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Extended Temporal Logic Revisited

  • Orna Kupferman
  • Nir Piterman
  • Moshe Y. Vardi
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2154)

Abstract

A key issue in the design of a model-checking tool is the choice of the formal language with which properties are specified. It is now recognized that a good language should extend linear temporal logic with the ability to specify all ω-regular properties. Also, designers, who are familiar with finite-state machines, prefer extensions based on automata than these based on fixed points or propositional quantification. Early extensions of linear temporal logic with automata use nondeterministic Büchi automata. Their drawback has been inability to refer to the past and the asymmetrical structure of nondeterministic automata. In this work we study an extension of linear temporal logic, called ETL2a, that uses two-way alternating automata as temporal connectives. Twoway automata can traverse the input word back and forth and they are exponentially more succinct than one-way automata. Alternating automata combine existential and universal branching and they are exponentially more succinct than nondeterministic automata. The rich structure of two-way alternating automata makes ETL2a a very powerful and convenient logic. We show that ETL2a formulas can be translated to nondeterministic Büchi automata with an exponential blow up. It follows that the satisfiability and model-checking problems for ETL2a are PSPACE-complete, as are the ones for LTL and its earlier extensions with automata. So, in spite of the succinctness of two-way and alternating automata, the advantages of ETL2a are obtained without a major increase in space complexity. The recent acceptance of alternating automata by the industry and the development of symbolic procedures for handling them make us optimistic about the practicality of ETL2a.

Keywords

Model Check Temporal Logic Linear Temporal Logic Atomic Proposition Acceptance Condition 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2001

Authors and Affiliations

  • Orna Kupferman
    • 1
  • Nir Piterman
    • 2
  • Moshe Y. Vardi
    • 3
  1. 1.School of Engineering and Computer ScienceHebrew UniversityJerusalemIsrael
  2. 2.Department of Computer ScienceWeizmann Institute of ScienceRehovotIsrael
  3. 3.Department of Computer ScienceRice UniversityHoustonUSA

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