Closed-loop Analysis of Vision-based Autonomous Systems : A Case Study

Deep neural networks (DNNs) are increasingly used in safety-critical autonomous systems as perception components processing high-dimensional image data. Formal analysis of these systems is particularly challenging due to the complexity of the perception DNNs, the sensors (cameras), and the environment conditions. We present a case study applying formal probabilistic analysis techniques to an experimental autonomous system that guides airplanes on taxiways using a perception DNN. We address the above challenges by replacing the camera and the network with a compact probabilistic abstraction built from the confusion matrices computed for the DNN on a representative image data set. We also show how to leverage local, DNN-specific analyses as run-time guards to increase the safety of the overall system. Our findings are applicable to other autonomous systems that use complex DNNs for perception

Supporting Semi-Automatic Co-Evolution of Architecture and Fault Tree Models

In the whole life-cycle of systems in safety-critical domains, system models must consistently co-evolve with quality evaluation models like fault trees. However, performing these co-evolution steps is a cumbersome and often manual task. To understand this problem in detail, we have analyzed the evolution and mined common changes of architecture and fault tree models for a set of evolution scenarios of a part of a factory automation system called pick&place unit. Based on the results, we could derive a set of co-evolution rules which fully cover the evolution scenarios of the case study and which offer the potential to semi-automate the co-evolution process. In particular, we evaluated these rules by a comparison to typical visual editor operations. Our results show a signicant reduction of the amount of required user interactions in order to realize the co-evolution

The semantics of the interaction between agents and web services on the semantic web

IEEE;IEEE Computer Society36th Annual IEEE International Computer Software and Applications Conference Workshops, COMPSACW 2012 -- 16 July 2012 through 20 July 2012 -- Izmir -- 94271Development of agent systems is naturally a complex task due to the fundamental characteristics of agents. In addition, agent internals and inter-agent behavior models inside Multi-agent Systems (MAS) may become even more difficult to implement when interactions of agents with web services on the Semantic Web are taken into account. Our approach consists of the utilization of a Domain-specific Modeling Language (DSML) during MAS development in order to cope with the abovementioned challenge. This paper describes how the formal semantics of this DSML can be defined by especially focusing on its viewpoint on agentsemantic service interactions and discusses the use of this semantics definition on MAS validation. Determined semantic rules are both defined and implemented by using Alloy specification language which has a strong description capability based on both relational and first-order logic. © 2012 IEEE

The GMF-based syntax tool of a DSML for the semantic web enabled multi-agent systems

ACM SIGPLANACM International Conference on Systems, Programming, Languages, and Applications: Software for Humanity, SPLASH'11 and the Co-Located Workshops: DSM'11, TMC'11, AGERE'11, AOOPES'11, NEAT'11, and VMIL'11 -- 23 October 2011 through 24 October 2011 -- Portland, OR -- 88278Internal complexity of agents makes development of agent-based software systems complicated. On the other hand, MAS implementation becomes even more complex when new requirements and interactions for environments such as the Semantic Web is considered. A Domain Specific Modelling Language (DSML) can provide the required abstraction and hence support a more fruitful methodology for the development of MAS especially working on the new challenging environments like the Semantic Web. In this paper, a graphical syntax tool is introduced in which agent developers can model MASs according to a DSML called Semantic web Enabled Agent Modeling Language, SEA-ML. © 2011 ACM

Development of an agent based E-barter system

TUBITAK;IEEE2011 International Symposium on INnovations in Intelligent SysTems and Applications, INISTA 2011 -- 15 June 2011 through 18 June 2011 -- Istanbul-Kadikoy -- 85879A barter system is an alternative commerce approach where customers meet at a marketplace in order to exchange their goods or services without currency. In order to cope with challenges of electronic barter systems (e.g. efficient management of trades and determination of best-matching goods and their suppliers), several software systems based on intelligent agents have been proposed. However only a very few of these proposals consider exact development and implementation of multi-agent e-barter systems. Besides, most of the approaches consider matching between exchanged goods in which only price and quantity information are used. Hence, in this paper, we discuss design and implementation of a multi-agent e-barter system which utilizes ontology-based comparison for bid matching. Formal representation and decision-making criteria for agent mediated barter process are given and behavioral model of collaborating agents within the system is described. In addition to the traditional e-barter members, a new type of software agent is introduced in order to infer about semantic closeness between offered and purchased items. Related approach may enhance capabilities of e-barter systems in the way of finding the most appropriate matches between supplies and demands, considering not only price and quantities for goods. © 2011 IEEE

Selected challenges of software evolution for automated production systems

Automated machines and plants are operated for some decades and undergo an everlasting evolution during this time. In this paper, we present three related open evolution challenges focusing on software evolution in the domain of automated production systems, i.e. evolution and co-evolution of (interdisciplinary) engineering models and code, quality assurance as well as variant and version management during evolution