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for Symbolic Model Checking Alessandro Cimatti1, Edmund Clarke2, Enrico Giunchiglia3, Fausto Giunchiglia4, Marco Pistore1, Marco Roveri1, Roberto Sebastiani4, and Armando Tacchella3 1 ITC-IRST, Via Sommarive 18, 38050 Trento, Italy fcimatti,pistore,roverig@irst.itc.it 2 Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh (PA), USA emc@cs.cmu.edu 3(More)
This paper describes NUSMV, a new symbolic model checker developed as a joint project between Carnegie Mellon University (CMU) and Istituto per la Ricerca Scientifica e Tecnolgica (IRST). NUSMV is designed to be a well structured, open, flexible and documented platform for model checking. In order to make NUSMV applicable in technology transfer projects, it(More)
This paper describes a new symbolic model checker, called NuSMV, developed as part of a joint project between CMU and IRST. NuSMV is the result of the reengineering, reimplementation and, to a limited extent, extension of the CMU SMV model checker. The core of this paper consists of a detailed description of the NuSMV functionalities, architecture, and(More)
This report has been submitted forr publication outside of ITC and will probably be copyrighted if accepted for publication. It has been issued as a Technical Report forr early dissemination of its contents. In view of the transfert of copy right too the outside publisher, its distribution outside of ITC priorr to publication should be limited to peer(More)
Planning under partial observability is one of the most significant and challenging planning problems. It has been shown to be hard, both theoretically and experimentally. In this paper, we present a novel approach to the problem of planning under partial observability in non-deterministic domains. We propose an algorithm that searches through a (possibly(More)
We present a framework that supports the formal verification of early requirements specifications. The framework is based on Formal Tropos, a specification language that adopts primitive concepts for modeling early requirements (such as actor, goal, and strategic dependency), along with a rich temporal specification language. We show how existing formal(More)
We tackle the problem of planning in nondeterministic domains, by presenting a new approach to conformant planning. Conformant planning is the problem of nding a sequence of actions that is guaranteed to achieve the goal despite the nondeterminism of the domain. Our approach is based on the representation of the planning domain as a nite state automaton. We(More)
Rarely planning domains are fully observable. For this reason, the ability to deal with partial observability is one of the most important challenges in planning. In this paper, we tackle the problem of strong planning under partial observability in nondeterministic domains: find a conditional plan that will result in a successful state, regardless of(More)
Most real world environments are non-deterministic. Automatic plan formation in non-deterministic dommns is, however, still an open problem. In this paper we present a practical algorithm for the automatic generation of solutions to planning problems in nondeterministic domains. Our approach has the following main features. First, the planner generates(More)
The Model Based Planner (MBP) is a system for planning in non-deterministic domains. It can generate plans automatically to solve various planning problems, like conformant planning, planning under partial observability, and planning for temporally extended goals. Moreover, MBP can validate plans, and offers a variety of simulation functionalities for plans(More)