation was minimum; participants were able to attend various workshops, so fostering collaboration and cross-fertilization; there was a friendly atmosphere and plenty of time for networking, by maximizing the time participants spent together"