1,557 research outputs found
Data optimizations for constraint automata
Constraint automata (CA) constitute a coordination model based on finite
automata on infinite words. Originally introduced for modeling of coordinators,
an interesting new application of CAs is implementing coordinators (i.e.,
compiling CAs into executable code). Such an approach guarantees
correctness-by-construction and can even yield code that outperforms
hand-crafted code. The extent to which these two potential advantages
materialize depends on the smartness of CA-compilers and the existence of
proofs of their correctness.
Every transition in a CA is labeled by a "data constraint" that specifies an
atomic data-flow between coordinated processes as a first-order formula. At
run-time, compiler-generated code must handle data constraints as efficiently
as possible. In this paper, we present, and prove the correctness of two
optimization techniques for CA-compilers related to handling of data
constraints: a reduction to eliminate redundant variables and a translation
from (declarative) data constraints to (imperative) data commands expressed in
a small sequential language. Through experiments, we show that these
optimization techniques can have a positive impact on performance of generated
executable code
Ants: Mobile Finite State Machines
Consider the Ants Nearby Treasure Search (ANTS) problem introduced by
Feinerman, Korman, Lotker, and Sereni (PODC 2012), where mobile agents,
initially placed at the origin of an infinite grid, collaboratively search for
an adversarially hidden treasure. In this paper, the model of Feinerman et al.
is adapted such that the agents are controlled by a (randomized) finite state
machine: they possess a constant-size memory and are able to communicate with
each other through constant-size messages. Despite the restriction to
constant-size memory, we show that their collaborative performance remains the
same by presenting a distributed algorithm that matches a lower bound
established by Feinerman et al. on the run-time of any ANTS algorithm
Building a Nest by an Automaton
A robot modeled as a deterministic finite automaton has to build a structure from material available to it. The robot navigates in the infinite oriented grid Z x Z. Some cells of the grid are full (contain a brick) and others are empty. The subgraph of the grid induced by full cells, called the field, is initially connected. The (Manhattan) distance between the farthest cells of the field is called its span. The robot starts at a full cell. It can carry at most one brick at a time. At each step it can pick a brick from a full cell, move to an adjacent cell and drop a brick at an empty cell. The aim of the robot is to construct the most compact possible structure composed of all bricks, i.e., a nest. That is, the robot has to move all bricks in such a way that the span of the resulting field be the smallest.
Our main result is the design of a deterministic finite automaton that accomplishes this task and subsequently stops, for every initially connected field, in time O(sz), where s is the span of the initial field and z is the number of bricks. We show that this complexity is optimal
Analyzing Social Network Structures in the Iterated Prisoner's Dilemma with Choice and Refusal
The Iterated Prisoner's Dilemma with Choice and Refusal (IPD/CR) is an
extension of the Iterated Prisoner's Dilemma with evolution that allows players
to choose and to refuse their game partners. From individual behaviors,
behavioral population structures emerge. In this report, we examine one
particular IPD/CR environment and document the social network methods used to
identify population behaviors found within this complex adaptive system. In
contrast to the standard homogeneous population of nice cooperators, we have
also found metastable populations of mixed strategies within this environment.
In particular, the social networks of interesting populations and their
evolution are examined.Comment: 37 pages, uuencoded gzip'd Postscript (1.1Mb when gunzip'd) also
available via WWW at http://www.cs.wisc.edu/~smucker/ipd-cr/ipd-cr.htm
The development of children\u27s orthographic knowledge: A microgenetic perspective
Literacy scholars traditionally described spelling development as a stage-like progression of increasing orthographic understanding measured by orthographic feature errors children used but confused (Henderson, 1980; Ehri, 1992). Overlapping Wave theorists defined spelling development as a series of adaptive choices between sophisticated and unsophisticated spelling strategies measured by the type and amount of strategies children used (Siegler, 1996). To disentangle discrepancies found between the alternative viewpoints, the current study: replicated and extended a previous investigation that described spelling development as overlapping waves (Riffle-Johnson & Sieger, 1999); investigated differential feedback conditions as a source of spelling growth; and examined correlates between orthographic features and spelling strategies used by low-ability first-grade students. The study used a trial-by-trial microgenetic approach combining statistical and observational methods. The study found evidence of spelling development proceeding in accumulative phases, continuously, and in overlapping waves concomitantly. Results defined three feature-strategy relationships: (1) direct relationships between orthographic feature knowledge and spelling strategy use, (2) time-sensitive relationships dependent on the depth of orthographic understanding, and (3) stable relationships not affecting strategy use. Individual differences in children\u27s growth rate uncovered the Matthew effect (Stanovich, 1986). Additionally, the study illustrated the advantages of a microgenetic mixed design
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