5,634 research outputs found
The Planning Spectrum - One, Two, Three, Infinity
Linear Temporal Logic (LTL) is widely used for defining conditions on the
execution paths of dynamic systems. In the case of dynamic systems that allow
for nondeterministic evolutions, one has to specify, along with an LTL formula
f, which are the paths that are required to satisfy the formula. Two extreme
cases are the universal interpretation A.f, which requires that the formula be
satisfied for all execution paths, and the existential interpretation E.f,
which requires that the formula be satisfied for some execution path.
When LTL is applied to the definition of goals in planning problems on
nondeterministic domains, these two extreme cases are too restrictive. It is
often impossible to develop plans that achieve the goal in all the
nondeterministic evolutions of a system, and it is too weak to require that the
goal is satisfied by some execution.
In this paper we explore alternative interpretations of an LTL formula that
are between these extreme cases. We define a new language that permits an
arbitrary combination of the A and E quantifiers, thus allowing, for instance,
to require that each finite execution can be extended to an execution
satisfying an LTL formula (AE.f), or that there is some finite execution whose
extensions all satisfy an LTL formula (EA.f). We show that only eight of these
combinations of path quantifiers are relevant, corresponding to an alternation
of the quantifiers of length one (A and E), two (AE and EA), three (AEA and
EAE), and infinity ((AE)* and (EA)*). We also present a planning algorithm for
the new language that is based on an automata-theoretic approach, and study its
complexity
Nonlinear Models of Neural and Genetic Network Dynamics:\ud \ud Natural Transformations of Łukasiewicz Logic LM-Algebras in a Łukasiewicz-Topos as Representations of Neural Network Development and Neoplastic Transformations \ud
A categorical and Łukasiewicz-Topos framework for Algebraic Logic models of nonlinear dynamics in complex functional systems such as Neural Networks, Cell Genome and Interactome Networks is introduced. Łukasiewicz Algebraic Logic models of both neural and genetic networks and signaling pathways in cells are formulated in terms of nonlinear dynamic systems with n-state components that allow for the generalization of previous logical models of both genetic activities and neural networks. An algebraic formulation of variable next-state/transfer functions is extended to a Łukasiewicz Topos with an N-valued Łukasiewicz Algebraic Logic subobject classifier description that represents non-random and nonlinear network activities as well as their transformations in developmental processes and carcinogenesis.\u
Finiteness results for subgroups of finite extensions
We discuss in the context of finite extensions two classical theorems of
Takahasi and Howson on subgroups of free groups. We provide bounds for the rank
of the intersection of subgroups within classes of groups such as virtually
free groups, virtually nilpotent groups or fundamental groups of finite graphs
of groups with virtually polycyclic vertex groups and finite edge groups. As an
application of our generalization of Takahasi's Theorem, we provide an uniform
bound for the rank of the periodic subgroup of any endomorphism of the
fundamental group of a given finite graph of groups with finitely generated
virtually nilpotent vertex groups and finite edge groups.Comment: 20 pages; no figures. Keywords: finite extensions, Howson's Theorem,
Hanna Neumann Conjecture, Takahasi's Theorem, periodic subgroup
Automatic Filters for the Detection of Coherent Structure in Spatiotemporal Systems
Most current methods for identifying coherent structures in
spatially-extended systems rely on prior information about the form which those
structures take. Here we present two new approaches to automatically filter the
changing configurations of spatial dynamical systems and extract coherent
structures. One, local sensitivity filtering, is a modification of the local
Lyapunov exponent approach suitable to cellular automata and other discrete
spatial systems. The other, local statistical complexity filtering, calculates
the amount of information needed for optimal prediction of the system's
behavior in the vicinity of a given point. By examining the changing
spatiotemporal distributions of these quantities, we can find the coherent
structures in a variety of pattern-forming cellular automata, without needing
to guess or postulate the form of that structure. We apply both filters to
elementary and cyclical cellular automata (ECA and CCA) and find that they
readily identify particles, domains and other more complicated structures. We
compare the results from ECA with earlier ones based upon the theory of formal
languages, and the results from CCA with a more traditional approach based on
an order parameter and free energy. While sensitivity and statistical
complexity are equally adept at uncovering structure, they are based on
different system properties (dynamical and probabilistic, respectively), and
provide complementary information.Comment: 16 pages, 21 figures. Figures considerably compressed to fit arxiv
requirements; write first author for higher-resolution version
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