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An inner product space on irreducible and synchronizable probabilistic finite state automata

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Abstract

Probabilistic finite state automata (PFSA) have found their applications in diverse systems. This paper presents the construction of an inner-product space structure on a class of PFSA over the real field via an algebraic approach. The vector space is constructed in a stationary setting, which eliminates the need for an initial state in the specification of PFSA. This algebraic model formulation avoids any reference to the related notion of probability measures induced by a PFSA. A formal language-theoretic and symbolic modeling approach is adopted. Specifically, semantic models are constructed in the symbolic domain in an algebraic setting. Applicability of the theoretical formulation has been demonstrated on experimental data for robot motion recognition in a laboratory environment.

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Abbreviations

\({\mathcal{A}}\) :

is the set of all irreducible FSA [Definition 2.4];

\({\bar{\mathcal{A}}}\) :

is the set of all FSA [Definition 2.2];

\({\mathcal{A}_s}\) :

is the set of all irreducible and synchronizable FSA [Definition 3.3];

\({\mathcal{B}^+}\) :

is the set of all PFSA such that the probability map \({\tilde{\pi}}\) has only strictly positive entries and the underlying FSA is irreducible [Definition 4.1];

\({\mathcal{B}^+_s}\) :

is the subset of synchronizable PFSA in \({\mathcal{B}^+}\) [Definition 4.1];

\({\mathcal{C}^+}\) :

is the quotient set \({\mathcal{B}^+/\equiv}\) of all PFSA in \({\mathcal{B}^+}\) [Definition 4.7];

\({\mathcal{C}^+_s}\) :

is the quotient set \({\mathcal{B}^+_s/\equiv}\) of all PFSA in \({\mathcal{B}^+_s}\) [Definition 4.7];

\({\lfloor G \rfloor_K}\) :

is the lift of an FSA G relatively to a PFSA K [Definition 4.8];

\({\bar{K}}\) :

is the underlying FSA of the PFSA K [Definition 2.3];

\({\widehat{K}}\) :

is the minimal representation of the underlying FSA of the PFSA K [Theorem 4.5];

\({\lceil K \rceil}\) :

is the equivalence class of \({K \in \mathcal{B}^+}\) under the relation \({\equiv}\) [Definition 4.7];

\({\wp}\) :

is the stationary-probability distribution of the states [Sect. 5.3];

Q :

is the set of states of the FSA or PFSA [Definition 2.2];

T :

is the relabeling map between two FSA [Definition 3.1];

δ :

is the state transition map for FSA [Definition 2.2];

δ*:

is the extended state transition map of FSA [Sect. 2];

μ :

is the merging map for FSA [Definition 3.2];

π :

is the state-to-state transition probability map of PFSA [Sect. 5.3];

\({\tilde{\pi}}\) :

is the probability map of a PFSA [Definition 2.3];

\({\tilde{\pi}*}\) :

is the extended probability map of a PFSA [Sect. 2];

Σ:

is a finite alphabet of cardinality |Σ| [Sect. 2];

Σ*:

is the collection of all finite-length words made from Σ [Sect. 2];

\({\varpi}\) :

weight for the inner product \({\langle \cdot, \cdot \rangle}\) [Subsect. 5.3];

\({\mathfrak{S}}\) :

is the state relabeling equivalence for FSA [Definition 3.1];

\({\equiv}\) :

is the algebraic equivalence over PFSA [Definition 4.6];

\({\vartriangleleft}\) :

is the state splitting operation for FSA [Definition 3.2];

\({\vartriangleright}\) :

is the state merging operation for FSA [Definition 3.2];

\({\asymp}\) :

is the state relabeling operation for PFSA [Definition 4.2];

\({\preceq}\) :

is the state merging operation for PFSA [Definition 4.2];

\({\vee}\) :

is the join of two FSA [Definition 2.1] [Theorem B.1];

\({\curlyvee}\) :

is the join composition of two PFSA [Definition 2.1] [Definition 4.9];

\({\oplus}\) :

is the vector addition operation on the space \({\mathcal{B}^+_s}\) [Definition 5.2];

\({\odot}\) :

is the scalar multiplication operation on the space \({\mathcal{B}^+_s}\) [Definition 5.7];

 + :

is the vector addition operation on the space \({\mathcal{C}^+_s}\) [Definition 5.4];

\({\cdot}\) :

is the scalar multiplication operation on the space \({\mathcal{C}^+_s}\) [Definition 5.9];

\({\langle\langle \bullet, \bullet \rangle\rangle}\) :

is an inner product on the space \({\mathcal{B}^+_s}\) [Definition 5.12];

\({\langle \bullet, \bullet \rangle}\) :

is an inner product on the space \({\mathcal{C}^+_s}\) [Definition 5.14].

References

  1. Egerstedt MB, Frazzoli E, Pappas G (2006) Special section on symbolic methods for complex control systems. IEEE Trans Autom Control 51(6): 921–923

    Article  Google Scholar 

  2. Gupta S, Ray A (2009) Statistical mechanics of complex systems for pattern identification. J Stat Phys 134(2): 337–364

    Article  MathSciNet  MATH  Google Scholar 

  3. Ray A (2004) Symbolic dynamic analysis of complex systems for anomaly detection. Signal Processing 84(7): 1115–1130

    Article  MATH  Google Scholar 

  4. Gupta S, Ray A, Keller E (2007) Symbolic time series analysis of ultrasonic data for early detection of fatigue damage. Mech Syst Signal Process 21(2): 866–884

    Article  Google Scholar 

  5. Phoha S, LaPorta T, Griffin C (2006) Sensor network operations. IEEE Press, Piscataway

    Book  Google Scholar 

  6. Rajagopalan V, Ray A (2006) Symbolic time series analysis via wavelet-based partitioning. Signal Process 86(11): 3309–3320

    Article  MATH  Google Scholar 

  7. Vidal E, Thollard F, de la Higuera C, Casacuberta F, Carrasco RC (2005) Probabilistic finite-state machines—Part I. IEEE Trans Pattern Anal Mach Intell 27(7): 1013–1025

    Article  Google Scholar 

  8. Vidyasagar M (2011) The complete realization problem for hidden Markov models: a survey and some new results. Math Control Signals Syst 23: 1–65

    Article  Google Scholar 

  9. Rabiner L (1989) A tutorial on hidden Markov models and selected applications in speech proccessing. Proc IEEE 77(2): 257–286

    Article  Google Scholar 

  10. Carrasco R, Oncina J (1994) Learning stochastic regular grammars by means of a state merging method. In: Proceedings of second international colloquium on grammatical inference and applications, Alicante, Spain, pp 139–152

  11. Saul L, Pereira F (1997) Aggregate and mixed-order Markov models for statistical language processing. In: Proceedings of the second conference on empirical methods in natural language processing, Brown University, Providence, pp 81–89

  12. Ron D, Singer Y, Tishby N (1998) On the learnability and usage of acyclic probabilistic finite automata. J Comput Syst Sci 56(2): 133–152

    Article  MathSciNet  MATH  Google Scholar 

  13. Ray A (2005) Signed real measure of regular languages for discrete event supervisory control. Int J Control 78(12): 949–967

    Article  MATH  Google Scholar 

  14. Barfoot T, D’Eleuterio G (2002) An algebra for the control of stochastic systems: Exercises in linear algebra. In: Proceedings of the 5th international conference on dynamics and control of structures in space (DCSS), Cambridge, UK, pp 14–18

  15. Chattopadhyay I, Ray A (2008) Structural transformations of probabilistic finite state machines. Int J Control 81(5): 820–835

    Article  MathSciNet  MATH  Google Scholar 

  16. Wen Y, Ray A, Chattopadhyay I, Phoha S (2011) Modeling of symbolic systems: Part I-vector space representation of probabilistic finite state automata. In: Proceedings of American control conference, San Francisco, CA, USA, pp 5133–5138

  17. Wen Y, Ray A, Chattopadhyay I, Phoha S (2011) Modeling of SYMBOLIC SYSTems: Part II-Hilbert space construction for model identification and order reduction. In: Proceedings of American control conference, San Francisco, CA, USA, pp 5139–5144

  18. Rozenberg, G, Salomaa, A (eds) (1987) Handbook of formal languages, vol 2. Springer, Berlin

    Google Scholar 

  19. Lind D, Marcus M (1995) An introduction to symbolic dynamics and coding. Cambridge University Press, Cambridge

    Book  MATH  Google Scholar 

  20. Hopcroft JE, Motwani R, Ullman JD (2001) Introduction to automata theory, languages, and computation. 2nd edn. Addison-Wesley, Boston

    MATH  Google Scholar 

  21. Gill A (1976) Applied algebra for the computer sciences. Prentice-Hall, Englewood Cliffs

    MATH  Google Scholar 

  22. Shalizi C, Shalizi K (2004) Blind construction of optimal nonlinear recursive predictors for discrete sequences. In: AUAI ’04: proceedings of the 20th conference on uncertainty in artificial intelligence, Arlington, VA, USA. AUAI Press, Corvallis, pp 504–511

  23. Chattopadhyay I, Wen Y, Ray A, Phoha S (2011) Unsupervised inductive learning in symbolic sequences via recursive identification of self-similar semantics. In: Proceedings of American control conference, San Francisco, CA, USA, pp 125–130 (preprint)

  24. Youra H, Inoue T, Masuzawa T, Fujiwara H (1998) On the synthesis of synchronizable finite state machines with partial scan. Syst Comput Japan 29: 53–62

    Article  Google Scholar 

  25. Eilenberg S (1974) Automata, languages and machines. Academic Press, New York

    MATH  Google Scholar 

  26. Hartmanis J, Stearns RE (1966) Algebraic structure theory of sequential machines. Prentice Hall, Englewood Cliffs

    MATH  Google Scholar 

  27. Rudin W (1987) Real and complex analysis, 3rd edn. McGraw-Hill, New York

    MATH  Google Scholar 

  28. Bapat RB, Raghavan TES (1997) Nonnegative matrices and applications, Chap. 1. Cambridge University Press, Cambridge

    Book  Google Scholar 

  29. Berman A, Plemmons RJ (1994) Nonnegative matrices in the mathematical sciences, chap 1. SIAM, Philadelphia

    Book  Google Scholar 

  30. Cover TM, Thomas JA (2006) Elements of information theory. 2nd edn. Wiley-Interscience, Hoboken

    MATH  Google Scholar 

  31. Mallapragada G, Ray A, Jin X (2012) Symbolic dynamic filtering and language measure for behavior identification of mobile robots. IEEE Trans Syst Man Cybern Part B Cybern. doi:10.1109/TSMCB.2011.2172419

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Correspondence to Asok Ray.

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This work has been supported in part by the US Office of Naval Research under Grant No. N00014-09-1-0688, and by the US Army Research Laboratory and the US Army Research Office under Grant No. W911NF-07-1-0376. Any opinions, findings and conclusions or recommendations in this publication are those of the authors and do not reflect the views of the sponsoring agencies.

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Adenis, P., Wen, Y. & Ray, A. An inner product space on irreducible and synchronizable probabilistic finite state automata. Math. Control Signals Syst. 23, 281–310 (2012). https://doi.org/10.1007/s00498-012-0075-1

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