10,789 research outputs found
A Case Study on Formal Verification of Self-Adaptive Behaviors in a Decentralized System
Self-adaptation is a promising approach to manage the complexity of modern
software systems. A self-adaptive system is able to adapt autonomously to
internal dynamics and changing conditions in the environment to achieve
particular quality goals. Our particular interest is in decentralized
self-adaptive systems, in which central control of adaptation is not an option.
One important challenge in self-adaptive systems, in particular those with
decentralized control of adaptation, is to provide guarantees about the
intended runtime qualities. In this paper, we present a case study in which we
use model checking to verify behavioral properties of a decentralized
self-adaptive system. Concretely, we contribute with a formalized architecture
model of a decentralized traffic monitoring system and prove a number of
self-adaptation properties for flexibility and robustness. To model the main
processes in the system we use timed automata, and for the specification of the
required properties we use timed computation tree logic. We use the Uppaal tool
to specify the system and verify the flexibility and robustness properties.Comment: In Proceedings FOCLASA 2012, arXiv:1208.432
Intelligent agent for formal modelling of temporal multi-agent systems
Software systems are becoming complex and dynamic with the passage of time, and to provide better fault tolerance and resource management they need to have the ability of self-adaptation. Multi-agent systems paradigm is an active area of research for modeling real-time systems. In this research, we have proposed a new agent named SA-ARTIS-agent, which is designed to work in hard real-time temporal constraints with the ability of self-adaptation. This agent can be used for the formal modeling of any self-adaptive real-time multi-agent system. Our agent integrates the MAPE-K feedback loop with ARTIS agent for the provision of self-adaptation. For an unambiguous description, we formally specify our SA-ARTIS-agent using Time-Communicating Object-Z (TCOZ) language. The objective of this research is to provide an intelligent agent with self-adaptive abilities for the execution of tasks with temporal constraints. Previous works in this domain have used Z language which is not expressive to model the distributed communication process of agents. The novelty of our work is that we specified the non-terminating behavior of agents using active class concept of TCOZ and expressed the distributed communication among agents. For communication between active entities, channel communication mechanism of TCOZ is utilized. We demonstrate the effectiveness of the proposed agent using a real-time case study of traffic monitoring system
Modeling and Analyzing Adaptive User-Centric Systems in Real-Time Maude
Pervasive user-centric applications are systems which are meant to sense the
presence, mood, and intentions of users in order to optimize user comfort and
performance. Building such applications requires not only state-of-the art
techniques from artificial intelligence but also sound software engineering
methods for facilitating modular design, runtime adaptation and verification of
critical system requirements.
In this paper we focus on high-level design and analysis, and use the
algebraic rewriting language Real-Time Maude for specifying applications in a
real-time setting. We propose a generic component-based approach for modeling
pervasive user-centric systems and we show how to analyze and prove crucial
properties of the system architecture through model checking and simulation.
For proving time-dependent properties we use Metric Temporal Logic (MTL) and
present analysis algorithms for model checking two subclasses of MTL formulas:
time-bounded response and time-bounded safety MTL formulas. The underlying idea
is to extend the Real-Time Maude model with suitable clocks, to transform the
MTL formulas into LTL formulas over the extended specification, and then to use
the LTL model checker of Maude. It is shown that these analyses are sound and
complete for maximal time sampling. The approach is illustrated by a simple
adaptive advertising scenario in which an adaptive advertisement display can
react to actions of the users in front of the display.Comment: In Proceedings RTRTS 2010, arXiv:1009.398
Formal Modeling of Connectionism using Concurrency Theory, an Approach Based on Automata and Model Checking
This paper illustrates a framework for applying formal methods techniques, which are symbolic in nature, to specifying and verifying neural networks, which are sub-symbolic in nature. The paper describes a communicating automata [Bowman & Gomez, 2006] model of neural networks. We also implement the model using timed automata [Alur & Dill, 1994] and then undertake a verification of these models using the model checker Uppaal [Pettersson, 2000] in order to evaluate the performance of learning algorithms. This paper also presents discussion of a number of broad issues concerning cognitive neuroscience and the debate as to whether symbolic processing or connectionism is a suitable representation of cognitive systems. Additionally, the issue of integrating symbolic techniques, such as formal methods, with complex neural networks is discussed. We then argue that symbolic verifications may give theoretically well-founded ways to evaluate and justify neural learning systems in the field of both theoretical research and real world applications
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