63 research outputs found
Strengthening Model Checking Techniques with Inductive Invariants
This paper describes optimized techniques to efficiently compute and reap benefits from inductive invariants within SAT-based model checking. We address sequential circuit verification, and we consider both equivalences and implications between pairs of nodes in the logic networks. First, we present a very efficient dynamic procedure, based on equivalence classes and incremental SAT, specifically oriented to reduce the set of checked invariants. Then, we show how to effectively integrate the computation of inductive invariants within state-of-the-art SAT-based model checking procedures. Experiments (on more than 600 designs) show the robustness of our approach on verification instances on which stand-alone techniques fai
Applying Formal Methods to Networking: Theory, Techniques and Applications
Despite its great importance, modern network infrastructure is remarkable for
the lack of rigor in its engineering. The Internet which began as a research
experiment was never designed to handle the users and applications it hosts
today. The lack of formalization of the Internet architecture meant limited
abstractions and modularity, especially for the control and management planes,
thus requiring for every new need a new protocol built from scratch. This led
to an unwieldy ossified Internet architecture resistant to any attempts at
formal verification, and an Internet culture where expediency and pragmatism
are favored over formal correctness. Fortunately, recent work in the space of
clean slate Internet design---especially, the software defined networking (SDN)
paradigm---offers the Internet community another chance to develop the right
kind of architecture and abstractions. This has also led to a great resurgence
in interest of applying formal methods to specification, verification, and
synthesis of networking protocols and applications. In this paper, we present a
self-contained tutorial of the formidable amount of work that has been done in
formal methods, and present a survey of its applications to networking.Comment: 30 pages, submitted to IEEE Communications Surveys and Tutorial
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Testing from a stochastic timed system with a fault model
In this paper we present a method for testing a system against a non-deterministic stochastic finite state machine. As usual, we assume that the functional behaviour of the system under test
(SUT) is deterministic but we allow the timing to be non-deterministic. We extend the state counting method of deriving tests, adapting it to the presence of temporal requirements represented by means of random variables. The notion of conformance is introduced using an implementation relation considering temporal aspects and the limitations imposed by a black-box framework. We propose an algorithm for generating a test suite that determines the conformance of a deterministic SUT with respect to a non-deterministic specification. We show how previous work on testing from stochastic systems can be encoded into the framework presented in this paper as an instantiation of our parameterized implementation relation. In this setting, we use a notion of conformance up to a given confidence level
Goal-oriented Behaviour for Intelligent Game Agents
This thesis concerns our innovation in game AI techniques, mainly game agents' modeling, planning and learning. The research topic involves the development of a game design software --- Gameme. Our work mainly focus on the development of the core AI module.
In this thesis, after discussing the system design of Gameme, we explain our contributions in two parts: off-line design and real-time processing. In off-line design, we present goal-oriented behaviour design and related modeling methodology for game agents. The goal-oriented design provides not only an intuitive behaviour design methodology for non-professional game designers but also efficient support for real-time behaviour control. In particular, the goal-oriented design can be used in modeling agents in different games.
The real-time processing component includes planning and learning mechanisms for game agents. These mechanisms are placed in a layered architecture. Basically, a procedural planning mechanism allows game agents to have the ability of fast reaction to their environment. Then, the creative transfer and adaptive learning mechanism trains game agents to learn from their experience and cooperate in teamwork. Furthermore, the unique emergent learning mechanism can allow game agents to have the ability to analyze different PCs' behaviour patterns and to find the suitable strategy to defeat PCs in real-time.
Most of the experiments in this thesis are performed in fighting scenarios. We connected the core AI module with a 3D graphics engine in order to have visual testing results. All test cases show that our goal-oriented behaviour design along with planning and learning mechanisms can provide fast, autonomous, collaborative and adaptive behaviour instructions for game agent in real-time game play
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