45,166 research outputs found
Collective Phenomena and Non-Finite State Computation in a Human Social System
We investigate the computational structure of a paradigmatic example of
distributed social interaction: that of the open-source Wikipedia community. We
examine the statistical properties of its cooperative behavior, and perform
model selection to determine whether this aspect of the system can be described
by a finite-state process, or whether reference to an effectively unbounded
resource allows for a more parsimonious description. We find strong evidence,
in a majority of the most-edited pages, in favor of a collective-state model,
where the probability of a "revert" action declines as the square root of the
number of non-revert actions seen since the last revert. We provide evidence
that the emergence of this social counter is driven by collective interaction
effects, rather than properties of individual users.Comment: 23 pages, 4 figures, 3 tables; to appear in PLoS ON
Learning probability distributions generated by finite-state machines
We review methods for inference of probability distributions generated by probabilistic automata and related models for sequence generation. We focus on methods that can be proved to learn in the inference
in the limit and PAC formal models. The methods we review are state merging and state splitting methods for probabilistic deterministic automata and the recently developed spectral method for nondeterministic probabilistic automata. In both cases, we derive them from a high-level algorithm described in terms of the Hankel matrix of the distribution to be learned, given as an oracle, and then describe how to adapt that algorithm to account for the error introduced by a finite sample.Peer ReviewedPostprint (author's final draft
Mutation testing from probabilistic finite state machines
Mutation testing traditionally involves mutating a program in order to produce a set of mutants and using these mutants in order to either estimate the effectiveness of a test suite or to drive test generation. Recently, however, this approach has been applied to specifications such as those written as finite state machines. This paper extends mutation testing to finite state machine models in which transitions have associated probabilities. The paper describes several ways of mutating a probabilistic finite state machine (PFSM) and shows how test sequences that distinguish between a PFSM and its mutants can be generated. Testing then involves applying each test sequence multiple times, observing the resultant output sequences and using results from statistical sampling theory in order to compare the observed frequency of each output sequence with that expected
Quantitative Safety: Linking Proof-Based Verification with Model Checking for Probabilistic Systems
This paper presents a novel approach for augmenting proof-based verification
with performance-style analysis of the kind employed in state-of-the-art model
checking tools for probabilistic systems. Quantitative safety properties
usually specified as probabilistic system invariants and modeled in proof-based
environments are evaluated using bounded model checking techniques.
Our specific contributions include the statement of a theorem that is central
to model checking safety properties of proof-based systems, the establishment
of a procedure; and its full implementation in a prototype system (YAGA) which
readily transforms a probabilistic model specified in a proof-based environment
to its equivalent verifiable PRISM model equipped with reward structures. The
reward structures capture the exact interpretation of the probabilistic
invariants and can reveal succinct information about the model during
experimental investigations. Finally, we demonstrate the novelty of the
technique on a probabilistic library case study
Effective Theories for Circuits and Automata
Abstracting an effective theory from a complicated process is central to the
study of complexity. Even when the underlying mechanisms are understood, or at
least measurable, the presence of dissipation and irreversibility in
biological, computational and social systems makes the problem harder. Here we
demonstrate the construction of effective theories in the presence of both
irreversibility and noise, in a dynamical model with underlying feedback. We
use the Krohn-Rhodes theorem to show how the composition of underlying
mechanisms can lead to innovations in the emergent effective theory. We show
how dissipation and irreversibility fundamentally limit the lifetimes of these
emergent structures, even though, on short timescales, the group properties may
be enriched compared to their noiseless counterparts.Comment: 11 pages, 9 figure
Behavioral Learning of Aircraft Landing Sequencing Using a Society of Probabilistic Finite State Machines
Air Traffic Control (ATC) is a complex safety critical environment. A tower
controller would be making many decisions in real-time to sequence aircraft.
While some optimization tools exist to help the controller in some airports,
even in these situations, the real sequence of the aircraft adopted by the
controller is significantly different from the one proposed by the optimization
algorithm. This is due to the very dynamic nature of the environment. The
objective of this paper is to test the hypothesis that one can learn from the
sequence adopted by the controller some strategies that can act as heuristics
in decision support tools for aircraft sequencing. This aim is tested in this
paper by attempting to learn sequences generated from a well-known sequencing
method that is being used in the real world. The approach relies on a genetic
algorithm (GA) to learn these sequences using a society Probabilistic
Finite-state Machines (PFSMs). Each PFSM learns a different sub-space; thus,
decomposing the learning problem into a group of agents that need to work
together to learn the overall problem. Three sequence metrics (Levenshtein,
Hamming and Position distances) are compared as the fitness functions in GA. As
the results suggest, it is possible to learn the behavior of the
algorithm/heuristic that generated the original sequence from very limited
information
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